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et al., 2019",2019,"Table 3","Photo-realistic synthetic sequences rendered in Blender along an approximately 30 m corridor trajectory at 1.6 m\u002Fs and 30 FPS; errors against exact sy…",[20],"photo-realistic synthetic corridor dataset",[22,23],"ATE_mean","other",[25,23],"ate",[27],"BIM-Tracker",[14],16,0,{"slug":32,"sourceId":14,"sourceLabel":15,"sourceYear":16,"table":33,"note":34,"datasets":35,"metrics":37,"families":39,"methods":41,"methodIds":51,"rows":52,"failures":30},"acharya2019bimtracker-table-4","Table 4","Average time per localisation step for one frame; MATLAB implementation without hardware optimisation",[36],"BIM-Tracker experiments",[38],"runtime_per_frame",[40],"compute",[42,43,44,45,46,47,48,49,50],"BIM-Tracker (Image edge detection)","BIM-Tracker (Kalman filtering)","BIM-Tracker (MSAC (pose estimation by least-squares))","BIM-Tracker (MSAC (selection of best set))","BIM-Tracker (MSAC (updating correspondences))","BIM-Tracker (Point sampling)","BIM-Tracker (Search for correspondences)","BIM-Tracker (Total average time)","BIM-Tracker (Visible edge detection)",[14],9,{"slug":54,"sourceId":14,"sourceLabel":15,"sourceYear":16,"table":55,"note":56,"datasets":57,"metrics":59,"families":60,"methods":61,"methodIds":62,"rows":63,"failures":63},"acharya2019bimtracker-text-sec-4-5","Text Sec.4.5","Region of convergence on real smartphone data: random initial poses within +-1.5 m and +-20 deg of manually estimated true poses for a few frames",[58],"real smartphone corridor dataset",[23],[23],[27],[14],2,{"slug":65,"sourceId":66,"sourceLabel":67,"sourceYear":68,"table":69,"note":70,"datasets":71,"metrics":73,"families":77,"methods":79,"methodIds":83,"rows":52,"failures":30},"affan2026semanticmeshing-table-1","affan2026semanticmeshing","Affan et al., 2026",2026,"Table 1","Meshes sampled to 500,000 points, manually coarse-registered then ICP-aligned to TLS ground truth; outlier radius filter; inlier threshold tau = 0.30…",[72],"Oxford Spires",[74,75,76],"fscore","map_accuracy","map_completeness",[78],"map",[80,81,82],"ImMesh","Ours","Voxblox",[66,84,85],"lin2023immesh","oleynikova2017voxblox",{"slug":87,"sourceId":88,"sourceLabel":89,"sourceYear":90,"table":91,"note":92,"datasets":93,"metrics":95,"families":96,"methods":97,"methodIds":101,"rows":102,"failures":30},"arun1987svd-table-i","arun1987svd","Arun et al., 1987",1987,"Table I","Simulation: N random 3-D points in a 6x6x6 cube, rotated 75 deg about axis (0.6, 0.7, 0.39), translated by (80, 60, 70), Gaussian noise std 0.5 per co…",[94],"synthetic point sets (Sec. VII)",[23],[23],[98,99,100],"SVD algorithm (proposed)","iterative algorithm (Huang, Blostein and Margerum [3])","quaternion algorithm (Faugeras and Hebert [4])",[88],18,{"slug":104,"sourceId":105,"sourceLabel":106,"sourceYear":107,"table":108,"note":109,"datasets":110,"metrics":112,"families":114,"methods":115,"methodIds":118,"rows":120,"failures":30},"asadi2018visionrobot-table-2","asadi2018visionrobot","Asadi et al., 2018",2018,"Table 2","SLAM Module CPU usage on one NVIDIA Jetson TX1 (quad core, 100 = one full core, range 0 to 400) logged remotely on a separate five-minute video",[111],"authors' outdoor videos",[113],"cpu_usage",[40],[116,117],"ORB SLAM","Odometry scaler",[105,119],"orbslam2015",6,{"slug":122,"sourceId":105,"sourceLabel":106,"sourceYear":107,"table":17,"note":123,"datasets":124,"metrics":125,"families":126,"methods":127,"methodIds":130,"rows":120,"failures":30},"asadi2018visionrobot-table-3","Context-Awareness Module CPU usage on one NVIDIA Jetson TX1 (quad core, 100 = one full core, range 0 to 400) logged remotely on a separate five-minute…",[111],[113],[40],[128,129],"qlua (ENet segmentation)","store_images",[105],{"slug":132,"sourceId":105,"sourceLabel":106,"sourceYear":107,"table":33,"note":133,"datasets":134,"metrics":135,"families":136,"methods":137,"methodIds":140,"rows":120,"failures":30},"asadi2018visionrobot-table-4","Mapping Module CPU usage on one NVIDIA Jetson TX1 (quad core, 100 = one full core, range 0 to 400) logged remotely on a separate five-minute video",[111],[113],[40],[138,139],"Global map","Local map",[105],{"slug":142,"sourceId":105,"sourceLabel":106,"sourceYear":107,"table":143,"note":144,"datasets":145,"metrics":147,"families":149,"methods":150,"methodIds":153,"rows":63,"failures":154},"asadi2018visionrobot-text-sec-6-2-1","Text Sec.6.2.1","ENet inference rate for 512 x 256 images on a Jetson TX1 before integration of modules",[146],"authors' images",[148],"frequency",[40],[151,152],"Context-Awareness Module in the integrated system","ENet on Jetson TX1 (stand-alone)",[105],1,{"slug":156,"sourceId":105,"sourceLabel":106,"sourceYear":107,"table":157,"note":158,"datasets":159,"metrics":160,"families":161,"methods":162,"methodIds":164,"rows":154,"failures":30},"asadi2018visionrobot-text-sec-6-3-1","Text Sec.6.3.1","Average time to update the local occupancy map (2.5 m x 1.5 m)",[111],[38],[40],[163],"Mapping Module (local map)",[105],{"slug":166,"sourceId":167,"sourceLabel":168,"sourceYear":16,"table":169,"note":170,"datasets":171,"metrics":174,"families":175,"methods":176,"methodIds":178,"rows":63,"failures":30},"asadi2019imagebimslam-text-computation-time","asadi2019imagebimslam","Asadi et al., 2019","Text Computation Time","Average computation per keyframe at 640 x 360 (rough pose from the augmented SLAM, VP\u002FVL estimation, iterative fine-pose alignment) on the UGV's Jetso…",[172,173],"authors' construction-site video","authors' hallway video",[38],[40],[177],"proposed augmented SLAM + perspective alignment",[167],{"slug":180,"sourceId":167,"sourceLabel":168,"sourceYear":16,"table":181,"note":182,"datasets":183,"metrics":184,"families":185,"methods":186,"methodIds":188,"rows":154,"failures":154},"asadi2019imagebimslam-text-discussion","Text Discussion","Same construction-site keyframes processed on a desktop computer",[172],[38],[40],[187],"proposed method on desktop",[167],{"slug":190,"sourceId":167,"sourceLabel":168,"sourceYear":16,"table":191,"note":192,"datasets":193,"metrics":194,"families":195,"methods":196,"methodIds":198,"rows":154,"failures":30},"asadi2019imagebimslam-text-practical-implications","Text Practical Implications","Distance error of 18 pixels for keyframe 17 of the construction-site video, cited in the text with reference to Fig. 13 (per-keyframe mean square erro…",[172],[23],[23],[197],"proposed method (VP estimation at 640 x 360; keyframe 17 of the construction-site video)",[167],{"slug":200,"sourceId":167,"sourceLabel":168,"sourceYear":16,"table":201,"note":202,"datasets":203,"metrics":204,"families":205,"methods":206,"methodIds":208,"rows":63,"failures":63},"asadi2019imagebimslam-text-vp-estimation","Text VP estimation","Average vanishing point and line estimation time per keyframe at 640 x 360 on the Jetson TX1",[172,173],[38],[40],[207],"VP\u002FVL estimation (Hedau et al. 2009 method)",[],{"slug":210,"sourceId":211,"sourceLabel":212,"sourceYear":213,"table":108,"note":214,"datasets":215,"metrics":217,"families":220,"methods":221,"methodIds":223,"rows":224,"failures":30},"asadi2020ugvuav-table-2","asadi2020ugvuav","Asadi et al., 2020",2020,"Hardware utilization of one module on the UGV laptop during a 3 min run with all modules active; CPU in percent of 400 (quad core), RAM in GB of 16, G…",[216],"own 3 min test run",[113,218,219],"gpu_usage","memory",[40],[222],"LNSNet semantic segmentation (Context-Awareness Module)",[],12,{"slug":226,"sourceId":211,"sourceLabel":212,"sourceYear":213,"table":17,"note":214,"datasets":227,"metrics":228,"families":229,"methods":230,"methodIds":232,"rows":224,"failures":30},"asadi2020ugvuav-table-3",[216],[113,218,219],[40],[231],"ZED Stereo ROS Wrapper",[],{"slug":234,"sourceId":211,"sourceLabel":212,"sourceYear":213,"table":33,"note":214,"datasets":235,"metrics":236,"families":237,"methods":238,"methodIds":240,"rows":120,"failures":30},"asadi2020ugvuav-table-4",[216],[113,219],[40],[239],"RTAB-MAP (UGV SLAM Module)",[241],"rtabmap2019",{"slug":243,"sourceId":211,"sourceLabel":212,"sourceYear":213,"table":244,"note":214,"datasets":245,"metrics":246,"families":247,"methods":248,"methodIds":250,"rows":120,"failures":30},"asadi2020ugvuav-table-5","Table 5",[216],[113,219],[40],[249],"Vins-Mono (blimp localization, run on the UGV laptop)",[251],"vinsmono2018",{"slug":253,"sourceId":211,"sourceLabel":212,"sourceYear":213,"table":254,"note":255,"datasets":256,"metrics":258,"families":259,"methods":260,"methodIds":262,"rows":154,"failures":30},"asadi2020ugvuav-text-sec-4-1","Text Sec. 4.1","Inference speed of the segmentation model on the UGV laptop for 640 x 360 input",[257],"not_reported (inference speed of the trained model on the UGV laptop for 640 x 360 input; the 3000-image set is the training dataset)",[148],[40],[261],"LNSNet semantic segmentation",[],{"slug":264,"sourceId":211,"sourceLabel":212,"sourceYear":213,"table":265,"note":266,"datasets":267,"metrics":269,"families":270,"methods":271,"methodIds":273,"rows":274,"failures":30},"asadi2020ugvuav-text-sec-5-2","Text Sec. 5.2","Almost constant difference between the UAV-UGV relative position from marker detection and from SLAM comparison during a 115 s hallway trial (about 21…",[268],"own hallway trial",[23],[23],[272],"SLAM comparison (RTAB-Map on UGV vs Vins-Mono on blimp) vs Whycon marker detection",[211],3,{"slug":276,"sourceId":277,"sourceLabel":278,"sourceYear":16,"table":279,"note":280,"datasets":281,"metrics":283,"families":284,"methods":285,"methodIds":300,"rows":301,"failures":30},"babin2019robust-table-iii","babin2019robust","Babin et al., 2019","Table III","libpointmatcher point-to-plane ICP (Table II) with only the outlier filter changed; 12 scan pairs per environment spanning 40 to 100% overlap; 128 ran…",[282],"Challenging Datasets for Point Cloud Registration (Pomerleau et al. 2012)",[23],[23],[286,287,288,289,290,291,292,293,294,295,296,297,298,299],"Cauchy","Cauchy Berg","Cauchy MAD","GM MAD","Huber MAD","L1","L2","Max. Dist.","SC MAD","Student","Trim","Tukey MAD","Var. Trim.","Welsch MAD",[],56,{"slug":303,"sourceId":304,"sourceLabel":305,"sourceYear":306,"table":91,"note":307,"datasets":308,"metrics":313,"families":314,"methods":315,"methodIds":320,"rows":322,"failures":30},"fasterlio2022-table-i","fasterlio2022","Bai et al., 2022",2022,"Time evaluation: 'pre' = preprocessing + undistortion + downsampling per scan, 'opt' = pose computation; Spd inc = speed increase against FastLIO2; LI…",[309,310,311,312],"LIO-SAM dataset","NCLT","ULHK (UrbanLoco)","UTBM robocar dataset",[23],[23],[316,317,318,319],"FastLIO2","Faster-LIO","Faster-LIO (versus FastLIO2)","Faster-LIO PHC",[304,321],"fastlio2_2022",63,{"slug":324,"sourceId":304,"sourceLabel":305,"sourceYear":306,"table":325,"note":326,"datasets":327,"metrics":328,"families":331,"methods":333,"methodIds":336,"rows":339,"failures":340},"fasterlio2022-table-ii","Table II","Accuracy in APE (m) over whole trajectories and translational RPE (%) per 100 m; loop closure of LIO-SAM and LiLi-OM disabled; parameters of LIO-SAM a…",[309,310,311,312],[329,330],"APE","RPE_trans",[25,332],"rpe",[316,317,319,334,335],"LIO-SAM","LiLi-OM",[304,321,337,338],"liliom2021","liosam2020",90,14,{"slug":342,"sourceId":343,"sourceLabel":344,"sourceYear":345,"table":346,"note":347,"datasets":348,"metrics":350,"families":351,"methods":352,"methodIds":355,"rows":356,"failures":30},"barfoot2017ser-text-sec-4-1-2-and-4-2-11","barfoot2017ser","Barfoot, 2017",2017,"Text Sec.4.1.2 and 4.2.11","One-dimensional stereo-camera landmark-depth example (f = 400 px, b = 0.1 m, prior 20 m with variance 9 m^2, R = 0.09 px^2); 1,000,000 Monte Carlo tri…",[349],"synthetic",[23],[23],[353,354],"MAP estimator","iterated sigmapoint Kalman filter (ISPKF)",[],4,{"slug":358,"sourceId":343,"sourceLabel":344,"sourceYear":345,"table":359,"note":360,"datasets":361,"metrics":362,"families":363,"methods":364,"methodIds":369,"rows":356,"failures":30},"barfoot2017ser-text-sec-4-2-11","Text Sec.4.2.11","Single trial of the same stereo-camera example with x_true = 26 m and y_meas = f b \u002F x_true - 0.6 px; landmark depth estimates compared with the poste…",[349],[23],[23],[365,366,367,368],"IEKF","ISPKF","MAP solution (posterior mode)","mean of full posterior",[],{"slug":371,"sourceId":372,"sourceLabel":373,"sourceYear":374,"table":91,"note":375,"datasets":376,"metrics":378,"families":379,"methods":380,"methodIds":391,"rows":396,"failures":120},"sgraphsplus2023-table-i","sgraphsplus2023","Bavle et al., 2023",2023,"Simulated experiments: C1F0 and C1F2 from 3D meshes of two floors of real architectural plans, SE1 to SE3 generic simulated indoor layouts; odometry f…",[377],"in-house simulated data (VLP-16 simulated)",[329],[25],[381,382,383,384,385,386,387,388,389,390],"ALOAM [6] (ALOAM odometry)","FLOAM [7] (FLOAM odometry)","HDL-SLAM [11] (VGICP [26] odometry)","LeGO-LOAM [8] (LeGO-LOAM odometry)","MLOAM [10] (MLOAM odometry)","S-Graphs [9] (VGICP odometry)","S-Graphs+ (ours) (FLOAM odometry)","S-Graphs+ (ours) (VGICP odometry)","S-Graphs+ w. OF (VGICP odometry; new room detection, old factors)","S-Graphs+ w. OR (VGICP odometry; old room detection, new room-to-wall factors)",[392,393,394,395,372],"aloam_software","floam2021","koide2019_hdlgraphslam","legoloam2018",50,{"slug":398,"sourceId":372,"sourceLabel":373,"sourceYear":374,"table":325,"note":399,"datasets":400,"metrics":402,"families":403,"methods":404,"methodIds":414,"rows":415,"failures":416},"sgraphsplus2023-table-ii","In-house real sequences on ongoing construction sites (C1: single house, C2: four combined houses, C3: two combined houses); all methods use odometry…",[401],"in-house construction-site dataset (VLP-16)",[75],[78],[405,406,407,408,409,410,411,412,413],"ALOAM [6]","FLOAM [7]","HDL-SLAM [11]","LeGO-LOAM [8]","MLOAM [10]","S-Graphs [9]","S-Graphs+ (ours)","S-Graphs+ w. OF","S-Graphs+ w. OR",[392,393,394,395,372],72,5,{"slug":418,"sourceId":372,"sourceLabel":373,"sourceYear":374,"table":279,"note":419,"datasets":420,"metrics":421,"families":422,"methods":423,"methodIds":428,"rows":429,"failures":30},"sgraphsplus2023-table-iii","Mean computation time per S-Graphs+ module on the in-house construction-site sequences; sequence length 487 s for C1F1",[401],[38],[40],[424,425,426,427],"S-Graphs+ (Back-End)","S-Graphs+ (Floor Segmentation)","S-Graphs+ (Plane Segmentation)","S-Graphs+ (Room Segmentation)",[372],28,{"slug":431,"sourceId":432,"sourceLabel":433,"sourceYear":107,"table":325,"note":434,"datasets":435,"metrics":437,"families":440,"methods":442,"methodIds":449,"rows":451,"failures":30},"suma2018-table-ii","suma2018","Behley & Stachniss, 2018","KITTI odometry training set; relative errors averaged over 100-800 m trajectories; values written rot [deg\u002F100m] \u002F trans [%]; * = sequence contains lo…",[436],"KITTI odometry (training)",[438,439],"KITTI_drift_pct","KITTI_rot_deg_per_100m",[441],"kitti",[443,444,445,446,447,448],"Frame-to-Frame","Frame-to-Model","Frame-to-Model with loop closure","LOAM [35]","S-LSD [6] (Stereo LSD-SLAM)","SOFT-SLAM [2]",[450,432],"loam2017_auro",132,{"slug":453,"sourceId":432,"sourceLabel":433,"sourceYear":107,"table":454,"note":455,"datasets":456,"metrics":458,"families":459,"methods":460,"methodIds":463,"rows":356,"failures":30},"suma2018-text-sec-iv","Text Sec. IV","KITTI odometry test set, compared with the value the authors quote for LOAM",[457],"KITTI odometry (test)",[438,23],[441,23],[461,462],"LOAM","SuMa (test-set submission; configuration not stated)",[450,432],{"slug":465,"sourceId":432,"sourceLabel":433,"sourceYear":107,"table":466,"note":467,"datasets":468,"metrics":470,"families":471,"methods":472,"methodIds":475,"rows":356,"failures":30},"suma2018-text-sec-iv-runtime","Text Sec. IV Runtime","Processing time on KITTI sequence 00 (revisits require GPU up and download and loop verification)",[469],"KITTI odometry",[38],[40],[473,474],"SuMa full pipeline","SuMa odometry and map update",[432],{"slug":477,"sourceId":478,"sourceLabel":479,"sourceYear":480,"table":481,"note":482,"datasets":483,"metrics":485,"families":486,"methods":487,"methodIds":489,"rows":63,"failures":154},"besl1992icp-text-sec-vi-a","besl1992icp","Besl & McKay, 1992",1992,"Text Sec.VI-A","Local point set matching without correspondence: 8 data points against 11 model points (Table I), one initial rotation and one initial translation, si…",[484],"Table I point sets (synthetic)",[23],[23],[488],"ICP algorithm (six iterations; basic or accelerated variant not stated)",[478],{"slug":491,"sourceId":478,"sourceLabel":479,"sourceYear":480,"table":492,"note":493,"datasets":494,"metrics":496,"families":497,"methods":498,"methodIds":500,"rows":154,"failures":154},"besl1992icp-text-sec-vi-a1","Text Sec.VI-A1","Brute-force comparison paragraph: point set of 2500 points registered to 4200 points (African mask example) using 60 initial rotation states",[495],"NRCC African mask range data",[23],[23],[499],"ICP with 60 initial rotation states",[478],{"slug":502,"sourceId":478,"sourceLabel":479,"sourceYear":480,"table":503,"note":504,"datasets":505,"metrics":507,"families":508,"methods":509,"methodIds":512,"rows":63,"failures":63},"besl1992icp-text-sec-vi-c1","Text Sec.VI-C1","Global matching of 250 noisy points (vector noise std 0.1 units) to a Bezier surface patch drawn with 450 triangles in a 3 x 3 x 1 unit box, 24 initia…",[506],"synthetic Bezier surface patch",[23],[23],[510,511],"ICP with 24 initial rotation states","ICP with 24 rotation and 6 translation states",[478],{"slug":514,"sourceId":478,"sourceLabel":479,"sourceYear":480,"table":515,"note":516,"datasets":517,"metrics":519,"families":520,"methods":521,"methodIds":523,"rows":274,"failures":154},"besl1992icp-text-sec-vi-c2","Text Sec.VI-C2","Thinned 2546-point data set of the 90 mm African mask registered to a 64 x 68 range-image model (8442 triangles); all trial positionings; 24 initial s…",[518],"NRCC African mask range data (Hyscan sensor)",[23],[23],[522],"ICP",[478],{"slug":525,"sourceId":478,"sourceLabel":479,"sourceYear":480,"table":526,"note":527,"datasets":528,"metrics":530,"families":531,"methods":532,"methodIds":533,"rows":154,"failures":154},"besl1992icp-text-sec-vi-c3","Text Sec.VI-C3","Local matching of 13 655 terrain points (interior section covering about 60% of the surface, lifted and rotated) to a 45 900-triangle terrain model ne…",[529],"University of Arizona terrain data",[23],[23],[510],[478],{"slug":535,"sourceId":536,"sourceLabel":537,"sourceYear":538,"table":539,"note":540,"datasets":541,"metrics":543,"families":544,"methods":545,"methodIds":547,"rows":63,"failures":30},"biber2003ndt-text-sec-vi","biber2003ndt","Biber & Strasser, 2003",2003,"Text Sec.VI","Position tracking against a keyframe; per-scan cost of building the NDT",[542],"authors' indoor SICK scans",[23,38],[40,23],[546],"NDT (proposed)",[536],{"slug":549,"sourceId":536,"sourceLabel":537,"sourceYear":538,"table":550,"note":551,"datasets":552,"metrics":553,"families":554,"methods":555,"methodIds":557,"rows":63,"failures":30},"biber2003ndt-text-sec-viii","Text Sec.VIII","Offline processing of the lab-corridor run (every fifth of 28 430 scans used, about 23 scans\u002Fs at a simulated 35 cm\u002Fs; tracking every scan, SLAM step…",[542],[148,23],[40,23],[556],"NDT scan matcher with keyframe SLAM (proposed)",[536],{"slug":559,"sourceId":560,"sourceLabel":561,"sourceYear":562,"table":563,"note":564,"datasets":565,"metrics":567,"families":569,"methods":570,"methodIds":576,"rows":578,"failures":30},"molalo2025-table-10","molalo2025","Blanco-Claraco, 2025",2025,"Table 10","UAL campus, electric vehicle with VLP-16, RTK GNSS ground truth; with and without scan deskewing",[566],"UAL VLP-16 campus dataset",[568,38],"ATE_RMSE",[25,40],[571,572,573,574,575],"KISS-ICP","KISS-ICP (w\u002Fo deskew)","MOLA-LO (ours)","MOLA-LO (w\u002Fo deskew) (ours)","MOLA-LO + LC (ours)",[577,560],"kissicp2023",10,{"slug":580,"sourceId":560,"sourceLabel":561,"sourceYear":562,"table":17,"note":581,"datasets":582,"metrics":583,"families":584,"methods":585,"methodIds":595,"rows":598,"failures":274},"molalo2025-table-3","KITTI odometry average RTE over training sequences 00 to 10; IMLS-SLAM, MULLS and CT-ICP2 values from their publications; 0.205 deg vertical correctio…",[469],[438,38],[40,441],[586,587,571,588,589,590,591,592,593,594],"CT-ICP2 (LC)","IMLS-SLAM","MOLA-LO (3D-NDT) (ours)","MOLA-LO (Horn's) (ours)","MOLA-LO (default) (ours)","MOLA-LO (default) + LC (ours)","MULLS (LC)","SiMpLE (offline)","SiMpLE (online)",[596,577,560,597],"cticp2022","mulls2021",30,{"slug":600,"sourceId":560,"sourceLabel":561,"sourceYear":562,"table":33,"note":601,"datasets":602,"metrics":604,"families":605,"methods":606,"methodIds":607,"rows":608,"failures":30},"molalo2025-table-4","KITTI-360 ATE RMSE (evo_ape -a); sequences 03, 07 and 10 have no loop closures",[603],"KITTI-360",[568],[25],[571,573,575],[577,560],24,{"slug":610,"sourceId":560,"sourceLabel":561,"sourceYear":562,"table":244,"note":611,"datasets":612,"metrics":614,"families":615,"methods":616,"methodIds":617,"rows":618,"failures":30},"molalo2025-table-5","Paris LuCo single sequence (HDL-32, two loops around the Luxembourg Garden)",[613],"ParisLuco",[568,38],[25,40],[571,588,590,591],[577,560],8,{"slug":620,"sourceId":560,"sourceLabel":561,"sourceYear":562,"table":621,"note":622,"datasets":623,"metrics":625,"families":626,"methods":627,"methodIds":630,"rows":416,"failures":30},"molalo2025-table-7","Table 7","Voxgraph aerial sequence t0 (OS1-64 drone, RTK ground truth, evaluated only where ground truth exists); LOAM and Voxgraph values from literature",[624],"Voxgraph dataset",[568],[25],[571,628,588,590,629],"LOAM (Zhang and Singh 2017)","Voxgraph",[577,450,560],{"slug":632,"sourceId":560,"sourceLabel":561,"sourceYear":562,"table":633,"note":634,"datasets":635,"metrics":637,"families":638,"methods":639,"methodIds":640,"rows":641,"failures":416},"molalo2025-table-8","Table 8","DARPA SubT final event, four ANYmal C legged robots with VLP-16; ground truth by scan matching against survey-grade scanner point cloud; KISS-ICP defa…",[636],"DARPA Subterranean final event (Team CERBERUS)",[568,38],[25,40],[571,573,593],[577,560],15,{"slug":643,"sourceId":560,"sourceLabel":561,"sourceYear":562,"table":644,"note":645,"datasets":646,"metrics":649,"families":650,"methods":651,"methodIds":653,"rows":301,"failures":654},"molalo2025-table-9","Table 9","Handheld Newer College sequences; ATE RMSE from evo_ape -a (Umeyama alignment); x(value) marks divergence; no method uses the IMU; same default config…",[647,648],"Newer College (2020, sequences 01 and 02)","Newer College extension (2021)",[568],[25],[571,652,573,575],"MOLA-LO (always updates local map)",[577,560],7,{"slug":656,"sourceId":560,"sourceLabel":561,"sourceYear":562,"table":657,"note":658,"datasets":659,"metrics":661,"families":662,"methods":663,"methodIds":665,"rows":63,"failures":30},"molalo2025-text-sec-4-6","Text Sec. 4.6","HILTI 2021 RPG drone testing arena sequence (OS0-64), the larger of two sequences with full SE(3) ground truth; values stated in text",[660],"HILTI 2021 SLAM challenge",[568],[25],[571,664],"MOLA-LO",[577,560],{"slug":667,"sourceId":560,"sourceLabel":561,"sourceYear":562,"table":668,"note":669,"datasets":670,"metrics":672,"families":673,"methods":674,"methodIds":676,"rows":63,"failures":30},"molalo2025-text-sec-6","Text Sec. 6","Localization-only mode on a reference map built from the SLAM output of KAIST02 (hashed voxel map, 2 m, 20 points per voxel); initial pose given manua…",[671],"MulRan",[568],[25],[675],"MOLA-LO localization (map from KAIST02)",[560],{"slug":678,"sourceId":679,"sourceLabel":680,"sourceYear":681,"table":91,"note":682,"datasets":683,"metrics":685,"families":686,"methods":687,"methodIds":693,"rows":416,"failures":30},"rovio2015-table-i","rovio2015","Bloesch et al., 2015",2015,"Processing time per image versus total number of features in the state; hand-held slow-motion experiment; single core of an Intel i7-2760QM",[684],"own VI-Sensor hand-held dataset (motion capture)",[38],[40],[688,689,690,691,692],"ROVIO (10 features)","ROVIO (20 features)","ROVIO (30 features)","ROVIO (40 features)","ROVIO (50 features)",[],{"slug":695,"sourceId":696,"sourceLabel":697,"sourceYear":698,"table":91,"note":699,"datasets":700,"metrics":702,"families":703,"methods":704,"methodIds":706,"rows":356,"failures":30},"blum2021precisebim-table-i","blum2021precisebim","Blum et al., 2021",2021,"Full LiDAR scan localized in the full building model (plan-derived mesh without the artificial 0.3 m deviation); position RMSE of the tracked prism ve…",[701],"own construction-site recordings",[329],[25],[705],"full ICP, full scan (traditional ICP baseline)",[],{"slug":708,"sourceId":696,"sourceLabel":697,"sourceYear":698,"table":325,"note":709,"datasets":710,"metrics":711,"families":712,"methods":713,"methodIds":720,"rows":721,"failures":30},"blum2021precisebim-table-ii","Stationary localization study: ICP against the full model or selectively against reference surfaces, using the full, semantically filtered (Eq. 1) or…",[701],[329,23],[25,23],[714,715,716,717,718,719],"full ICP, filtered scan","full ICP, full scan","full ICP, weighted scan","selective ICP, filtered scan","selective ICP, full scan","selective ICP, weighted scan",[696],36,{"slug":723,"sourceId":696,"sourceLabel":697,"sourceYear":698,"table":279,"note":709,"datasets":724,"metrics":725,"families":726,"methods":727,"methodIds":728,"rows":721,"failures":30},"blum2021precisebim-table-iii",[701],[329,23],[25,23],[714,715,716,717,718,719],[696],{"slug":730,"sourceId":696,"sourceLabel":697,"sourceYear":698,"table":731,"note":709,"datasets":732,"metrics":733,"families":734,"methods":735,"methodIds":736,"rows":721,"failures":30},"blum2021precisebim-table-iv","Table IV",[701],[329,23],[25,23],[714,715,716,717,718,719],[696],{"slug":738,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":325,"note":741,"datasets":742,"metrics":744,"families":745,"methods":746,"methodIds":762,"rows":641,"failures":30},"okvis2x2025-table-ii","okvis2x2025","Boche et al., 2025","EuRoC, RMS ATE (m) after SE(3) alignment, average column over 11 sequences; V-SLAM rows exclude V2_03 as failed; ours = median of 10 runs; competitors…",[743],"EuRoC MAV",[568],[25],[747,748,749,750,751,752,753,754,755,756,757,758,759,760,761],"Kimera2 (VIO, causal)","MAVIS-SLAM (VI-SLAM, NC)","ORB-SLAM3 (V-SLAM, NC)","ORB-SLAM3 (VI-SLAM, NC)","OpenVINS (VIO, causal)","Ours-v-ba (V-SLAM)","Ours-v-c (V-SLAM, causal)","Ours-v-nc (V-SLAM)","Ours-vi-ba","Ours-vi-c (VI-SLAM, causal)","Ours-vi-c (VIO, no loop closure)","Ours-vi-nc","Ours-vid-ba","Ours-vid-c (VI-SLAM, causal)","Ours-vid-nc",[763],"orbslam3_2021",{"slug":765,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":279,"note":766,"datasets":767,"metrics":769,"families":770,"methods":771,"methodIds":776,"rows":618,"failures":30},"okvis2x2025-table-iii","EuRoC Vicon-room sequences V1_01 to V2_03; mesh accuracy (m) and completeness (%) with 0.2 m threshold, average row; SimpleMapping run by the authors…",[768],"EuRoC MAV (Vicon room point clouds)",[75,76],[78],[772,773,774,775],"Ours-vid","Ours-vid (Mono)","Ours-vid (Stereo)","SimpleMapping",[],{"slug":778,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":731,"note":779,"datasets":780,"metrics":782,"families":783,"methods":784,"methodIds":796,"rows":800,"failures":30},"okvis2x2025-table-iv","Hilti-Oxford (Hilti 2022 challenge) localisation score, total over exp01, 02, 03, 07, 09, 11, 15, 21; ours = best of 3 runs (median in brackets in the…",[781],"Hilti-Oxford (Hilti 2022 challenge)",[23],[23],[785,786,787,788,789,755,790,759,791,792,793,794,795],"BAMF-SLAM (VI, non-causal)","FAST-LIVO2 (VIL, causal)","MAVIS SLAM (VI, non-causal)","ORB-SLAM3 (VI, non-causal)","OpenVINS (VI, causal)","Ours-vi-c","Ours-vid-c","Ours-vil-ba","Ours-vil-c","VILENS (VIL, non-causal)","Wildcat (LiDAR-inertial)",[797,763,798,799],"fastlivo2_2025","vilens2023","wildcat2022",13,{"slug":802,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":803,"note":804,"datasets":805,"metrics":808,"families":809,"methods":810,"methodIds":814,"rows":618,"failures":30},"okvis2x2025-table-ix","Table IX","Maximum memory (GB) by configuration and dataset",[806,807],"Hilti-Oxford","VBR",[219],[40],[811,812,772,813],"FAST-LIVO","Ours-vi","Ours-vil",[815],"fastlivo2022",{"slug":817,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":818,"note":819,"datasets":820,"metrics":821,"families":822,"methods":823,"methodIds":824,"rows":29,"failures":30},"okvis2x2025-table-v","Table V","Hilti-Oxford sequences with dense ground truth; mesh accuracy (m) and completeness (%) with 0.2 m threshold; far plane 3 m (depth) and 30 m (LiDAR)",[806],[75,76],[78],[772,813],[],{"slug":826,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":827,"note":828,"datasets":829,"metrics":830,"families":831,"methods":832,"methodIds":835,"rows":608,"failures":154},"okvis2x2025-table-vi","Table VI","VBR (Rome), RMS ATE (m) after SE(3) alignment, median of 3 runs; ORB-SLAM3 and FAST-LIVO run by the authors; ORB-SLAM3 failed on Ciampino0 and has few…",[807],[568],[25],[833,788,789,755,790,758,759,791,761,792,793,834],"FAST-LIVO (VIL, causal)","Ours-vil-nc",[815,763],{"slug":837,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":838,"note":839,"datasets":840,"metrics":842,"families":843,"methods":844,"methodIds":848,"rows":849,"failures":30},"okvis2x2025-table-vii","Table VII","VBR Campus1 (about 2.9 km) with simulated RTK-GNSS (1 cm horizontal, 2 cm vertical noise) and a simulated 75 s, 450 m GNSS dropout in narrow streets;…",[841],"VBR Campus1 with simulated RTK-GNSS",[568],[25],[812,772,845,846,813,847],"Ours-vidg","Ours-vig","Ours-vilg",[],27,{"slug":851,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":852,"note":853,"datasets":854,"metrics":855,"families":856,"methods":857,"methodIds":867,"rows":52,"failures":30},"okvis2x2025-table-viii","Table VIII","Hilti-Oxford exp04 to exp06, ATE RMSE (m) average column, median of 3 runs; online calibration versus fixed post-calibrated extrinsics versus neither",[806],[568],[25],[858,859,860,861,862,863,864,865,866],"Ours-vi-ba, no calibration","Ours-vi-ba, online calibration","Ours-vi-ba, post-calibration","Ours-vi-c, no calibration","Ours-vi-c, online calibration","Ours-vi-c, post-calibration","Ours-vi-nc, no calibration","Ours-vi-nc, online calibration","Ours-vi-nc, post-calibration",[],{"slug":869,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":870,"note":871,"datasets":872,"metrics":874,"families":875,"methods":876,"methodIds":883,"rows":120,"failures":30},"okvis2x2025-text-sec-vi-e","Text Sec.VI-E","GVINS dataset complex_environment (nearly 3 km, indoor-outdoor); RTKLIB SPP fused as global positions; RMSE ATE after 6-DoF alignment to RTK where RTK…",[873],"GVINS-Dataset",[568],[25],[877,878,879,880,881,882],"GVINS (as reported by its authors)","Ours-vi (visual-inertial baseline)","Ours-vig-ba","Ours-vig-c","Ours-vig-nc","RTKLIB SPP solution",[],{"slug":885,"sourceId":739,"sourceLabel":740,"sourceYear":562,"table":886,"note":887,"datasets":888,"metrics":889,"families":890,"methods":891,"methodIds":893,"rows":120,"failures":30},"okvis2x2025-text-sec-vi-h","Text Sec.VI-H","Wall time divided by the number of processed frames, Ours-vi versus ORB-SLAM3; desktop i7-13700 with RTX 3080",[743,806,807],[38],[40],[892,812],"ORB-SLAM3",[763],{"slug":895,"sourceId":896,"sourceLabel":897,"sourceYear":898,"table":108,"note":899,"datasets":900,"metrics":902,"families":903,"methods":904,"methodIds":906,"rows":907,"failures":356},"borrmann2008-6dlum-table-2","borrmann2008_6dlum","Borrmann et al., 2008",2008,"Horn TLS data set, 13 scans; poses perturbed with random initial errors (horizontal position, vertical-axis rotation), then ICP + 6D LUM; errors after…",[901],"Horn (Austria) main square, RIEGL scans",[329,23],[25,23],[905],"6D LUM (ICP + Lu-Milios style GraphSLAM)",[896],52,{"slug":909,"sourceId":896,"sourceLabel":897,"sourceYear":898,"table":910,"note":911,"datasets":912,"metrics":913,"families":914,"methods":915,"methodIds":916,"rows":274,"failures":30},"borrmann2008-6dlum-text-sec-7-1","Text Sec. 7.1","Horn data set, 13 scans of 240,000 to 300,000 points, matching with reduced points until no scan moved more than 0.5 cm per iteration",[901],[23],[23],[905],[896],{"slug":918,"sourceId":919,"sourceLabel":920,"sourceYear":921,"table":69,"note":922,"datasets":923,"metrics":925,"families":926,"methods":927,"methodIds":929,"rows":52,"failures":30},"borrmann2014thermalmapping-table-1","borrmann2014thermalmapping","Borrmann et al., 2014",2014,"Voxel counts of the 3D model of room 1 during the full exploration run (0.2 m voxels, field-of-view constraint of the thermal camera, stop threshold V…",[924],"authors' Irma3D exploration data",[76],[78],[928],"combined 2D and 3D NBV exploration",[919],{"slug":931,"sourceId":919,"sourceLabel":920,"sourceYear":921,"table":108,"note":932,"datasets":933,"metrics":934,"families":935,"methods":936,"methodIds":939,"rows":641,"failures":30},"borrmann2014thermalmapping-table-2","Two additional exploration experiments in room 1 from the same start position, one with 2D exploration only and one with 2D plus 3D NBV planning",[924],[76],[78],[937,938],"2D NBV only","3D NBV (combined 2D and 3D exploration)",[919],{"slug":941,"sourceId":919,"sourceLabel":920,"sourceYear":921,"table":17,"note":942,"datasets":943,"metrics":945,"families":946,"methods":947,"methodIds":949,"rows":120,"failures":30},"borrmann2014thermalmapping-table-3","Marching-cubes reconstruction of part of the dataset with different spatial subdivisions",[944],"authors' Irma3D data (part of the dataset)",[23],[23],[948],"probabilistic marching cubes reconstruction",[919],{"slug":951,"sourceId":919,"sourceLabel":920,"sourceYear":921,"table":952,"note":953,"datasets":954,"metrics":955,"families":956,"methods":957,"methodIds":959,"rows":154,"failures":30},"borrmann2014thermalmapping-text-sec-5","Text Sec.5","Duration of one 3D scan with thermal and colour image acquisition at a scanning position",[924],[23],[23],[958],"Irma3D stop-and-go scanning",[919],{"slug":961,"sourceId":962,"sourceLabel":963,"sourceYear":921,"table":964,"note":965,"datasets":966,"metrics":968,"families":969,"methods":970,"methodIds":972,"rows":578,"failures":30},"bosche2014flatness-fig-12-tables","bosche2014flatness","Bosché & Guenet, 2014","Fig. 12 tables","Grid-Square straightedge (2 m) deviations from the TLS-based system (10% of the initial scans) compared with manual chalk-grid straightedge and steel-…",[967],"own TLS scans (FARO Focus3D)",[23],[23],[971],"Straightedge Grid-Square (TLS-based, Scan-vs-BIM)",[962],{"slug":974,"sourceId":962,"sourceLabel":963,"sourceYear":921,"table":975,"note":976,"datasets":977,"metrics":978,"families":979,"methods":980,"methodIds":984,"rows":224,"failures":30},"bosche2014flatness-fig-13-tables","Fig. 13 tables","Maximum and mean 2 m straightedge deviations from the TLS-based system for three straightedge generation methods; Random uses as many straightedges as…",[967],[23],[23],[981,982,983],"Straightedge Grid-Square","Straightedge Grid-Star (proposed pattern)","Straightedge Random",[962],{"slug":986,"sourceId":962,"sourceLabel":963,"sourceYear":921,"table":987,"note":988,"datasets":989,"metrics":990,"families":991,"methods":992,"methodIds":995,"rows":356,"failures":30},"bosche2014flatness-fig-14-tables","Fig. 14 tables","F-Numbers from the TLS-based ASTM E1155 implementation (10% of the initial TLS data, 90% confidence interval in brackets) compared with the maximum de…",[967],[23],[23],[993,994],"F-Numbers (ASTM E1155, TLS-based)","Straightedge Grid-Star, 3 m",[962],{"slug":997,"sourceId":962,"sourceLabel":963,"sourceYear":921,"table":244,"note":998,"datasets":999,"metrics":1000,"families":1001,"methods":1002,"methodIds":1004,"rows":578,"failures":63},"bosche2014flatness-table-5","Approximate durations of the TLS-based control procedure; processing with 10% of the original scan data; hardware not reported",[967],[23],[23],[1003],"TLS-based system (Scan-vs-BIM + Straightedge Grid-Square)",[962],{"slug":1006,"sourceId":962,"sourceLabel":963,"sourceYear":921,"table":1007,"note":1008,"datasets":1009,"metrics":1011,"families":1012,"methods":1013,"methodIds":1015,"rows":274,"failures":30},"bosche2014flatness-text-sec-8-1-2","Text Sec. 8.1.2","Manual global flatness control duration (chalk 2 m grid, straightedge and steel rule)",[1010,967],"manual survey",[23],[23],[981,1014],"manual Straightedge method (reference)",[962],{"slug":1017,"sourceId":962,"sourceLabel":963,"sourceYear":921,"table":1018,"note":1019,"datasets":1020,"metrics":1022,"families":1023,"methods":1024,"methodIds":1027,"rows":356,"failures":63},"bosche2014flatness-text-sec-8-2","Text Sec. 8.2","Processing time of Random and Grid-Star flatness control",[1021,967],"estimate",[23],[23],[1025,1026],"Straightedge Random and Grid-Star","manual Straightedge method (estimate)",[962],{"slug":1029,"sourceId":1030,"sourceLabel":1031,"sourceYear":1032,"table":108,"note":1033,"datasets":1034,"metrics":1036,"families":1037,"methods":1038,"methodIds":1041,"rows":1042,"failures":30},"bosche2010asbuiltdims-table-2","bosche2010asbuiltdims","Bosché, 2010",2010,"Model registration of each site scan to the 3D CAD model (612 objects, 19,478 facets): New = coarse registration plus proposed ICP model fine registra…",[1035],"PEC steel structure scans",[23],[23],[1039,1040],"New (model fine registration)","Old (Bosche et al. [11])",[1030],20,{"slug":1044,"sourceId":1030,"sourceLabel":1031,"sourceYear":1032,"table":17,"note":1045,"datasets":1046,"metrics":1047,"families":1048,"methods":1049,"methodIds":1051,"rows":608,"failures":30},"bosche2010asbuiltdims-table-3","CAD object recognition (same recognition metric, Surf_min about 0.01 m2 for n = 5) after New vs Old registration; objects present in each scan identif…",[1035],[23],[23],[1050,1040],"New",[1030],{"slug":1053,"sourceId":1030,"sourceLabel":1031,"sourceYear":1032,"table":33,"note":1054,"datasets":1055,"metrics":1056,"families":1057,"methods":1058,"methodIds":1060,"rows":578,"failures":30},"bosche2010asbuiltdims-table-4","After object fine registration (per-object ICP refinement of recognized objects); compare with Table 2",[1035],[23],[23],[1059],"New with object fine registration",[1030],{"slug":1062,"sourceId":1030,"sourceLabel":1031,"sourceYear":1032,"table":244,"note":1063,"datasets":1064,"metrics":1065,"families":1066,"methods":1067,"methodIds":1068,"rows":1069,"failures":30},"bosche2010asbuiltdims-table-5","As-built minus as-designed pose of the 16 exterior columns from Scan 4 only; no ground truth or manual survey available; the Delta Z columns (all 0.0…",[1035],[23],[23],[1059],[1030],48,{"slug":1071,"sourceId":1030,"sourceLabel":1031,"sourceYear":1032,"table":1072,"note":1073,"datasets":1074,"metrics":1075,"families":1076,"methods":1077,"methodIds":1078,"rows":1079,"failures":30},"bosche2010asbuiltdims-table-6","Table 6","Difference between as-built and as-designed distances between structurally connected columns (Scan 4 only); no ground truth",[1035],[23],[23],[1059],[1030],44,{"slug":1081,"sourceId":1030,"sourceLabel":1031,"sourceYear":1032,"table":1082,"note":1083,"datasets":1084,"metrics":1085,"families":1086,"methods":1087,"methodIds":1088,"rows":63,"failures":30},"bosche2010asbuiltdims-text-sec-2-4-2","Text Sec. 2.4.2","Model fine registration time for Scan 4 (about 650,000 points, about 20,000 facets), CPU implementation",[1035],[23],[23],[1039],[1030],{"slug":1090,"sourceId":1030,"sourceLabel":1031,"sourceYear":1032,"table":1091,"note":1092,"datasets":1093,"metrics":1094,"families":1095,"methods":1096,"methodIds":1097,"rows":63,"failures":30},"bosche2010asbuiltdims-text-sec-3-3-1","Text Sec. 3.3.1","Correlation between calculated point pose deviations and distance of the columns to the scanner (20-80 m), Scan 4",[1035],[23],[23],[1059],[1030],{"slug":1099,"sourceId":1030,"sourceLabel":1031,"sourceYear":1032,"table":1100,"note":1101,"datasets":1102,"metrics":1103,"families":1104,"methods":1105,"methodIds":1106,"rows":154,"failures":30},"bosche2010asbuiltdims-text-sec-3-3-2","Text Sec. 3.3.2","Processing time of Scan 4 including model fine registration, recognition and as-built pose calculation (30 min of it for the as-built pose calculation…",[1035],[23],[23],[1059],[1030],{"slug":1108,"sourceId":1109,"sourceLabel":1110,"sourceYear":1111,"table":69,"note":1112,"datasets":1113,"metrics":1115,"families":1116,"methods":1117,"methodIds":1121,"rows":102,"failures":30},"bosche2012planebim-table-1","bosche2012planebim","Bosché, 2012",2012,"Two experienced users each performed 12 coarse registrations of Engineering V scans to the 3D model; proposed system used 10% of the scan points with…",[1114],"Engineering V dataset (University of Waterloo)",[23],[23],[1118,1119,1120],"Geomagic Studio (manual point-based coarse registration)","Trimble RealWorks (manual point-based coarse registration)","proposed plane-based system (automated scan plane extraction)",[1109],{"slug":1123,"sourceId":1109,"sourceLabel":1110,"sourceYear":1111,"table":108,"note":1124,"datasets":1125,"metrics":1126,"families":1127,"methods":1128,"methodIds":1129,"rows":102,"failures":30},"bosche2012planebim-table-2","Quality after the same robust ICP-based fine registration [6] applied to each coarse result: N. Matches = points within 25 mm of the model, RMSE of th…",[1114],[23],[23],[1120],[1109],{"slug":1131,"sourceId":1109,"sourceLabel":1110,"sourceYear":1111,"table":1132,"note":1133,"datasets":1134,"metrics":1136,"families":1137,"methods":1138,"methodIds":1140,"rows":154,"failures":30},"bosche2012planebim-text-sec-4-2-2","Text Sec.4.2.2","Single plane extracted after the user clicks one point (Fig. 5 example)",[1135],"laser scanned point cloud shown in Fig. 5 (dataset not stated)",[23],[23],[1139],"proposed semi-automated one-click RANSAC plane extraction",[1109],{"slug":1142,"sourceId":1109,"sourceLabel":1110,"sourceYear":1111,"table":1143,"note":1144,"datasets":1145,"metrics":1147,"families":1148,"methods":1149,"methodIds":1152,"rows":63,"failures":154},"bosche2012planebim-text-sec-5-3","Text Sec.5.3","Bicocca condominium scans (about 5 M points each, acquired far from the building) using 25% of the points",[1146],"Bicocca condominium project (Milan)",[23],[23],[1150,1151],"proposed system, automated scan plane extraction","proposed system, semi-automated one-click RANSAC plane extraction",[1109],{"slug":1154,"sourceId":1155,"sourceLabel":1156,"sourceYear":1157,"table":1158,"note":1159,"datasets":1160,"metrics":1163,"families":1164,"methods":1165,"methodIds":1167,"rows":356,"failures":356},"bosse-zlot2009-ctscan-text-sec-iii","bosse_zlot2009_ctscan","Bosse & Zlot, 2009",2009,"Text Sec. III","industrial compound, flat paved, about 200 m; sliding windows of 5 to 30 m aligned at window start; reference = 2D SLAM trajectory of a separate horiz…",[1161,1162],"authors' spinning-laser data (industrial)","authors' spinning-laser data (off-road)",[23],[23],[1166],"3D sweep-matching (continuous-time ICP)",[1155],{"slug":1169,"sourceId":1155,"sourceLabel":1156,"sourceYear":1157,"table":454,"note":1170,"datasets":1171,"metrics":1173,"families":1174,"methods":1175,"methodIds":1177,"rows":154,"failures":154},"bosse-zlot2009-ctscan-text-sec-iv","average processing cost of the ICP sweep-matching in MATLAB",[1172],"authors' spinning-laser data",[23],[23],[1176],"3D sweep-matching (continuous-time ICP), MATLAB implementation",[1155],{"slug":1179,"sourceId":1180,"sourceLabel":1181,"sourceYear":1111,"table":1182,"note":1183,"datasets":1184,"metrics":1186,"families":1187,"methods":1188,"methodIds":1190,"rows":274,"failures":274},"zebedee2012-text-sec-iv-a","zebedee2012","Bosse et al., 2012","Text Sec.IV-A","Pseudostationary tests: cart with spinning SICK stationary, Zebedee waved by hand around a box pattern within about 1 m; open-loop solution without fi…",[1185],"authors' pseudostationary experiments",[75,23],[78,23],[1189],"Zebedee SLAM open-loop (0 fixed views)",[1180],{"slug":1192,"sourceId":1180,"sourceLabel":1181,"sourceYear":1111,"table":1193,"note":1194,"datasets":1195,"metrics":1197,"families":1198,"methods":1199,"methodIds":1205,"rows":52,"failures":654},"zebedee2012-text-sec-iv-b","Text Sec.IV-B","Handheld Zebedee tethered to a pushcart carrying a spinning SICK LMS291; looped path traversed twice in each environment; the spinning-laser data give…",[1196],"authors' mobile mapping experiments",[75,23],[78,23],[1200,1201,1202,1189,1203,1204],"Zebedee SLAM closed-loop","Zebedee SLAM global registration","Zebedee SLAM open-loop","Zebedee SLAM open-loop (2 fixed views)","Zebedee SLAM open-loop (5 fixed views)",[1180],{"slug":1207,"sourceId":1180,"sourceLabel":1181,"sourceYear":1111,"table":1208,"note":1209,"datasets":1210,"metrics":1212,"families":1213,"methods":1214,"methodIds":1218,"rows":120,"failures":274},"zebedee2012-text-sec-iv-c","Text Sec.IV-C","Vicon motion capture (14 cameras, about 2 x 2 m tracked region, mm position precision, about 1 deg orientation precision): handheld run with loops and…",[1211],"authors' Vicon experiment",[568,23],[25,23],[1215,1216,1217,1189,1203,1204],"Zebedee SLAM closed-loop (0 fixed views)","Zebedee SLAM closed-loop (2 fixed views)","Zebedee SLAM closed-loop (5 fixed views)",[1180],{"slug":1220,"sourceId":1180,"sourceLabel":1181,"sourceYear":1111,"table":1221,"note":1222,"datasets":1223,"metrics":1225,"families":1226,"methods":1227,"methodIds":1229,"rows":356,"failures":274},"zebedee2012-text-sec-iv-d","Text Sec.IV-D","Stairwell between the first and third floors with deliberate oscillation stoppages of 3 to 6 s (second-generation handheld), and a hands-free backpack…",[1224],"authors' operator-control experiments",[568,23],[25,23],[1216,1189,1228],"Zebedee SLAM open-loop (2 fixed views, hands-free)",[1180],{"slug":1231,"sourceId":1232,"sourceLabel":1233,"sourceYear":1234,"table":1235,"note":1236,"datasets":1237,"metrics":1239,"families":1240,"methods":1241,"methodIds":1248,"rows":120,"failures":30},"bouaziz2013sparseicp-fig-4","bouaziz2013sparseicp","Bouaziz et al., 2013",2013,"Fig. 4","Virtually scanned 'owl' model registered to its ground truth; e = RMSE of registered point locations with respect to the ground-truth alignment; value…",[1238],"virtually scanned 'owl' statue model (lgg.epfl.ch\u002Fstatues)",[23],[23],[1242,1243,1244,1245,1246,1247],"initial alignment (before registration)","l1-ICP (p = 1) without explicit outlier management","lp-ICP, p = 0.4 (proposed)","traditional ICP, p = 2, correspondences pruned above dth = 10%","traditional ICP, p = 2, correspondences pruned above dth = 20%","traditional ICP, p = 2, correspondences pruned above dth = 5%",[1232],{"slug":1250,"sourceId":1251,"sourceLabel":1252,"sourceYear":213,"table":69,"note":1253,"datasets":1254,"metrics":1256,"families":1257,"methods":1258,"methodIds":1262,"rows":608,"failures":641},"brossard2020icpcov-table-1","brossard2020icpcov","Brossard et al., 2020","ICP covariance consistency averaged over 8 sequences, 1020 registrations x 1000 initializations; Q_ini 0.1 m and 10 deg; noise and bias SD 5 cm; NNE t…",[1255],"Challenging data sets for point cloud registration (Pomerleau et al. 2012)",[23],[23],[1259,1260,1261],"Q_censi (closed-form, Censi 2007)","Q_monte carlo (65 Monte Carlo ICP samples)","proposed",[1251,1263],"censi2007covariance",{"slug":1265,"sourceId":1251,"sourceLabel":1252,"sourceYear":213,"table":108,"note":1266,"datasets":1267,"metrics":1268,"families":1269,"methods":1270,"methodIds":1274,"rows":1069,"failures":63},"brossard2020icpcov-table-2","Trajectory consistency: Mahalanobis distance of compounded ICP trajectories to ground truth, averaged over 40 initial trajectories per sequence; targe…",[1255],[23],[23],[1271,1272,1273],"CELLO-3D","ini.+ICP (fusion without cross-covariance)","proposed (full ML covariance, Eq. 15)",[1251,1275],"landry2019cello3d",{"slug":1277,"sourceId":1251,"sourceLabel":1252,"sourceYear":213,"table":1278,"note":1279,"datasets":1280,"metrics":1281,"families":1282,"methods":1283,"methodIds":1284,"rows":63,"failures":154},"brossard2020icpcov-text-sec-iv-c","Text Sec. IV-C","Execution time of the covariance computation per registration pair (Algorithm 1)",[1255],[23],[23],[1261],[1251],{"slug":1286,"sourceId":1287,"sourceLabel":1288,"sourceYear":107,"table":17,"note":1289,"datasets":1290,"metrics":1294,"families":1295,"methods":1296,"methodIds":1300,"rows":849,"failures":30},"bueno2018plcs-table-3","bueno2018plcs","Bueno et al., 2018","4-PlCS on the three simulated datasets; ranking by plane support then RMSEc; errors are against exact ground truth; eps_R = absolute difference of qua…",[1291,1292,1293],"House-1 (simulated from BIM, sigma 2 mm noise)","House-2 (simulated from BIM, sigma 2 mm noise)","Steel-1 (simulated from BIM, sigma 2 mm noise)",[23],[23],[1297,1298,1299],"4-PlCS (proposed)","4-PlCS (proposed), rank 1 candidate (correct transformation)","4-PlCS (proposed), rank 2 candidate (other transformation)",[1287],{"slug":1302,"sourceId":1287,"sourceLabel":1288,"sourceYear":107,"table":33,"note":1303,"datasets":1304,"metrics":1306,"families":1307,"methods":1308,"methodIds":1314,"rows":1315,"failures":30},"bueno2018plcs-table-4","4-PlCS on the real UW-E5 dataset, five top-ranked transformations; ground truth is the result of fine registration, not an independent survey; the cor…",[1305],"UW-E5 (real; University of Waterloo Engineering V)",[23],[23],[1297,1309,1310,1311,1312,1313],"4-PlCS (proposed), rank 1 candidate (incorrect transformation)","4-PlCS (proposed), rank 2 candidate (correct transformation)","4-PlCS (proposed), rank 3 candidate (incorrect transformation)","4-PlCS (proposed), rank 4 candidate (incorrect transformation)","4-PlCS (proposed), rank 5 candidate (incorrect transformation)",[1287],21,{"slug":1317,"sourceId":1287,"sourceLabel":1288,"sourceYear":107,"table":244,"note":1318,"datasets":1319,"metrics":1321,"families":1322,"methods":1323,"methodIds":1324,"rows":1315,"failures":30},"bueno2018plcs-table-5","4-PlCS on the real Mercury-1 dataset, five top-ranked transformations; ground truth is the result of fine registration, not an independent survey; the…",[1320],"Mercury-1 (real)",[23],[23],[1297,1309,1310,1311,1312,1313],[1287],{"slug":1326,"sourceId":1287,"sourceLabel":1288,"sourceYear":107,"table":1072,"note":1327,"datasets":1328,"metrics":1329,"families":1330,"methods":1331,"methodIds":1338,"rows":1339,"failures":30},"bueno2018plcs-table-6","Point Support variant: support re-evaluated on the points of matched patches (gamma_min 40%); first three ranked transformations; correct transformati…",[1291,1292,1320,1293,1305],[23],[23],[1332,1333,1334,1335,1336,1337],"4-PlCS with Point Support (variant)","4-PlCS with Point Support (variant), rank 1 candidate (correct transformation)","4-PlCS with Point Support (variant), rank 1 candidate (incorrect transformation)","4-PlCS with Point Support (variant), rank 2 candidate (correct transformation)","4-PlCS with Point Support (variant), rank 2 candidate (incorrect transformation)","4-PlCS with Point Support (variant), rank 3 candidate (incorrect transformation)",[1287],35,{"slug":1341,"sourceId":1287,"sourceLabel":1288,"sourceYear":107,"table":621,"note":1342,"datasets":1343,"metrics":1344,"families":1345,"methods":1346,"methodIds":1348,"rows":641,"failures":30},"bueno2018plcs-table-7","4-PlCS versus 4.5-PlCS (known vertical axis added as a virtual horizontal plane); both give the same final ranking; eps_T of the correct transformatio…",[1291,1292,1320,1293,1305],[23],[23],[1297,1347],"4.5-PlCS (proposed variant)",[1287],{"slug":1350,"sourceId":1287,"sourceLabel":1288,"sourceYear":107,"table":633,"note":1351,"datasets":1352,"metrics":1353,"families":1354,"methods":1355,"methodIds":1357,"rows":598,"failures":30},"bueno2018plcs-table-8","4-PlCS versus the standard 3-plane congruent sets (3-PlCS) baseline; rank and eps_T refer to the correct transformation",[1291,1292,1320,1293,1305],[23],[23],[1356,1297],"3-PlCS (standard baseline)",[1287],{"slug":1359,"sourceId":1360,"sourceLabel":1361,"sourceYear":562,"table":91,"note":1362,"datasets":1363,"metrics":1365,"families":1366,"methods":1367,"methodIds":1373,"rows":641,"failures":30},"steamlio2025-table-i","steamlio2025","Burnett et al., 2025","KITTI-raw (motion-distorted, LiDAR only, 22 km); KITTI relative translation error; Overall = average over all segments of all sequences, Seq. Avg. = m…",[1364],"KITTI-raw",[438,38],[40,441],[1368,1369,1370,1371,1372],"CT-ICP [18]","Constant Velocity (ablation baseline)","KISS-ICP [5]","STEAM-ICP [12]","STEAM-LO (Ours, LiDAR only)",[596,577,1360],{"slug":1375,"sourceId":1360,"sourceLabel":1361,"sourceYear":562,"table":325,"note":1376,"datasets":1377,"metrics":1379,"families":1380,"methods":1381,"methodIds":1391,"rows":907,"failures":63},"steamlio2025-table-ii","Newer College Dataset (handheld, 6 km); RMS ATE after Umeyama alignment; star = explicit loop closures, dagger = results from DLIOM [71], double dagge…",[1378],"Newer College Dataset",[568,38],[25,40],[1382,1383,1369,1384,1385,1386,1387,1388,1389,1390],"CLIO* [60] (loop closures, uses camera)","CT-ICP* [18] (explicit loop closures)","DLIO [11]","FAST-LIO2 [10] (result from [71])","KISS-ICP [5] (result from [71])","SLICT* [52] (explicit loop closures)","STEAM-LIO (Ours)","STEAM-LO (Ours)","STEAM-LO + Gyro (Ours)",[596,1392,321,577,1393,1360],"dlio2023","slict2023",{"slug":1395,"sourceId":1360,"sourceLabel":1361,"sourceYear":562,"table":279,"note":1396,"datasets":1397,"metrics":1399,"families":1400,"methods":1401,"methodIds":1409,"rows":1410,"failures":30},"steamlio2025-table-iii","Boreas test set (102 km, 4.3 h, repeated route over one year incl. snowstorms); KITTI-style translational drift (%) and rotational drift (deg\u002F100 m);…",[1398],"Boreas",[438,439,38],[40,441],[1402,1403,1404,1405,1406,1407,1408],"STEAM-LIO","STEAM-LO","STEAM-LO (SE2)","STEAM-RIO","STEAM-RO","VTR3-Lidar [1]","VTR3-Radar [1]",[1360],47,{"slug":1412,"sourceId":1360,"sourceLabel":1361,"sourceYear":562,"table":731,"note":1413,"datasets":1414,"metrics":1415,"families":1416,"methods":1417,"methodIds":1418,"rows":224,"failures":274},"steamlio2025-table-iv","Ablation: ATE on Newer College 01-Short when scaling the default power spectral density diag(Q) = {50, 50, 50, 5, 5, 5}",[1378],[568],[25],[1402,1403],[1360],{"slug":1420,"sourceId":1360,"sourceLabel":1361,"sourceYear":562,"table":818,"note":1421,"datasets":1422,"metrics":1423,"families":1424,"methods":1425,"methodIds":1427,"rows":274,"failures":30},"steamlio2025-table-v","Boreas radar odometry supplementary results: STEAM-RIO++ competition configuration (four-scan window, Huber loss, higher gyro weight, 1 m keyframing),…",[1398],[438,439,38],[40,441],[1426],"STEAM-RIO++",[1360],{"slug":1429,"sourceId":1360,"sourceLabel":1361,"sourceYear":562,"table":1430,"note":1431,"datasets":1432,"metrics":1433,"families":1434,"methods":1435,"methodIds":1438,"rows":274,"failures":30},"steamlio2025-text-sec-v-b","Text Sec.V-B","Overall ATE over the entire Newer College Dataset computed by concatenating squared errors across all timestamps of all sequences",[1378],[568],[25],[1436,1437,1388],"DLIO","FAST-LIO2",[1392,321,1360],{"slug":1440,"sourceId":1360,"sourceLabel":1361,"sourceYear":562,"table":1441,"note":1442,"datasets":1443,"metrics":1444,"families":1445,"methods":1446,"methodIds":1447,"rows":154,"failures":30},"steamlio2025-text-sec-v-d","Text Sec.V-D","Normalized estimation error squared of frame-to-frame odometry for STEAM-LIO on the first 100 s of Boreas 2021-01-26-10-59 (ideal 1.0)",[1398],[23],[23],[1402],[1360],{"slug":1449,"sourceId":1450,"sourceLabel":1451,"sourceYear":698,"table":1452,"note":1453,"datasets":1454,"metrics":1456,"families":1457,"methods":1458,"methodIds":1461,"rows":63,"failures":63},"cai2021ikdtree-text-sec-v-a","cai2021ikdtree","Cai et al., 2021","Text Sec. V-A","Randomized test: 5,000 initial points in a 10 m cube, 1,000 operations of 200-point insertions and 5-NN queries, periodic box-wise deletes and 2,000-p…",[1455],"randomized synthetic points",[23],[23],[1459,1460],"Static K-D Tree (PCL)","ikd-Tree",[1450],{"slug":1463,"sourceId":1450,"sourceLabel":1451,"sourceYear":698,"table":1464,"note":1465,"datasets":1466,"metrics":1468,"families":1469,"methods":1470,"methodIds":1475,"rows":120,"failures":356},"cai2021ikdtree-text-sec-v-b","Text Sec. V-B","FAST-LIO on a real outdoor scene with a Livox Avia; time to fuse one new LiDAR scan averaged over the most recent 100 scans; ikd-Tree replaces the sta…",[1467],"authors' outdoor recording",[148,38],[40],[1471,1472,1460,1473,1474],"FAST-LIO with ikd-Tree","FAST-LIO with static K-D tree","original FAST-LIO with static k-d tree","static k-d tree",[1450,1476],"fastlio2021",{"slug":1478,"sourceId":763,"sourceLabel":1479,"sourceYear":698,"table":325,"note":1480,"datasets":1481,"metrics":1483,"families":1484,"methods":1485,"methodIds":1506,"rows":1514,"failures":52},"orbslam3-2021-table-ii","Campos et al., 2021","EuRoC single session, RMS ATE (m); ORB-SLAM3 median of 10 executions, Sim(3) alignment for monocular and SE(3) otherwise; other systems as reported by…",[1482],"EuRoC",[568],[25],[1486,1487,1488,1489,1490,1491,1492,1493,1494,1495,1496,1497,1498,1499,1500,1501,1502,1503,1504,1505],"BASALT (stereo-inertial)","DSM (monocular)","DSO (monocular)","Kimera (stereo-inertial)","MCSKF (monocular-inertial)","OKVIS (monocular-inertial)","ORB-SLAM (monocular)","ORB-SLAM2 (stereo)","ORB-SLAM3 (monocular)","ORB-SLAM3 (monocular-inertial)","ORB-SLAM3 (stereo)","ORB-SLAM3 (stereo-inertial)","ORBSLAM-VI (monocular-inertial)","ROVIO (monocular-inertial)","SVO (monocular)","SVO (stereo)","VI-DSO (monocular-inertial)","VINS-Fusion (stereo)","VINS-Fusion (stereo-inertial)","VINS-Mono (monocular-inertial)",[1507,1508,1509,1510,119,1511,763,1512,1513,251],"dso2018","kimera2020","mourikis2007msckf","okvis2015","orbslam2_2017","svo2017","vinsfusion2019",97,{"slug":1516,"sourceId":763,"sourceLabel":1479,"sourceYear":698,"table":731,"note":1517,"datasets":1518,"metrics":1520,"families":1521,"methods":1522,"methodIds":1523,"rows":429,"failures":30},"orbslam3-2021-table-iv","TUM-VI room sequences (ground truth over the whole trajectory), RMS ATE (m) of ORB-SLAM3 in four sensor configurations, median of 3 executions; monocu…",[1519],"TUM-VI",[568],[25],[1494,1495,1496,1497],[763],{"slug":1525,"sourceId":763,"sourceLabel":1479,"sourceYear":698,"table":818,"note":1526,"datasets":1527,"metrics":1528,"families":1529,"methods":1530,"methodIds":1533,"rows":102,"failures":30},"orbslam3-2021-table-v","EuRoC multi-session: all sessions of one environment processed sequentially, single global alignment, RMS ATE (m); ORB-SLAM3 median of 5 executions ag…",[1482],[568],[25],[1531,1494,1495,1496,1497,1532],"CCM-SLAM (monocular)","VINS (monocular-inertial)",[763,251],{"slug":1535,"sourceId":763,"sourceLabel":1479,"sourceYear":698,"table":827,"note":1536,"datasets":1537,"metrics":1538,"families":1539,"methods":1540,"methodIds":1541,"rows":416,"failures":30},"orbslam3-2021-table-vi","Tracking-thread total time on EuRoC V202 (752x480 at 20 Hz; IMU 200 Hz for inertial modes), mean in ms (standard deviation in the table); mapping and…",[1482],[38],[40],[1493,1494,1495,1496,1497],[1511,763],{"slug":1543,"sourceId":1544,"sourceLabel":1545,"sourceYear":213,"table":1546,"note":1547,"datasets":1548,"metrics":1550,"families":1552,"methods":1554,"methodIds":1559,"rows":120,"failures":274},"pronto2020-sec-7-text","pronto2020","Camurri et al., 2020","Sec. 7 text","Values stated in the text of Section 7",[1549],"Pronto experiments (authors)",[329,1551,23],"end_to_end_drift",[25,1553,23],"drift",[1555,1556,1557,1558],"Pronto (IMU+LO+AICP)","Pronto (IMU+LO+AICP+VO)","Pronto (IMU+LO+VO+AICP)","Pronto (IMU+LO, without AICP)",[1544],{"slug":1561,"sourceId":1544,"sourceLabel":1545,"sourceYear":213,"table":108,"note":1562,"datasets":1563,"metrics":1564,"families":1565,"methods":1566,"methodIds":1571,"rows":618,"failures":63},"pronto2020-table-2","Summary of experiments; RPE = translational part of relative pose error evaluated over 10 m distance; OL = online, CL = used in the control loop; GT =…",[1549],[330],[332],[1567,1568,1569,1570],"Pronto (IMU+LO only (without VO and AICP), offline)","Pronto (IMU+LO+AICP, online, control loop)","Pronto (IMU+LO+VO+AICP, offline)","Pronto (IMU+LO+VO+AICP, online, control loop)",[1544],{"slug":1573,"sourceId":1574,"sourceLabel":1575,"sourceYear":306,"table":91,"note":1576,"datasets":1577,"metrics":1582,"families":1583,"methods":1584,"methodIds":1586,"rows":618,"failures":154},"gvins2022-table-i","gvins2022","Cao et al., 2022","Experiment profiles: maximum velocity and overall RTK fixed rate (context for the evaluation)",[1578,1579,1580,1581],"GVINS simulation","own dataset: indoor-outdoor","own dataset: sports field","own dataset: urban driving",[23],[23],[1585],"experiment profile",[],{"slug":1588,"sourceId":1574,"sourceLabel":1575,"sourceYear":306,"table":325,"note":1589,"datasets":1590,"metrics":1591,"families":1592,"methods":1593,"methodIds":1595,"rows":618,"failures":30},"gvins2022-table-ii","Initialisation quality of GVINS: error of the local-ENU yaw offset and of the anchor point",[1578,1579,1580,1581],[23],[23],[1594],"GVINS initialisation",[],{"slug":1597,"sourceId":1574,"sourceLabel":1575,"sourceYear":306,"table":279,"note":1598,"datasets":1599,"metrics":1600,"families":1601,"methods":1602,"methodIds":1607,"rows":29,"failures":63},"gvins2022-table-iii","RMSE (m) of global positioning; GVINS, VINS-Fusion (VIO plus RTKLIB SPP, loosely coupled) and RTKLIB compared to RTK without alignment (simulation: to…",[1578,1579,1580,1581],[568],[25],[1603,1604,1605,1606],"GVINS","RTKLIB (SPP)","VINS-Fusion (mono+IMU+GNSS SPP)","VINS-Mono",[251],{"slug":1609,"sourceId":1574,"sourceLabel":1575,"sourceYear":306,"table":1610,"note":1611,"datasets":1612,"metrics":1613,"families":1614,"methods":1615,"methodIds":1617,"rows":274,"failures":30},"gvins2022-text-sec-viii-b","Text Sec.VIII-B","Time for GNSS-VI initialisation after visual-inertial alignment (indoor-outdoor mostly waiting for navigation messages)",[1579,1580,1581],[23],[23],[1616],"GNSS-VI initialisation",[],{"slug":1619,"sourceId":1574,"sourceLabel":1575,"sourceYear":306,"table":1620,"note":1621,"datasets":1622,"metrics":1623,"families":1624,"methods":1625,"methodIds":1628,"rows":416,"failures":30},"gvins2022-text-sec-viii-b5","Text Sec.VIII-B5","Computation on the urban driving sequence; Intel i7-8700K at 3.7 GHz, 32 GB",[1581],[38],[40],[1626,1603,1627,1606],"Front end shared by VINS-Mono, VINS-Fusion and GVINS","VINS-Fusion (pose-graph GNSS fusion)",[251],{"slug":1630,"sourceId":1631,"sourceLabel":1632,"sourceYear":562,"table":1633,"note":1634,"datasets":1635,"metrics":1637,"families":1638,"methods":1639,"methodIds":1645,"rows":339,"failures":154},"resple2025-table-ii-ntu-viral","resple2025","Cao et al., 2025","Table II (NTU VIRAL)","NTU VIRAL drone sequences, horizontal OS1-16 for RESPLE; APE RMSE with the official NTU VIRAL evaluation script; T-LO, C-MLO (two LiDARs) and F-LIO2 v…",[1636],"NTU VIRAL",[329],[25],[1640,1641,1642,1643,1644],"C-MLO (CTE-MLO, 2 LiDARs)","F-LIO2 (FAST-LIO2)","R-LIO (RESPLE LiDAR-inertial)","R-LO (RESPLE LiDAR-only)","T-LO (Traj-LO)",[321,1631,1646],"trajlo2024",{"slug":1648,"sourceId":1631,"sourceLabel":1632,"sourceYear":562,"table":1649,"note":1650,"datasets":1651,"metrics":1653,"families":1654,"methods":1655,"methodIds":1663,"rows":301,"failures":618},"resple2025-table-iii-grandtour","Table III (GrandTour)","GrandTour ANYmal D quadruped with Boxi rig: L1 Hesai XT32, L2 Livox Mid360, I built-in IMU of L2; APE RMSE via evo after interpolating estimates at gr…",[1652],"GrandTour",[329],[25],[1656,1657,1658,1659,1660,1661,1662],"C-MLO (L1+L2)","F-LIO2 (L1+I)","R-LIO (L1+I)","R-LO (L1)","R-MLIO (L1+L2+I)","R-MLO (L1+L2)","T-LO (L1)",[321,1631,1646],{"slug":1665,"sourceId":1631,"sourceLabel":1632,"sourceYear":562,"table":827,"note":1666,"datasets":1667,"metrics":1669,"families":1670,"methods":1671,"methodIds":1676,"rows":578,"failures":30},"resple2025-table-vi","Runtime comparison on HD_03 (helmet Livox Mid360); processing time per available interval (available time 50 ms for T-LO and SLICT2, 10 ms for the oth…",[1668],"HelmDyn (own dataset)",[23,38],[40,23],[1672,1673,1674,1675,1644],"C-MLO (CTE-MLO)","R-LIO","R-LO","SLICT2",[1631,1646],{"slug":1678,"sourceId":1631,"sourceLabel":1632,"sourceYear":562,"table":1679,"note":1680,"datasets":1681,"metrics":1683,"families":1684,"methods":1685,"methodIds":1690,"rows":416,"failures":30},"resple2025-text-sec-v-c-r-campus","Text Sec. V-C (R-Campus)","Own R-Campus sequence: Livox Avia on the DIABLO wheeled bipedal robot, about 1400 m at 1.2 m\u002Fs, start and end at the same place; end-to-end error",[1682],"R-Campus (own experiment)",[1551],[1553],[1686,1437,1687,1688,1689],"CTE-MLO","RESPLE LIO","RESPLE LO","Traj-LO",[321,1631,1646],{"slug":1692,"sourceId":1263,"sourceLabel":1693,"sourceYear":1694,"table":1695,"note":1696,"datasets":1697,"metrics":1699,"families":1700,"methods":1701,"methodIds":1705,"rows":1042,"failures":30},"censi2007covariance-fig-4-table-circle-scan-matching","Censi, 2007",2007,"Fig. 4 table (circle, scan matching)","Std of scan-matching error: Monte Carlo sample (true) vs predicted; sigma(w1), sigma(w2) are errors projected on the observable manifold O",[1698],"simulation (Monte Carlo, 300 runs)",[23],[23],[1702,1703,1261,1704],"Hessian [1]","Offline [1]","true (Monte Carlo sample)",[1263],{"slug":1707,"sourceId":1263,"sourceLabel":1693,"sourceYear":1694,"table":1708,"note":1696,"datasets":1709,"metrics":1710,"families":1711,"methods":1712,"methodIds":1713,"rows":1042,"failures":30},"censi2007covariance-fig-4-table-corridor-scan-matching","Fig. 4 table (corridor, scan matching)",[1698],[23],[23],[1702,1703,1261,1704],[1263],{"slug":1715,"sourceId":1263,"sourceLabel":1693,"sourceYear":1694,"table":1716,"note":1717,"datasets":1718,"metrics":1719,"families":1720,"methods":1721,"methodIds":1723,"rows":52,"failures":30},"censi2007covariance-fig-4-table-square-localization","Fig. 4 table (square, localization)","Std of ICP pose error: Monte Carlo sample (true) vs covariance predicted by each estimator; 52-ray simulated sensor, 0.03 m noise, 300 runs",[1698],[23],[23],[1722,1261,1704],"CRB",[1263],{"slug":1725,"sourceId":1263,"sourceLabel":1693,"sourceYear":1694,"table":1726,"note":1717,"datasets":1727,"metrics":1728,"families":1729,"methods":1730,"methodIds":1731,"rows":224,"failures":30},"censi2007covariance-fig-4-table-square-scan-matching","Fig. 4 table (square, scan matching)",[1698],[23],[23],[1702,1703,1261,1704],[1263],{"slug":1733,"sourceId":1734,"sourceLabel":1735,"sourceYear":898,"table":1736,"note":1737,"datasets":1738,"metrics":1740,"families":1741,"methods":1742,"methodIds":1748,"rows":415,"failures":30},"censi2008-plicp-fig-3-table","censi2008_plicp","Censi, 2008","Fig. 3 table","Minguez et al. (2006) artificial-error experiment: each of 778 scans matched against a copy of itself displaced by a uniform random error up to the li…",[1739],"Minguez et al. (2006) wheelchair SICK log (778 scans)",[23],[23],[1743,1744,522,1745,1746,1747],"GPM","GPM o PLICP","IDC","MBICP","PLICP",[478,1734],{"slug":1750,"sourceId":1734,"sourceLabel":1735,"sourceYear":898,"table":1751,"note":1752,"datasets":1753,"metrics":1755,"families":1756,"methods":1757,"methodIds":1760,"rows":356,"failures":30},"censi2008-plicp-text-app-ii-b-table","Text App. II.B table","Correspondence search cost on the same log; naive search uses max |t| = 0.5 m and max |theta| = 25 deg",[1754],"Minguez et al. (2006) wheelchair SICK log",[148,23],[40,23],[1758,1759],"naive correspondence search","smart correspondence search",[1734],{"slug":1762,"sourceId":1734,"sourceLabel":1735,"sourceYear":898,"table":1763,"note":1764,"datasets":1765,"metrics":1766,"families":1767,"methods":1768,"methodIds":1769,"rows":618,"failures":30},"censi2008-plicp-text-sec-v-b-table","Text Sec. V.B table","Average iterations and execution time per scan matching on the same log; MBICP, ICP and IDC values copied from Minguez et al.; the author cautions tha…",[1754],[23,38],[40,23],[522,1745,1746,1747],[478,1734],{"slug":1771,"sourceId":1772,"sourceLabel":1773,"sourceYear":306,"table":325,"note":1774,"datasets":1775,"metrics":1780,"families":1781,"methods":1782,"methodIds":1787,"rows":1790,"failures":30},"lamp2-2022-table-ii","lamp2_2022","Chang et al., 2022","Loop-closure relative pose estimation with different ICP initializations on ground-truth and false loop-closure sets; SAC cumulative error threshold 3…",[1776,1777,1778,1779],"CoSTAR multi-robot dataset: Final","CoSTAR multi-robot dataset: KU","CoSTAR multi-robot dataset: Tunnel","CoSTAR multi-robot dataset: Urban",[23],[23],[1783,1784,1785,1786],"GT initialization (oracle)","OdomRot [8] initialization (LAMP 1.0)","SAC-IA initialization + GICP","TEASER++ initialization + GICP",[1788,1789],"rusu2009fpfh","yang2021teaser",64,{"slug":1792,"sourceId":1772,"sourceLabel":1773,"sourceYear":306,"table":279,"note":1793,"datasets":1794,"metrics":1795,"families":1796,"methods":1797,"methodIds":1800,"rows":608,"failures":30},"lamp2-2022-table-iii","Number of loop closures at each stage of the multi-robot front-end and back-end; LAMP 1.0 uses fixed-radius candidates, odometric ICP initialization a…",[1776,1777,1778,1779],[23],[23],[1798,1799],"LAMP 1.0","LAMP 2.0",[1772],{"slug":1802,"sourceId":1772,"sourceLabel":1773,"sourceYear":306,"table":731,"note":1803,"datasets":1804,"metrics":1805,"families":1806,"methods":1807,"methodIds":1809,"rows":1810,"failures":30},"lamp2-2022-table-iv","End-to-end system evaluation: data played back in real time to the base station (about 1 h per run), same odometry input for all variants; ground-trut…",[1776,1777,1778,1779],[329],[25],[1798,1799,1808],"LAMP 2.0 single robot (no inter-robot loop closures)",[1772],39,{"slug":1812,"sourceId":1813,"sourceLabel":1814,"sourceYear":16,"table":69,"note":1815,"datasets":1816,"metrics":1818,"families":1820,"methods":1822,"methodIds":1824,"rows":578,"failures":30},"charron2019bridgerobot-table-1","charron2019bridgerobot","Charron et al., 2019","After registering the UGV cloud to a Faro Focus M TLS reference cloud, girder depth measured at 100 evenly spaced 20 mm thick cross sections of the fi…",[1817],"authors' Conestogo River bridge data",[1819],"dimension_error",[1821],"task",[1823],"UGV SLAM map (EKF + scan-to-map ICP point-to-plane)",[1813],{"slug":1826,"sourceId":1813,"sourceLabel":1814,"sourceYear":16,"table":108,"note":1827,"datasets":1828,"metrics":1829,"families":1831,"methods":1832,"methodIds":1835,"rows":618,"failures":30},"charron2019bridgerobot-table-2","Local noise: residual distance from each point to a best-fit plane over about 2 m subsets on the underside and side of a girder",[1817],[1830],"plane_fit_residual",[78],[1833,1834],"Faro Focus 3D (TLS reference)","UGV (proposed platform and SLAM)",[1813],{"slug":1837,"sourceId":1813,"sourceLabel":1814,"sourceYear":16,"table":1838,"note":1839,"datasets":1840,"metrics":1841,"families":1842,"methods":1843,"methodIds":1845,"rows":63,"failures":63},"charron2019bridgerobot-text-evaluation-metrics","Text Evaluation Metrics","Field time to scan the Conestogo test site",[1817],[23],[23],[1844,1834],"Faro Focus M TLS",[1813],{"slug":1847,"sourceId":1848,"sourceLabel":1849,"sourceYear":68,"table":244,"note":1850,"datasets":1851,"metrics":1853,"families":1854,"methods":1855,"methodIds":1857,"rows":29,"failures":30},"charron2026slamcentric-table-5","charron2026slamcentric","Charron et al., 2026","Defect quantification on the labelled bridge map (defects segmented with SAM); no ground truth for defect size",[1852],"Park Street bridge",[23],[23],[1856],"proposed pipeline (defect map labelling and quantification)",[1848],{"slug":1859,"sourceId":1848,"sourceLabel":1849,"sourceYear":68,"table":1072,"note":1860,"datasets":1861,"metrics":1863,"families":1864,"methods":1865,"methodIds":1866,"rows":429,"failures":30},"charron2026slamcentric-table-6","Crack quantification on the labelled garage map; crack length crudely estimated as the largest distance between any two points of the defect cloud; no…",[1862],"Duke Street parking garage",[23],[23],[1856],[1848],{"slug":1868,"sourceId":1848,"sourceLabel":1849,"sourceYear":68,"table":621,"note":1869,"datasets":1870,"metrics":1871,"families":1872,"methods":1873,"methodIds":1877,"rows":224,"failures":30},"charron2026slamcentric-table-7","Reference-free map quality of the lidar inspection map for three refinement levels (None = online SLAM only, light = light loop closure, max = full of…",[1862,1852],[23,1830],[78,23],[1874,1875,1876],"online SLAM only (refinement None; the authors' re-implementation of the LVI-SAM architecture)","proposed pipeline, refinement light","proposed pipeline, refinement max",[1848],{"slug":1879,"sourceId":1848,"sourceLabel":1849,"sourceYear":68,"table":633,"note":1880,"datasets":1881,"metrics":1882,"families":1883,"methods":1884,"methodIds":1890,"rows":641,"failures":30},"charron2026slamcentric-table-8","Ray-tracing time per image for three images with the same timestamp near the start of the garage dataset; ablation of the proposed speed-ups (map crop…",[1862],[38],[40],[1885,1886,1887,1888,1889],"Fixed ray extension","No hit map","No map crop","No speed ups (naive ray-tracing)","Ours (all speed-ups)",[1848],{"slug":1892,"sourceId":1848,"sourceLabel":1849,"sourceYear":68,"table":644,"note":1893,"datasets":1894,"metrics":1895,"families":1896,"methods":1897,"methodIds":1899,"rows":340,"failures":30},"charron2026slamcentric-table-9","Lengths of unambiguous features measured on site (instrument not stated) versus the same features measured in the colourized map; measurements 1-5 bri…",[1862,1852],[1819],[1821],[1898],"proposed pipeline (colourized map; refinement level not stated)",[1848],{"slug":1901,"sourceId":1848,"sourceLabel":1849,"sourceYear":68,"table":1902,"note":1903,"datasets":1904,"metrics":1906,"families":1907,"methods":1908,"methodIds":1909,"rows":274,"failures":30},"charron2026slamcentric-text-sec-5-5-3","Text Sec. 5.5.3","Summary of Table 9 stated in the text",[1905],"Park Street bridge and Duke Street parking garage",[1819],[1821],[1898],[1848],{"slug":1911,"sourceId":1912,"sourceLabel":1913,"sourceYear":698,"table":91,"note":1914,"datasets":1915,"metrics":1916,"families":1917,"methods":1918,"methodIds":1924,"rows":1925,"failures":356},"chebrolu2021adaptive-table-i","chebrolu2021adaptive","Chebrolu et al., 2021","Frame-to-frame point-to-plane projective ICP inside SuMa on KITTI odometry; only the robust kernel or outlier scheme differs between rows",[469],[438,439],[441],[1919,1920,1921,1922,1923],"Adaptive Kernel (Barron [6])","Fixed Kernel (Geman-McClure)","Fixed Kernel (Huber)","Hand-Crafted Outlier Rejection [7] (SuMa original: Huber + rejection of correspondences >2 m or normal angle >30 deg)","Our Approach",[1912,432],120,{"slug":1927,"sourceId":1912,"sourceLabel":1913,"sourceYear":698,"table":1928,"note":1929,"datasets":1930,"metrics":1932,"families":1934,"methods":1936,"methodIds":1941,"rows":356,"failures":30},"chebrolu2021adaptive-text-sec-iv-b","Text Sec. IV-B","BA convergence test: camera poses perturbed with sigma in [0.1 m, 5 m], 20 instances per noise level, 500 instances; converged if final camera-centre…",[1931],"authors' CARLA-simulated bundle adjustment datasets",[1933],"success_rate",[1935],"success",[1937,1938,1939,1940],"Geman-McClure","Huber","Our approach (adaptive truncated kernel)","squared loss",[1912],{"slug":1943,"sourceId":1944,"sourceLabel":1945,"sourceYear":480,"table":1946,"note":1947,"datasets":1948,"metrics":1951,"families":1952,"methods":1953,"methodIds":1955,"rows":654,"failures":30},"chen1992pointtoplane-fig-5d-and-fig-6e-histogram-labels","chen1992pointtoplane","Chen & Medioni, 1992","Fig. 5d and Fig. 6e histogram labels","Registration error image: distance from the first surface to the second along the first-surface normal after registration; range images at 0.5 mm spat…",[1949,1950],"authors' range images (Mozart bust)","authors' range images (model tooth)",[23],[23],[1954],"Chen-Medioni registration",[1944],{"slug":1957,"sourceId":1944,"sourceLabel":1945,"sourceYear":480,"table":1958,"note":1959,"datasets":1960,"metrics":1961,"families":1962,"methods":1963,"methodIds":1965,"rows":120,"failures":30},"chen1992pointtoplane-text-p-152","Text p. 152","Two range images per object, object rotated about the y axis between views; identity matrix as initial transformation; epsilon_c = 0.01",[1949,1950],[23],[23],[1954,1964],"ground truth (set-up rotation)",[1944],{"slug":1967,"sourceId":1968,"sourceLabel":1969,"sourceYear":16,"table":91,"note":1970,"datasets":1971,"metrics":1973,"families":1974,"methods":1975,"methodIds":1979,"rows":1980,"failures":30},"sumapp2019-table-i","sumapp2019","Chen et al., 2019","KITTI raw road-category drives 2011_09_26_drive_0015_sync to 2011_10_03_drive_0047_sync renamed 30-41; relative errors averaged over segments of 5 to…",[1972],"KITTI raw (road category)",[438,439],[441],[1976,1977,1978],"SuMa","SuMa++","SuMa_nomovable",[432,1968],78,{"slug":1982,"sourceId":1968,"sourceLabel":1969,"sourceYear":16,"table":325,"note":1983,"datasets":1984,"metrics":1985,"families":1986,"methods":1987,"methodIds":1990,"rows":1991,"failures":224},"sumapp2019-table-ii","KITTI odometry training sequences 00-10; relative errors averaged over 100 to 800 m segments (rot deg\u002F100 m, trans %); asterisked sequences 00, 02, 05…",[436],[438,439],[441],[1988,1989,1976,1977,1978],"IMLS-SLAM [7]","LOAM [40]",[450,432,1968],96,{"slug":1993,"sourceId":1968,"sourceLabel":1969,"sourceYear":16,"table":1994,"note":1995,"datasets":1996,"metrics":1998,"families":1999,"methods":2000,"methodIds":2004,"rows":274,"failures":30},"sumapp2019-text-sec-iv","Text Sec.IV","Per-scan timing reported in text for the full SuMa++ pipeline",[1997,436],"KITTI",[38],[40],[2001,2002,2003],"SuMa++ (RangeNet++ step)","SuMa++ (loop closure integration)","SuMa++ (surfel mapping step)",[1968],{"slug":2006,"sourceId":1968,"sourceLabel":1969,"sourceYear":16,"table":1193,"note":2007,"datasets":2008,"metrics":2009,"families":2010,"methods":2011,"methodIds":2013,"rows":356,"failures":30},"sumapp2019-text-sec-iv-b","KITTI odometry test sequences evaluated on the benchmark server (no ground truth available to the authors)",[457],[438,23],[441,23],[2012,1977],"SuMa (original)",[432,1968],{"slug":2015,"sourceId":2016,"sourceLabel":2017,"sourceYear":213,"table":325,"note":2018,"datasets":2019,"metrics":2021,"families":2022,"methods":2023,"methodIds":2028,"rows":29,"failures":63},"overlapnet2020-table-ii","overlapnet2020","Chen et al., 2020","Loop closure detection vs state of the art; best candidate per query, 100 latest scans excluded, overlap threshold 30%; KITTI uses all cues, Ford uses…",[2020,469],"Ford Campus",[23],[23],[2024,2025,2026,2027,1976],"Histogram","M2DP","Ours (AllChannel, TwoHeads)","Ours (GeoOnly)",[2029,2016,432],"m2dp2016",{"slug":2031,"sourceId":2016,"sourceLabel":2017,"sourceYear":213,"table":279,"note":2032,"datasets":2033,"metrics":2034,"families":2035,"methods":2036,"methodIds":2041,"rows":224,"failures":30},"overlapnet2020-table-iii","Comparison with OverlapNet variants; CovNearestOfTop10 uses covariance-propagated Mahalanobis search space (prior pose information)",[2020,469],[23],[23],[2037,2038,2039,2040,2026,2027],"CovNearestOfTop10","DeltaOnly","GeoCovNearestOfTop10","MLPOnly",[2016],{"slug":2043,"sourceId":2016,"sourceLabel":2017,"sourceYear":213,"table":731,"note":2044,"datasets":2045,"metrics":2046,"families":2047,"methods":2048,"methodIds":2051,"rows":52,"failures":30},"overlapnet2020-table-iv","Relative yaw estimation errors without ICP, KITTI 00, no odometry prior, OREOS setup; * values produced by the OREOS authors",[469],[23,1933],[23,1935],[2049,2050,2026],"FPFH+RANSAC*","OREOS*",[2016],{"slug":2053,"sourceId":2016,"sourceLabel":2017,"sourceYear":213,"table":818,"note":2054,"datasets":2055,"metrics":2057,"families":2058,"methods":2059,"methodIds":2064,"rows":29,"failures":30},"overlapnet2020-table-v","Ablation on input modalities (overlap AUC and F1, yaw mean and std); dataset not named in the caption, context indicates KITTI because semantics are u…",[2056],"not_reported (context: KITTI odometry)",[23],[23],[2060,2061,2062,2063],"OverlapNet input: Depth","OverlapNet input: Depth+Normals","OverlapNet input: Depth+Normals+Intensity","OverlapNet input: Depth+Normals+Intensity+Semantics",[2016],{"slug":2066,"sourceId":2016,"sourceLabel":2017,"sourceYear":213,"table":2067,"note":2068,"datasets":2069,"metrics":2071,"families":2072,"methods":2073,"methodIds":2076,"rows":63,"failures":30},"overlapnet2020-text-sec-iii-b","Text Sec.III-B","Overlap estimation time for one scan pair",[2070],"not_reported",[23],[23],[2074,2075],"Exhaustive Eq. (3) evaluation","OverlapNet",[2016],{"slug":2078,"sourceId":2016,"sourceLabel":2017,"sourceYear":213,"table":2079,"note":2080,"datasets":2081,"metrics":2082,"families":2083,"methods":2084,"methodIds":2085,"rows":618,"failures":30},"overlapnet2020-text-sec-iv-g","Text Sec.IV-G","Runtime breakdown of OverlapNet",[2020,469],[38],[40],[2075],[2016],{"slug":2087,"sourceId":2088,"sourceLabel":2089,"sourceYear":306,"table":325,"note":2090,"datasets":2091,"metrics":2093,"families":2094,"methods":2095,"methodIds":2100,"rows":356,"failures":30},"dlo2022-table-ii","dlo2022","Chen et al., 2022a","Percentage of LiDAR scans dropped per data-recycling scheme, SubT Alpha course",[2092],"DARPA SubT Urban Circuit Alpha Course",[23],[23],[2096,2097,2098,2099],"DLO recycling scheme: Both","DLO recycling scheme: Covariances","DLO recycling scheme: KDTrees","DLO recycling scheme: None",[2088],{"slug":2102,"sourceId":2088,"sourceLabel":2089,"sourceYear":306,"table":279,"note":2103,"datasets":2104,"metrics":2106,"families":2107,"methods":2108,"methodIds":2115,"rows":2119,"failures":30},"dlo2022-table-iii","DARPA SubT Urban Circuit Alpha and Beta courses; competitor numbers and ground truth retrieved from the LOCUS paper [13], not rerun; APE max\u002Fmean\u002Fstd…",[2105],"DARPA SubT Urban Circuit (Alpha and Beta courses)",[329,113,23],[25,40,23],[2109,2110,2111,2112,2113,2114],"BLAM [12]","Cartographer [19]","DLO","LIO-Mapping [5]","LOAM [10]","LOCUS [13]",[2116,2088,2117,2118],"cartographer2016","liomapping2019","loam2014",60,{"slug":2121,"sourceId":2088,"sourceLabel":2089,"sourceYear":306,"table":2122,"note":2123,"datasets":2124,"metrics":2125,"families":2126,"methods":2127,"methodIds":2131,"rows":120,"failures":30},"dlo2022-text-sec-iii-a-1","Text Sec.III-A-1","Component evaluation on SubT Urban Alpha course (Velodyne VLP-16, VectorNav VN-100, 60 min); submapping schemes",[2092],[113,38],[40],[2128,2129,2130],"DLO variant: keyframe-based submapping, 1 m static threshold","DLO variant: keyframe-based submapping, adaptive threshold (DLO)","DLO variant: radius-based submapping (r = 10 m)",[2088],{"slug":2133,"sourceId":2088,"sourceLabel":2089,"sourceYear":306,"table":2134,"note":2135,"datasets":2136,"metrics":2137,"families":2138,"methods":2139,"methodIds":2142,"rows":274,"failures":30},"dlo2022-text-sec-iii-a-2","Text Sec.III-A-2","Component evaluation on SubT Urban Alpha course; data-structure recycling schemes",[2092],[113,38],[40],[2140,2141],"DLO variant: no data-structure reuse","DLO: full recycling scheme",[2088],{"slug":2144,"sourceId":2088,"sourceLabel":2089,"sourceYear":306,"table":2145,"note":2146,"datasets":2147,"metrics":2149,"families":2150,"methods":2151,"methodIds":2155,"rows":274,"failures":30},"dlo2022-text-sec-iii-a-3","Text Sec.III-A-3","Average convergence time over 100 benchmark alignments of two LiDAR scans (FastGICP benchmark code), identity prior",[2148],"FastGICP benchmark scan pair",[23],[23],[2152,2153,2154],"FastGICP multithreaded [17]","NanoGICP (DLO)","PCL GICP [20]",[2088],{"slug":2157,"sourceId":2088,"sourceLabel":2089,"sourceYear":306,"table":2158,"note":2159,"datasets":2160,"metrics":2162,"families":2163,"methods":2164,"methodIds":2165,"rows":154,"failures":30},"dlo2022-text-sec-iii-c","Text Sec.III-C","Field test: quadruped (Velodyne VLP-16, VN-100) tele-operated over three levels of an abandoned subway, about 856 m (Fig. 9)",[2161],"abandoned subway, Los Angeles (field test)",[1551],[1553],[2111],[2088],{"slug":2167,"sourceId":2168,"sourceLabel":2169,"sourceYear":306,"table":91,"note":2170,"datasets":2171,"metrics":2172,"families":2173,"methods":2174,"methodIds":2177,"rows":608,"failures":30},"ndtloam2022-table-i","ndtloam2022","Chen et al., 2022b","KITTI 00-10, odometry part only (initial pose): A-LOAM feature odometry versus NDT-LOAM weighted NDT with Scan2Key; position error (%) from the KITTI…",[469],[438],[441],[2175,2176],"ALOAM (odometry only)","NDT-LOAM (odometry only, wNDT + Scan2Key)",[392,2168],{"slug":2179,"sourceId":2168,"sourceLabel":2169,"sourceYear":306,"table":325,"note":2180,"datasets":2181,"metrics":2182,"families":2183,"methods":2184,"methodIds":2189,"rows":1410,"failures":30},"ndtloam2022-table-ii","KITTI 00-10 after refinement (LFA); position error (%); A-LOAM and NDT-LOAM run at 10 Hz; LOAM values from the original LOAM paper (1 Hz); F-LOAM valu…",[469],[438],[441],[2185,2186,2187,2188],"ALOAM","FLOAM [36]","LOAM (from original paper)","NDT-LOAM",[392,393,450,2168],{"slug":2191,"sourceId":2168,"sourceLabel":2169,"sourceYear":306,"table":279,"note":2192,"datasets":2193,"metrics":2194,"families":2195,"methods":2196,"methodIds":2198,"rows":102,"failures":30},"ndtloam2022-table-iii","KITTI 00, 05, 09; absolute pose error (m) computed with evo; LeGO-LOAM with loop closure; alignment not stated",[469],[329],[25],[2197,2188],"LeGO-LOAM",[395,2168],{"slug":2200,"sourceId":2168,"sourceLabel":2169,"sourceYear":306,"table":731,"note":2201,"datasets":2202,"metrics":2204,"families":2205,"methods":2206,"methodIds":2207,"rows":356,"failures":30},"ndtloam2022-table-iv","Kylin backpack; trajectory error = offset between start and end of a path that starts and ends in a regular area (no GPS ground truth); keyframe thres…",[2203],"Kylin backpack",[1551],[1553],[2197,2188],[395,2168],{"slug":2209,"sourceId":2168,"sourceLabel":2169,"sourceYear":306,"table":818,"note":2210,"datasets":2211,"metrics":2212,"families":2213,"methods":2214,"methodIds":2222,"rows":1339,"failures":30},"ndtloam2022-table-v","Runtime of modules for processing one scan (ms) on KITTI 04, 06, 07, 09 and backpack K1",[469,2203],[38],[40],[2215,2216,2217,2218,2219,2220,2221],"LeGO-LOAM Extraction","LeGO-LOAM Mapping","LeGO-LOAM Odometry","LeGO-LOAM Segmentation","NDT-LOAM DLO","NDT-LOAM LFA-Extraction","NDT-LOAM LFA-Mapping",[395,2168],{"slug":2224,"sourceId":2168,"sourceLabel":2169,"sourceYear":306,"table":2225,"note":2226,"datasets":2227,"metrics":2228,"families":2229,"methods":2230,"methodIds":2235,"rows":356,"failures":30},"ndtloam2022-text-sec-iv-a","Text Sec. IV-A","KITTI 00-10 average odometry error of the direct odometry under two matching methods and two keyframe strategies (NDT grid 1 m; Scan2Key thresholds 10…",[469],[438],[441],[2231,2232,2233,2234],"NDT-LOAM direct odometry, classic NDT + Scan2Key","NDT-LOAM direct odometry, classic NDT + Scan2Scan","NDT-LOAM direct odometry, wNDT + Scan2Key","NDT-LOAM direct odometry, wNDT + Scan2Scan",[2168],{"slug":2237,"sourceId":1392,"sourceLabel":2238,"sourceYear":374,"table":91,"note":2239,"datasets":2240,"metrics":2241,"families":2242,"methods":2243,"methodIds":2251,"rows":2252,"failures":30},"dlio2023-table-i","Chen et al., 2023","Original Newer College dataset, Ouster LiDAR 10 Hz with Ouster IMU 100 Hz, evaluated with evo; default parameters except extrinsics; LIO-SAM loop clos…",[1378],[568,38],[25,40],[2244,2245,2246,2247,2248,2249,2250],"CT-ICP [9]","DLIO (Continuous): full proposed correction","DLIO (Discrete): nearest IMU integration only","DLIO (None): no motion correction","DLO [20]","FAST-LIO2 [6]","LIO-SAM [4]",[596,1392,2088,321,338],42,{"slug":2254,"sourceId":1392,"sourceLabel":2238,"sourceYear":374,"table":325,"note":2255,"datasets":2256,"metrics":2258,"families":2259,"methods":2260,"methodIds":2261,"rows":2262,"failures":30},"dlio2023-table-ii","Self-collected UCLA campus data, hand-carried aerial platform with Ouster OS1 (10 Hz, 32 channels, 512 horizontal) and InvenSense MPU-6050; no ground…",[2257],"UCLA Campus (self-collected)",[1551,38],[40,1553],[2244,1436,2248,2249,2250],[596,1392,2088,321,338],40,{"slug":2264,"sourceId":2265,"sourceLabel":2266,"sourceYear":2267,"table":325,"note":2268,"datasets":2269,"metrics":2274,"families":2275,"methods":2276,"methodIds":2279,"rows":598,"failures":63},"iglio2024-table-ii","iglio2024","Chen et al., 2024",2024,"Average processing time per scan (ms); only 6 of 20 sequences kept (nclt_1, ncd_1, ulhk_1, bg_1, avia_1 and the 100 Hz avia_2; bg_1*, bg_2*, avia_3, g…",[2270,2271,2272,310,2273],"AVIA","BG","NCD","ULHK",[38],[40],[1436,316,317,2277,2278],"iG-LIO","iG-LIO* (kd-tree surface covariance variant, ablation)",[1392,304,321,2265],{"slug":2281,"sourceId":2265,"sourceLabel":2266,"sourceYear":2267,"table":279,"note":2282,"datasets":2283,"metrics":2286,"families":2287,"methods":2288,"methodIds":2290,"rows":1991,"failures":63},"iglio2024-table-iii","Absolute pose error (RMSE, m); identical iG-LIO parameters for all sequences; BG sequences evaluated with origin alignment, others with SE(3) alignmen…",[2284,310,2285,311],"Botanic Garden (BG)","Newer College (NCD)",[568],[25],[1436,316,317,2289,2277,2278],"NDT-LIO (ablation)",[1392,304,321,2265],{"slug":2292,"sourceId":2265,"sourceLabel":2266,"sourceYear":2267,"table":731,"note":2293,"datasets":2294,"metrics":2296,"families":2297,"methods":2298,"methodIds":2299,"rows":1042,"failures":29},"iglio2024-table-iv","End-to-end drift (m) for loops starting and ending at the same place; no ground truth available for AVIA and GDUT",[2270,2295],"GDUT (self-collected)",[1551],[1553],[316,317,2289,2277,2278],[304,321,2265],{"slug":2301,"sourceId":2302,"sourceLabel":2303,"sourceYear":562,"table":563,"note":2304,"datasets":2305,"metrics":2307,"families":2308,"methods":2309,"methodIds":2312,"rows":63,"failures":30},"chen2025quadrupedinspection-table-10","chen2025quadrupedinspection","Chen et al., 2025a","3D object detection on SUN RGB-D test half; volume IoU > 0.25; per-class values omitted",[2306],"SUN RGB-D",[23],[23],[2310,2311],"ImVoteNet, 3D + 2D input","ImVoteNet, 3D only input",[],{"slug":2314,"sourceId":2302,"sourceLabel":2303,"sourceYear":562,"table":1072,"note":2315,"datasets":2316,"metrics":2318,"families":2319,"methods":2320,"methodIds":2322,"rows":154,"failures":30},"chen2025quadrupedinspection-table-6","Trajectory statistics of the inspection trip; localization runs on the robot's onboard processors",[2317],"authors' HKUST corridor recording (ROS bag)",[38],[40],[2321],"Proposed multi-sensor localization",[2302],{"slug":2324,"sourceId":2302,"sourceLabel":2303,"sourceYear":562,"table":621,"note":2325,"datasets":2326,"metrics":2327,"families":2328,"methods":2329,"methodIds":2335,"rows":2337,"failures":154},"chen2025quadrupedinspection-table-7","Localization RMSE computed with EVO against a reference built from loop closure plus scale alignment to the TLS cloud; one 92.77 m quadruped trip (Go1…",[2317],[568,23],[25,23],[2330,2331,2332,2333,2334],"Hector SLAM [Laser]","ORB-SLAM2 [RGB x 2]","Proposed [RGB + IMU + Laser]","RTAB-Map [RGB-D]","VINS-Mono (baseline) [RGB + IMU]",[2302,2336,1511,241,251],"hector2011",17,{"slug":2339,"sourceId":2302,"sourceLabel":2303,"sourceYear":562,"table":633,"note":2340,"datasets":2341,"metrics":2342,"families":2344,"methods":2345,"methodIds":2348,"rows":356,"failures":30},"chen2025quadrupedinspection-table-8","Reconstructed RGB-D point cloud vs Leica BLK360 TLS reference (10 stations, about 500 m2) after ICP; distance is the one-directional mean nearest-neig…",[2317],[2343],"chamfer",[78],[2346,2347],"Proposed localization + surfel mapping","RTAB-Map",[2302,241],{"slug":2350,"sourceId":2302,"sourceLabel":2303,"sourceYear":562,"table":644,"note":2351,"datasets":2352,"metrics":2354,"families":2355,"methods":2356,"methodIds":2358,"rows":154,"failures":30},"chen2025quadrupedinspection-table-9","2D door detection (YOLOv5m) on the DoorDetect test split (200 images); IoU > 0.5; per-class values omitted",[2353],"DoorDetect",[23],[23],[2357],"YOLOv5m (2D inspection module)",[],{"slug":2360,"sourceId":2302,"sourceLabel":2303,"sourceYear":562,"table":2361,"note":2362,"datasets":2363,"metrics":2364,"families":2365,"methods":2366,"methodIds":2367,"rows":154,"failures":30},"chen2025quadrupedinspection-text-sec-4-2-1","Text Sec. 4.2.1","Localization output rate stated in text",[2317],[148],[40],[2321],[2302],{"slug":2369,"sourceId":2370,"sourceLabel":2371,"sourceYear":562,"table":244,"note":2372,"datasets":2373,"metrics":2375,"families":2376,"methods":2377,"methodIds":2383,"rows":1925,"failures":2388},"chen2025geode-table-5","chen2025geode","Chen et al., 2025b","ATE (m) per sequence, average of five runs, parameters not tuned per sequence; X = breakdown or error > 100 m; - = algorithm not adapted to this data.…",[2374],"GEODE",[329],[25],[2378,2379,1436,1437,811,2380,2381,2382],"COIN-LIO","Coco-LIC","LVI-SAM","R3LIVE","VINS-Fusion",[2384,2385,1392,321,815,2386,2387,1513],"cocolic2023","coinlio2024","lvisam2021","r3live2022",33,{"slug":2390,"sourceId":2370,"sourceLabel":2371,"sourceYear":562,"table":2391,"note":2392,"datasets":2393,"metrics":2394,"families":2395,"methods":2396,"methodIds":2397,"rows":274,"failures":30},"chen2025geode-text-sec-5-2","Text Sec.5.2","Flat ground is not in Table 5; text says only VINS-Fusion localized, all other methods drifted (no values given).",[2374],[329],[25],[811,2382],[815,1513],{"slug":2399,"sourceId":2400,"sourceLabel":2401,"sourceYear":68,"table":108,"note":2402,"datasets":2403,"metrics":2410,"families":2412,"methods":2413,"methodIds":2419,"rows":2420,"failures":274},"chen2026lnconstructionrobots-table-2","chen2026lnconstructionrobots","Chen et al., 2026","Secondary compilation of localization accuracy reported by cited deployments, with the review's ground-truth and environment labels; not re-verified",[2404,2405,2406,2407,2408,2409],"EuRoC MAV (cited study ref. [77])","cited study ref. [43] (Feng et al. 2025)","cited study ref. [58]","cited study ref. [76]","cited study ref. [78]","cited study ref. [79]",[568,22,2411,23],"RPE_rot",[25,23,332],[2414,2415,2416,334,2417,2418],"BIM + point cloud registration","IR camera + reflective markers","Improved VIO","RGB-D camera + AprilTag","Visual-inertial SLAM",[338],11,{"slug":2422,"sourceId":2423,"sourceLabel":2424,"sourceYear":681,"table":2425,"note":2426,"datasets":2427,"metrics":2431,"families":2432,"methods":2433,"methodIds":2434,"rows":800,"failures":30},"choi2015robustrecon-supp-table-1","choi2015robustrecon","Choi et al., 2015","Supp. Table 1","Total running time of all pipeline steps (fragment creation, geometric registration, robust optimization, ICP refinement, integration) per sequence",[2428,2429,2430],"SUN3D","augmented ICL-NUIM (synthetic, realistic noise, full-scan trajectories)","authors' apartment sequence",[23],[23],[81],[2423],{"slug":2436,"sourceId":2423,"sourceLabel":2424,"sourceYear":681,"table":2437,"note":2438,"datasets":2439,"metrics":2440,"families":2441,"methods":2442,"methodIds":2447,"rows":1042,"failures":30},"choi2015robustrecon-supp-table-3","Supp. Table 3","Median distance of each reconstructed model to the ground-truth surface (supplementary Appendix E)",[2429],[75],[78],[2443,2444,2445,81,2446],"DVO SLAM [34]","GT trajectory (input depth fused along ground truth, reference)","Kintinuous [61]","SUN3D SfM [65]",[2423,2448],"kintinuous2015",{"slug":2450,"sourceId":2423,"sourceLabel":2424,"sourceYear":681,"table":2451,"note":2452,"datasets":2453,"metrics":2454,"families":2455,"methods":2456,"methodIds":2457,"rows":29,"failures":30},"choi2015robustrecon-supp-table-4","Supp. Table 4","Accuracy of estimated camera trajectories using the RMSE metric of Handa et al. (supplementary Appendix E); the supplementary table prints no unit, me…",[2429],[568],[25],[2443,2445,81,2446],[2423,2448],{"slug":2459,"sourceId":2423,"sourceLabel":2424,"sourceYear":681,"table":108,"note":2460,"datasets":2461,"metrics":2462,"families":2463,"methods":2464,"methodIds":2467,"rows":29,"failures":30},"choi2015robustrecon-table-2","Loop-closure recall of fragment pairs (ground-truth loop = more than 30% overlap; true positive if ground-truth correspondence RMSE below 0.2 m)",[2429],[23],[23],[2465,2466],"After line-process pruning (Ours)","Geometric registration candidates before pruning",[2423],{"slug":2469,"sourceId":2423,"sourceLabel":2424,"sourceYear":681,"table":33,"note":2470,"datasets":2471,"metrics":2472,"families":2473,"methods":2474,"methodIds":2475,"rows":1042,"failures":30},"choi2015robustrecon-table-4","Mean distance of each reconstructed model to the ground-truth surface; 'GT trajectory' fuses the noisy input along the ground-truth trajectory",[2429],[75],[78],[2443,2444,2445,81,2446],[2423,2448],{"slug":2477,"sourceId":2423,"sourceLabel":2424,"sourceYear":681,"table":244,"note":2478,"datasets":2479,"metrics":2480,"families":2481,"methods":2482,"methodIds":2488,"rows":1042,"failures":30},"choi2015robustrecon-table-5","Controlled substitution of pipeline components: loop detection (image-based [34] vs geometric) and robust optimization (switchable constraints SC, exp…",[2429],[75],[78],[2483,2484,2485,2486,2487],"EM [40] with geometric loop detection","EM [40] with image-based loop detection [34]","Ours (geometric loop detection + line-process optimization)","SC [57] with geometric loop detection","SC [57] with image-based loop detection [34]",[2423,2489],"sunderhauf2012switchable",{"slug":2491,"sourceId":2423,"sourceLabel":2424,"sourceYear":681,"table":621,"note":2492,"datasets":2493,"metrics":2494,"families":2495,"methods":2496,"methodIds":2498,"rows":2262,"failures":30},"choi2015robustrecon-table-7","Perceptual evaluation on real SUN3D scenes: Balanced Rank Estimation scores from 17,640 crowd-sourced pairwise comparisons (Amazon Mechanical Turk), r…",[2428],[23],[23],[2443,2445,81,2446,2497],"SUN3D manual (manually assisted reconstructions)",[2423,2448],{"slug":2500,"sourceId":2501,"sourceLabel":2502,"sourceYear":68,"table":69,"note":2503,"datasets":2504,"metrics":2506,"families":2507,"methods":2508,"methodIds":2511,"rows":641,"failures":30},"chowdhury2026-gema-table-1","chowdhury2026_gema","Chowdhury et al., 2026","Image-loss metrics of rendered 2DGS scene and planar SSIM of meshes; inputs resized to 938x512; ground-truth mesh is the RENSA model",[2505],"authors' drone videos of residential buildings",[23],[23],[2509,2510],"GEMA","post-GEMA",[2501],{"slug":2513,"sourceId":2501,"sourceLabel":2502,"sourceYear":68,"table":108,"note":2514,"datasets":2515,"metrics":2516,"families":2517,"methods":2518,"methodIds":2520,"rows":120,"failures":30},"chowdhury2026-gema-table-2","MiDaS-based densified cloud compared with cropped COLMAP sparse cloud; F1 threshold one fifth of building height; timing on RTX 3060",[2505],[74,23],[78,23],[2519],"COLMAP densification with MiDaS depth",[2501],{"slug":2522,"sourceId":2501,"sourceLabel":2502,"sourceYear":68,"table":17,"note":2523,"datasets":2524,"metrics":2525,"families":2527,"methods":2528,"methodIds":2530,"rows":598,"failures":30},"chowdhury2026-gema-table-3","GEMA model vs ground truth (authors' RENSA LoD3 model) for single-detached houses; percentage differences as printed",[2505],[1819,23,2526],"volume_error",[23,1821],[2509,2529],"Ground truth (RENSA model)",[2501],{"slug":2532,"sourceId":2501,"sourceLabel":2502,"sourceYear":68,"table":33,"note":2533,"datasets":2534,"metrics":2535,"families":2536,"methods":2537,"methodIds":2538,"rows":641,"failures":30},"chowdhury2026-gema-table-4","post-GEMA output vs 'ground truth image' dimensions (source of these reference dimensions not described) for other typologies",[2505],[1819,23,2526],[23,1821],[2510],[2501],{"slug":2540,"sourceId":2501,"sourceLabel":2502,"sourceYear":68,"table":244,"note":2541,"datasets":2542,"metrics":2543,"families":2544,"methods":2545,"methodIds":2550,"rows":2551,"failures":154},"chowdhury2026-gema-table-5","Sensitivity to input resolution with (Y) or without (N) densification; energy is annual consumption; N\u002FA means simulation failed without manual mesh i…",[2505],[23],[23],[2546,2547,2548,2549,2529],"GEMA 480x270, densification N","GEMA 480x270, densification Y","GEMA 960x540, densification N","GEMA 960x540, densification Y",[2501],26,{"slug":2553,"sourceId":2501,"sourceLabel":2502,"sourceYear":68,"table":2554,"note":2555,"datasets":2556,"metrics":2557,"families":2558,"methods":2559,"methodIds":2560,"rows":52,"failures":30},"chowdhury2026-gema-text-sec-4-2-1","Text Sec.4.2.1","Hourly CV(RMSE) of post-GEMA EnergyPlus simulation vs ground-truth (RENSA model) simulation, Charlottetown TMY; ASHRAE limit 30%",[2505],[23],[23],[2510],[2501],{"slug":2562,"sourceId":2501,"sourceLabel":2502,"sourceYear":68,"table":2563,"note":2564,"datasets":2565,"metrics":2566,"families":2567,"methods":2568,"methodIds":2571,"rows":63,"failures":30},"chowdhury2026-gema-text-sec-4-2-3","Text Sec.4.2.3","Runtime example: 150-frame video, capped at one million 2DGS primitives, 15k iterations",[2505],[23],[23],[2569,2570],"Dense COLMAP","GEMA (2DGS)",[2501],{"slug":2573,"sourceId":2574,"sourceLabel":2575,"sourceYear":16,"table":69,"note":2576,"datasets":2577,"metrics":2580,"families":2581,"methods":2582,"methodIds":2597,"rows":2252,"failures":274},"choy2019fcgf-table-1","choy2019fcgf","Choy et al., 2019","Feature-match recall at tau1 = 0.1 m, tau2 = 0.05 on 3DMatch, and on rotation-augmented 3DMatch; time per feature includes preprocessing",[2578,2579],"3DMatch","3DMatch with rotation augmentation",[23],[23],[2583,2584,2585,2586,2587,2588,2589,2590,2591,2592,2593,2594,2595,2596],"3DMatch [36]","CGF [17]","CapsuleNet [37]","DirectReg [8]","FPFH [23]","Folding [33]","Ours (FCGF)","PPF-Fold [6]","PPFNet [7]","PerfectMatch [11]","PointNet [21]","SHOT [26]","Spin [16]","USC [29]",[2574,1788],{"slug":2599,"sourceId":2574,"sourceLabel":2575,"sourceYear":16,"table":244,"note":2600,"datasets":2601,"metrics":2603,"families":2604,"methods":2605,"methodIds":2606,"rows":2607,"failures":30},"choy2019fcgf-table-5","Registration recall on the 3DMatch registration set; RANSAC with early termination; pair correct if overlap >= 30% and RMSE \u003C 0.2 m",[2602],"3DMatch registration set",[1933],[1935],[2583,2584,2587,2589,2591,2596],[2574,1788],54,{"slug":2609,"sourceId":2574,"sourceLabel":2575,"sourceYear":16,"table":1072,"note":2610,"datasets":2611,"metrics":2613,"families":2614,"methods":2615,"methodIds":2622,"rows":102,"failures":30},"choy2019fcgf-table-6","KITTI test pairs (scans at least 10 m apart, ICP-refined GPS ground truth, 555 test pairs); RANSAC on features; success if RTE \u003C 2 m and RRE \u003C 5 deg;…",[2612],"KITTI odometry (registration pairs)",[23,1933],[23,1935],[2616,2617,2618,2619,2620,2621],"3DFeat [34]","FCGF 20cm","FCGF 25cm","FCGF 30cm","FCGF 35cm","FCGF 40cm",[2574],{"slug":2624,"sourceId":2574,"sourceLabel":2575,"sourceYear":16,"table":2625,"note":2626,"datasets":2627,"metrics":2628,"families":2629,"methods":2630,"methodIds":2632,"rows":63,"failures":30},"choy2019fcgf-text-sec-6-7","Text Sec. 6.7","Average feature extraction time for a single 3DMatch fragment",[2578],[38],[40],[2631],"FCGF",[2574],{"slug":2634,"sourceId":2635,"sourceLabel":2636,"sourceYear":562,"table":17,"note":2637,"datasets":2638,"metrics":2640,"families":2641,"methods":2642,"methodIds":2644,"rows":356,"failures":30},"chung2025aspar-table-3","chung2025aspar","Chung et al., 2025","YOLOv8-OBB scaffold detector on SLAM-map BEV images; trained on 550 images from sites A-E, tested on 100 images from site F (Ouster OS0-128 data)",[2639],"ASPAR scaffold BEV dataset",[23],[23],[2643],"YOLOv8-OBB scaffold detector",[2635],{"slug":2646,"sourceId":2635,"sourceLabel":2636,"sourceYear":562,"table":33,"note":2647,"datasets":2648,"metrics":2650,"families":2651,"methods":2652,"methodIds":2654,"rows":274,"failures":30},"chung2025aspar-table-4","Scaffold registration after the initial exploration stage vs actual scaffolds, five trials at site F (per-trial rows omitted)",[2649],"authors' ASPAR field trials",[23],[23],[2653],"ASPAR initial exploration + detection",[2635],{"slug":2656,"sourceId":2635,"sourceLabel":2636,"sourceYear":562,"table":244,"note":2657,"datasets":2658,"metrics":2659,"families":2660,"methods":2661,"methodIds":2669,"rows":429,"failures":30},"chung2025aspar-table-5","Coverage rate = scaffold voxels (0.05 m) collected \u002F mean voxels of the manual trials x 100 (Eq. 8); scan counts and times per run; termination coeffi…",[2649],[76,23],[78,23],[2662,2663,2664,2665,2666,2667,2668],"Automated trial 1 (n = 0.5 %)","Automated trial 2 (n = 1 %)","Automated trial 3 (n = 3 %)","Automated trial 4 (n = 5 %)","Automated trial 5 (n = 10 %)","Manual trial 1 (Operator A)","Manual trial 2 (Operator B)",[2635],{"slug":2671,"sourceId":2635,"sourceLabel":2636,"sourceYear":562,"table":1072,"note":2672,"datasets":2673,"metrics":2674,"families":2675,"methods":2676,"methodIds":2687,"rows":2262,"failures":30},"chung2025aspar-table-6","Coverage rate = scaffold voxels (0.05 m) collected \u002F mean voxels of the manual trials x 100 (Eq. 8); scan counts and times per run; n = 1 %; three ope…",[2649],[76,23],[78,23],[2677,2678,2679,2680,2681,2682,2683,2684,2685,2686],"Automated trial 10","Automated trial 6","Automated trial 7","Automated trial 8","Automated trial 9","Automated trials: Average","Manual trial 3 (Operator A)","Manual trial 4 (Operator B)","Manual trial 5 (Operator C)","Manual trials: Average",[2635],{"slug":2689,"sourceId":2635,"sourceLabel":2636,"sourceYear":562,"table":621,"note":2690,"datasets":2691,"metrics":2692,"families":2693,"methods":2694,"methodIds":2697,"rows":618,"failures":30},"chung2025aspar-table-7","Coverage rate = scaffold voxels (0.05 m) collected \u002F mean voxels of the manual trials x 100 (Eq. 8); scan counts and times per run; n = 1 %; Table val…",[2649],[76,23],[78,23],[2695,2696],"Automated trial 11 (n = 1 %)","Manual trial 6 (Operator C)",[2635],{"slug":2699,"sourceId":2700,"sourceLabel":2701,"sourceYear":2702,"table":69,"note":2703,"datasets":2704,"metrics":2706,"families":2707,"methods":2708,"methodIds":2710,"rows":274,"failures":30},"cignoni1998metro-table-1","cignoni1998metro","Cignoni et al., 1998",1998,"Metro v.2 run time per mesh pair; S1 and S2 face counts, sampling step, sample count and faces tested per sample as listed.",[2705],"not_reported (three mesh pairs)",[23],[23],[2709],"Metro v.2",[2700],{"slug":2712,"sourceId":2700,"sourceLabel":2701,"sourceYear":2702,"table":2713,"note":2714,"datasets":2715,"metrics":2716,"families":2717,"methods":2718,"methodIds":2720,"rows":63,"failures":30},"cignoni1998metro-text-sec-4","Text Sec.4","Error measured by Metro on meshes simplified with Simplification Envelopes under a target error bound; units not stated.",[2070],[23],[23],[2719],"Simplification Envelopes (evaluated with Metro)",[],{"slug":2722,"sourceId":2723,"sourceLabel":2724,"sourceYear":306,"table":279,"note":2725,"datasets":2726,"metrics":2727,"families":2728,"methods":2729,"methodIds":2732,"rows":415,"failures":30},"cioffi2022ctvsdt-table-iii","cioffi2022ctvsdt","Cioffi et al., 2022","EuRoC V sequences in hardware-in-the-loop simulation: simulated GPS (10 Hz, 0.1 m noise), right camera only, camera stream delayed by 0, 10 or 20 ms;…",[743],[568,23],[25,23],[2730,2731],"Continuous-time (B-spline order 6, 10 Hz control nodes)","Discrete-time",[],{"slug":2734,"sourceId":2723,"sourceLabel":2724,"sourceYear":306,"table":731,"note":2735,"datasets":2736,"metrics":2737,"families":2738,"methods":2739,"methodIds":2744,"rows":721,"failures":30},"cioffi2022ctvsdt-table-iv","Full-batch optimization time (time for the solver to converge) on EuRoC V sequences; CT uses order-6 B-splines with 1, 2, 10, 20 or 100 Hz control nod…",[743],[23],[23],[2740,2730,2741,2742,2743,2731],"Continuous-time (B-spline order 6, 1 Hz control nodes)","Continuous-time (B-spline order 6, 100 Hz control nodes)","Continuous-time (B-spline order 6, 2 Hz control nodes)","Continuous-time (B-spline order 6, 20 Hz control nodes)",[],{"slug":2746,"sourceId":2723,"sourceLabel":2724,"sourceYear":306,"table":838,"note":2747,"datasets":2748,"metrics":2750,"families":2751,"methods":2752,"methodIds":2753,"rows":224,"failures":30},"cioffi2022ctvsdt-table-vii","Outdoor flying robot with time-synchronized VI sensor (left camera used) and 5 Hz GPS; Leica total station ground truth; CT uses order-6 B-splines wit…",[2749],"outdoor flying robot dataset of Surber et al. (paper ref. [27])",[568,23],[25,23],[2730,2731],[],{"slug":2755,"sourceId":2723,"sourceLabel":2724,"sourceYear":306,"table":852,"note":2756,"datasets":2757,"metrics":2759,"families":2760,"methods":2761,"methodIds":2768,"rows":578,"failures":30},"cioffi2022ctvsdt-table-viii","Outdoor ground robot with time-synchronized VI sensor (monocular camera and IMU) and 5 Hz GPS; RTK-GPS 3D position ground truth; CT evaluated for B-sp…",[2758],"outdoor ground robot dataset (provided by Fixposition team)",[568],[25],[2762,2763,2764,2730,2741,2743,2765,2766,2767,2731],"Continuous-time (B-spline order 5, 10 Hz control nodes)","Continuous-time (B-spline order 5, 100 Hz control nodes)","Continuous-time (B-spline order 5, 20 Hz control nodes)","Continuous-time (B-spline order 7, 10 Hz control nodes)","Continuous-time (B-spline order 7, 100 Hz control nodes)","Continuous-time (B-spline order 7, 20 Hz control nodes)",[],{"slug":2770,"sourceId":2723,"sourceLabel":2724,"sourceYear":306,"table":1208,"note":2771,"datasets":2772,"metrics":2773,"families":2774,"methods":2775,"methodIds":2776,"rows":356,"failures":30},"cioffi2022ctvsdt-text-sec-iv-c","Time offsets estimated on the outdoor ground-robot trajectory; CT with order-6 B-spline and 10 Hz control nodes",[2758],[23],[23],[2730,2731],[],{"slug":2778,"sourceId":2779,"sourceLabel":2780,"sourceYear":2781,"table":2782,"note":2783,"datasets":2784,"metrics":2786,"families":2787,"methods":2788,"methodIds":2790,"rows":154,"failures":30},"cole-newman2006-3dslam-text-sec-vii","cole_newman2006_3dslam","Cole & Newman, 2006",2006,"Text Sec. VII","Vehicle driven around the exterior of a medium-sized building on a smooth but non-flat surface; discrepancy between the first and last pose immediatel…",[2785],"authors' oscillating-SICK data",[1551],[1553],[2789],"delayed-state EKF 3D SLAM before loop closure",[2779],{"slug":2792,"sourceId":2793,"sourceLabel":2794,"sourceYear":374,"table":325,"note":2795,"datasets":2796,"metrics":2798,"families":2799,"methods":2800,"methodIds":2812,"rows":2813,"failures":30},"maplab2-2023-table-ii","maplab2_2023","Cramariuc et al., 2023","HILTI 2021 SLAM Dataset, RMSE of APE; seven baselines and four maplab 2.0 configurations (ROVIO + SIFT, OKVIS + SP + B, OKVIS + SP + B + ICP, FAST-LIO…",[2797],"HILTI 2021 SLAM Dataset",[329,23],[25,23],[2801,2802,2803,2804,2805,2806,2807,2808,2809,2810,2811],"FAST LIO2","LVI SAM","OKVIS","ORB SLAM3","ROVIO","RTAB Map","maplab","maplab 2.0: FAST-LIO2 + SP + B","maplab 2.0: OKVIS + SP + B","maplab 2.0: OKVIS + SP + B + ICP","maplab 2.0: ROVIO + SIFT",[321,2386,2793,1510,763,241],110,{"slug":2815,"sourceId":2793,"sourceLabel":2794,"sourceYear":374,"table":1193,"note":2816,"datasets":2817,"metrics":2818,"families":2819,"methods":2820,"methodIds":2823,"rows":356,"failures":30},"maplab2-2023-text-sec-iv-b","EuRoC 11 sequences with ROVIO and BRISK: parallel multi-robot mapping server vs sequential multi-session console workflow",[743],[329,23],[25,23],[2821,2822],"maplab 2.0 mapping server (parallel, 11 missions)","maplab 2.0 sequential multi-session (mapping node + console)",[2793],{"slug":2825,"sourceId":2826,"sourceLabel":2827,"sourceYear":2828,"table":2829,"note":2830,"datasets":2831,"metrics":2834,"families":2835,"methods":2836,"methodIds":2841,"rows":224,"failures":30},"curless1996volumetric-fig-8","curless1996volumetric","Curless & Levoy, 1996",1996,"Fig. 8","Reconstruction statistics with and without space carving and hole filling; Dragon voxel 0.35 mm (712x501x322), Buddha voxel 0.25 mm (407x957x407)",[2832,2833],"Dragon (authors' scans)","Happy Buddha (authors' scans)",[23],[23],[2837,2838,2839,2840],"Buddha (volumetric integration, no hole filling)","Buddha + fill (space carving and hole filling)","Dragon (volumetric integration, no hole filling)","Dragon + fill (space carving and hole filling)",[2826],{"slug":2843,"sourceId":2826,"sourceLabel":2827,"sourceYear":2828,"table":668,"note":2844,"datasets":2845,"metrics":2846,"families":2847,"methods":2848,"methodIds":2850,"rows":63,"failures":63},"curless1996volumetric-text-sec-6","RMS distance between points in the original (pre-aligned) range images and the reconstructed surface, stated as approximately 0.1 mm for both models;…",[2832,2833],[23],[23],[2849],"volumetric method",[2826],{"slug":2852,"sourceId":2853,"sourceLabel":2854,"sourceYear":213,"table":91,"note":2855,"datasets":2856,"metrics":2858,"families":2859,"methods":2860,"methodIds":2865,"rows":721,"failures":30},"deepfactors2020-table-i","deepfactors2020","Czarnowski et al., 2020","Factor ablation on shortened ScanNet validation scenes: pho = photometric, rep = reprojection, geo = sparse geometric",[2857],"ScanNet (validation)",[568,23],[25,23],[2861,2862,2863,2864],"DeepFactors (combined)","DeepFactors (pho)","DeepFactors (pho+geo)","DeepFactors (pho+rep)",[],{"slug":2867,"sourceId":2853,"sourceLabel":2854,"sourceYear":213,"table":325,"note":2868,"datasets":2869,"metrics":2873,"families":2874,"methods":2875,"methodIds":2880,"rows":2882,"failures":30},"deepfactors2020-table-ii","Percentage of key-frame pixels with depth within 10% of ground truth; DeepFactors trajectory and depth scaled by the optimal scale from the TUM script…",[2870,2871,2872],"ICL-NUIM","ICL-NUIM and TUM RGB-D","TUM RGB-D",[23],[23],[2876,2877,2878,2879],"CNN-SLAM [ 18 ]","DeepFactors","LSD-BS [ 19 ]","Laina [ 37 ]",[2881],"lsdslam2014",32,{"slug":2884,"sourceId":2853,"sourceLabel":2854,"sourceYear":213,"table":279,"note":2885,"datasets":2886,"metrics":2887,"families":2888,"methods":2889,"methodIds":2894,"rows":1042,"failures":30},"deepfactors2020-table-iii","TUM RGB-D fr1 validation sequences, ATE RMSE; CNN-SLAM without pose-graph optimization and DeepTAM values from the DeepTAM paper; CodeSLAM run by the…",[2872],[568],[25],[2890,2891,2892,2893],"CNN-SLAM","CodeSLAM (not real-time)","DeepFactors (no loop closure, no geometric factor)","DeepTAM (not real-time)",[],{"slug":2896,"sourceId":2897,"sourceLabel":2898,"sourceYear":345,"table":17,"note":2899,"datasets":2900,"metrics":2901,"families":2902,"methods":2903,"methodIds":2914,"rows":2262,"failures":30},"bundlefusion2017-table-3","bundlefusion2017","Dai et al., 2017a","ICL-NUIM living-room trajectories kt0 to kt3 with synthetic noise; ATE RMSE; comparator values match those printed in ElasticFusion Table II; Ours (s)…",[2870],[568],[25],[2904,2905,2906,2907,2908,2909,2910,2911,2912,2913],"BundleFusion (Ours)","BundleFusion ablation: Ours (s), sparse only","BundleFusion ablation: Ours (sd), sparse and local dense","DVO SLAM","Elastic Fusion","Kintinuous","MRSMap","RGB-D SLAM","Redwood (rigid)","VoxelHashing",[2897,2915,2448,2916],"elasticfusion2015","voxelhashing2013",{"slug":2918,"sourceId":2897,"sourceLabel":2898,"sourceYear":345,"table":33,"note":2919,"datasets":2920,"metrics":2921,"families":2922,"methods":2923,"methodIds":2926,"rows":1069,"failures":416},"bundlefusion2017-table-4","TUM RGB-D ATE RMSE; ground truth from a calibrated motion capture system for hand-held Kinect sequences; for Kinect data the dense reprojection thresh…",[2872],[568],[25],[2904,2905,2906,2907,2908,2909,2924,2910,2911,2912,2925,2913],"LSD-SLAM","Submap BA",[2897,2915,2448,2881,2916],{"slug":2928,"sourceId":2897,"sourceLabel":2898,"sourceYear":345,"table":1072,"note":2929,"datasets":2930,"metrics":2931,"families":2932,"methods":2933,"methodIds":2934,"rows":429,"failures":30},"bundlefusion2017-table-6","Surface reconstruction accuracy on ICL-NUIM living room: mean distance of each reconstructed model to the ground-truth surface; Kintinuous kt3 is prin…",[2870],[75],[78],[2904,2907,2908,2909,2910,2911,2912],[2897,2915,2448],{"slug":2936,"sourceId":2897,"sourceLabel":2898,"sourceYear":345,"table":621,"note":2937,"datasets":2938,"metrics":2940,"families":2941,"methods":2942,"methodIds":2945,"rows":1042,"failures":30},"bundlefusion2017-table-7","Augmented ICL-NUIM (Choi et al.) with longer trajectories and more loops, synthetic sensor noise, reported intrinsics; ATE RMSE; Redwood offline and g…",[2939],"Augmented ICL-NUIM",[568],[25],[2904,2907,2909,2943,2944],"Redwood","SUN3D SfM",[2897,2448],{"slug":2947,"sourceId":2897,"sourceLabel":2898,"sourceYear":345,"table":2948,"note":2949,"datasets":2950,"metrics":2952,"families":2953,"methods":2954,"methodIds":2955,"rows":416,"failures":30},"bundlefusion2017-text-sec-6-memory","Text Sec. 6 Memory","Memory stated in text and Table 1: CPU RAM holds all RGB-D frames and grows linearly with sequence length; GPU memory for TSDF and optimisation",[2951,2428],"BundleFusion captured sequences",[219],[40],[2904],[2897],{"slug":2957,"sourceId":2958,"sourceLabel":2959,"sourceYear":345,"table":2960,"note":2961,"datasets":2962,"metrics":2964,"families":2965,"methods":2966,"methodIds":2968,"rows":63,"failures":30},"dai2017scannet-text-app-a-2","dai2017scannet","Dai et al., 2017b","Text App.A.2","Reconstruction processing time per scene including data conversion, dense voxel fusion, mesh extraction, alignment, cleanup and thumbnail rendering; m…",[2963],"ScanNet",[23],[23],[2967],"ScanNet reconstruction pipeline (BundleFusion poses, VoxelHashing TSDF, marching cubes, cleanup)",[2958],{"slug":2970,"sourceId":2971,"sourceLabel":2972,"sourceYear":1694,"table":2973,"note":2974,"datasets":2975,"metrics":2977,"families":2978,"methods":2979,"methodIds":2981,"rows":224,"failures":30},"monoslam2007-table-in-sec-6-1","monoslam2007","Davison et al., 2007","Table in Sec. 6.1","Ground-truth characterisation on a desktop track: mean MonoSLAM camera position over several looped revisits (std in brackets) at four waypoints, hand…",[2976],"authors' desktop ground-truth track",[23],[23],[2980],"MonoSLAM",[2971],{"slug":2983,"sourceId":2971,"sourceLabel":2972,"sourceYear":1694,"table":2984,"note":2985,"datasets":2986,"metrics":2987,"families":2988,"methods":2989,"methodIds":2990,"rows":120,"failures":30},"monoslam2007-text-sec-6-2","Text Sec.6.2","Typical breakdown of per-frame processing time at 30 Hz (33 ms budget) on a 1.6 GHz Pentium M",[2070],[23,38],[40,23],[2980],[2971],{"slug":2992,"sourceId":2993,"sourceLabel":2994,"sourceYear":2781,"table":2995,"note":2996,"datasets":2997,"metrics":2999,"families":3000,"methods":3001,"methodIds":3007,"rows":2119,"failures":30},"dellaert2006sqrtsam-fig-10-table","dellaert2006sqrtsam","Dellaert & Kaess, 2006","Fig. 10 table","Batch square-root SAM in synthetic environments; time averaged over 10 trials for trajectory length M and N landmarks",[2998],"synthetic simulation",[23],[23],[3002,3003,3004,3005,3006],"batch square-root SAM, chol (MATLAB built-in Cholesky)","batch square-root SAM, ldl (Davis sparse LDL)","batch square-root SAM, mfqr (multifrontal QR)","batch square-root SAM, qr (MATLAB built-in QR)","none (no factorization; measures overhead)",[2993],{"slug":3009,"sourceId":2993,"sourceLabel":2994,"sourceYear":2781,"table":3010,"note":3011,"datasets":3012,"metrics":3013,"families":3014,"methods":3015,"methodIds":3020,"rows":356,"failures":30},"dellaert2006sqrtsam-text-figs-11-13","Text Figs.11-13","Synthetic 1000-step random walk in a 500-landmark Manhattan world (Fig. 9): non-zeros in the Cholesky factor R for different column orderings, compare…",[2998],[23],[23],[3016,3017,3018,3019],"EKF filtering covariance matrix (entries)","XL ordering (states then landmarks)","block-structured colamd ordering","colamd ordering",[2993],{"slug":3022,"sourceId":2993,"sourceLabel":2994,"sourceYear":2781,"table":3023,"note":3024,"datasets":3025,"metrics":3026,"families":3027,"methods":3028,"methodIds":3031,"rows":274,"failures":154},"dellaert2006sqrtsam-text-sec-7-2","Text Sec.7.2","Incremental square-root SAM versus a standard EKF, 500 time steps in a synthetic environment with 2000 landmarks (sparse LDL with symamd ordering)",[2998],[23],[23],[3029,3030],"incremental square-root SAM (LDL)","standard EKF",[2993],{"slug":3033,"sourceId":2993,"sourceLabel":2994,"sourceYear":2781,"table":3034,"note":3035,"datasets":3036,"metrics":3038,"families":3039,"methods":3040,"methodIds":3043,"rows":356,"failures":30},"dellaert2006sqrtsam-text-sec-8","Text Sec.8","Real indoor office sequence: ATRV-Mini with eight cameras and odometry, 260 joint images, about 190 m trajectory; batch square-root SAM every three jo…",[3037],"authors' office sequence",[23],[23],[3041,3042],"block-structured ordering heuristic","incremental (repeated batch) square-root SAM",[2993],{"slug":3045,"sourceId":3046,"sourceLabel":3047,"sourceYear":345,"table":3048,"note":3049,"datasets":3050,"metrics":3052,"families":3053,"methods":3054,"methodIds":3057,"rows":63,"failures":30},"dellaert2017fg-text-sec-4-1","dellaert2017fg","Dellaert & Kaess, 2017","Text Sec.4.1","Flop count of eliminating the five-variable toy SLAM example (Fig. 1.3) with ordering l1, l2, x1, x2, x3, assuming two measurement rows per landmark",[3051],"toy SLAM example (Fig. 1.3)",[23],[23],[3055,3056],"dense QR (same ordering)","sparse multifrontal QR",[],{"slug":3059,"sourceId":3046,"sourceLabel":3047,"sourceYear":345,"table":3060,"note":3061,"datasets":3062,"metrics":3064,"families":3065,"methods":3066,"methodIds":3069,"rows":63,"failures":30},"dellaert2017fg-text-sec-4-2","Text Sec.4.2","Non-zeros of the upper-triangular Cholesky factor R for the simulated 2D SLAM example of Fig. 2.1 (333 unknowns, 1126 scalar measurements) under two v…",[3063],"simulated 2D SLAM example (Fig. 2.1)",[23],[23],[3067,3068],"COLAMD ordering","natural ordering (poses first, then landmarks)",[],{"slug":3071,"sourceId":3072,"sourceLabel":3073,"sourceYear":1111,"table":1143,"note":3074,"datasets":3075,"metrics":3077,"families":3078,"methods":3079,"methodIds":3081,"rows":154,"failures":154},"dellaert2012gtsamtr-text-sec-5-3","dellaert2012gtsamtr","Dellaert, 2012","Larger planar SLAM example read from a .graph file (about 100 poses and about 30 landmarks)",[3076],"GTSAM example data (PlanarSLAMExample_graph)",[23],[23],[3080],"GTSAM nonlinear optimization",[3082],"gtsam_software",{"slug":3084,"sourceId":3085,"sourceLabel":3086,"sourceYear":698,"table":3087,"note":3088,"datasets":3089,"metrics":3091,"families":3092,"methods":3093,"methodIds":3094,"rows":63,"failures":30},"dellaert2021annrev-text-sec-3-2","dellaert2021annrev","Dellaert, 2021","Text Sec.3.2","Non-zeros of the Cholesky factor for the simulated 2D SLAM example of Fig. 3 (100 poses, 22 landmarks; 1,126 measurement rows) under two orderings; ba…",[3090],"simulated 2D SLAM example (Fig. 3)",[23],[23],[3067,3068],[],{"slug":3096,"sourceId":596,"sourceLabel":3097,"sourceYear":306,"table":91,"note":3098,"datasets":3099,"metrics":3103,"families":3104,"methods":3105,"methodIds":3110,"rows":3111,"failures":30},"cticp2022-table-i","Dellenbach et al., 2022","KITTI RTE (%) averaged over segments of 100 to 800 m, Driving profile; AVG over all segments of all sequences; one parameter set per method for all dr…",[603,3100,3101,1364,310,3102,613],"KITTI-CARLA","KITTI-corrected (motion-corrected odometry benchmark scans)","Newer College Dataset (NCD)",[438,38],[40,441],[3106,3107,3108,3109],"CT-ICP (ours)","IMLS-SLAM [15]","MULLS [4]","pyLiDAR F2M [33]",[596,597],156,{"slug":3113,"sourceId":596,"sourceLabel":3097,"sourceYear":306,"table":325,"note":3114,"datasets":3115,"metrics":3116,"families":3117,"methods":3118,"methodIds":3121,"rows":224,"failures":30},"cticp2022-table-ii","Loop closure evaluation; mean ATE (m) after the best rigid transform between ground truth and estimate; Nmap=100, Noverlap=30; Nloop=69 loops detected",[603,3100,1364,310,3102,613],[22],[25],[3119,3120],"CT-ICP odometry only (LO)","CT-ICP with loop closure and pose graph (LO+LC)",[596],{"slug":3123,"sourceId":596,"sourceLabel":3097,"sourceYear":306,"table":3124,"note":3125,"datasets":3126,"metrics":3128,"families":3129,"methods":3130,"methodIds":3132,"rows":63,"failures":30},"cticp2022-text-abstract","Text Abstract","KITTI odometry leaderboard submission on test sequences 11-21 (KITTI-corrected)",[3127],"KITTI odometry benchmark (test)",[438,38],[40,441],[3131],"CT-ICP",[596],{"slug":3134,"sourceId":596,"sourceLabel":3097,"sourceYear":306,"table":1430,"note":3135,"datasets":3136,"metrics":3137,"families":3138,"methods":3139,"methodIds":3141,"rows":63,"failures":30},"cticp2022-text-sec-v-b","Ablation: scan deskewed with a constant-velocity model before optimisation and a single pose estimated (instead of CT-ICP two-pose elastic formulation…",[603,1364],[438],[441],[3140],"CT-ICP variant: constant-velocity pre-deskew, single pose",[596],{"slug":3143,"sourceId":596,"sourceLabel":3097,"sourceYear":306,"table":3144,"note":3145,"datasets":3146,"metrics":3148,"families":3149,"methods":3150,"methodIds":3153,"rows":63,"failures":30},"cticp2022-text-sec-v-c","Text Sec.V-C","Loop closure module timing, averaged",[3147],"all datasets",[23],[23],[3151,3152],"CT-ICP back end (g2o)","CT-ICP loop detection",[596],{"slug":3155,"sourceId":3156,"sourceLabel":3157,"sourceYear":68,"table":69,"note":3158,"datasets":3159,"metrics":3160,"families":3161,"methods":3162,"methodIds":3180,"rows":2607,"failures":274},"deng2026-mcgs-slam-table-1","deng2026_mcgs_slam","Deng & Gan, 2026","TUM static scenes; ATE RMSE; the paper does not state whether baseline values were re-run or taken from the literature",[2872],[568],[25],[3163,3164,3165,3166,3167,3168,3169,3170,3171,3172,3173,3174,892,3175,3176,3177,3178,3179],"Baseline MonoGS","CG-SLAM","CVO-SLAM","Co-SLAM","DI-Fusion (reference [57] is DI-SLAM)","ESLAM","GLC-SLAM","GS-ICP-SLAM","Gaussian-SLAM","MCGS-SLAM (Ours)","MM3DGS-SLAM","NICE-SLAM","Point-SLAM","RKD-SLAM","SplaTAM","Uni-SLAM","Vox-Fusion",[3181,3156,3182,3183,3184,763,3185,3186],"coslam2023","eslam2023","monogs2024","niceslam2022","pointslam2023","splatam2024",{"slug":3188,"sourceId":3156,"sourceLabel":3157,"sourceYear":68,"table":3189,"note":3190,"datasets":3191,"metrics":3192,"families":3193,"methods":3194,"methodIds":3206,"rows":3207,"failures":120},"deng2026-mcgs-slam-table-11","Table 11","EuRoC MH01 to MH05; input modality for the 3DGS methods not stated; classical-method values match those listed in DROID-SLAM Tables 3 and 5 after roun…",[743],[568],[25],[3195,3196,3197,3198,3199,3200,3201,3202,3203,3204,3205],"Baseline MonoGS (w\u002Fo loop)","DSM (monocular, w\u002Fo loop)","DSO (monocular, w\u002Fo loop)","GI-SLAM (w\u002Fo loop)","MCGS-SLAM (Ours, w\u002Fo loop)","ORB-SLAM (monocular, with loop)","ORB-SLAM3 (stereo, with loop)","SVO (monocular, w\u002Fo loop)","SVO (stereo, w\u002Fo loop)","SplaTAM (w\u002Fo loop)","VINS-Fusion (stereo, with loop)",[3156,1507,3183,119,763,3186,1512,1513],55,{"slug":3209,"sourceId":3156,"sourceLabel":3157,"sourceYear":68,"table":108,"note":3210,"datasets":3211,"metrics":3212,"families":3213,"methods":3214,"methodIds":3218,"rows":224,"failures":30},"deng2026-mcgs-slam-table-2","TUM dynamic scene with walking people; ATE RMSE (ATE S.D. column not transcribed)",[2872],[568],[25],[3163,3166,3215,3216,3168,3170,3172,3174,892,3217,3177,3179],"DVO-SLAM","DynaSLAM","RoDyn-SLAM",[3181,3156,3182,3183,3184,763,3186],{"slug":3220,"sourceId":3156,"sourceLabel":3157,"sourceYear":68,"table":17,"note":3221,"datasets":3222,"metrics":3224,"families":3225,"methods":3226,"methodIds":3227,"rows":340,"failures":30},"deng2026-mcgs-slam-table-3","Replica pose tracking, baseline MonoGS versus MCGS-SLAM",[3223],"Replica",[568],[25],[3163,3172],[3156,3183],{"slug":3229,"sourceId":3156,"sourceLabel":3157,"sourceYear":68,"table":633,"note":3230,"datasets":3231,"metrics":3232,"families":3233,"methods":3234,"methodIds":3236,"rows":340,"failures":30},"deng2026-mcgs-slam-table-8","Replica FPS; MonoGS reproduced on the same RTX 4090 (Orbeez-SLAM, Point-SLAM and SplaTAM columns cited from GS-ICP-SLAM, not transcribed)",[3223],[148],[40],[3172,3235],"MonoGS (re-run by authors)",[3156,3183],{"slug":3238,"sourceId":3156,"sourceLabel":3157,"sourceYear":68,"table":644,"note":3239,"datasets":3240,"metrics":3241,"families":3242,"methods":3243,"methodIds":3245,"rows":120,"failures":30},"deng2026-mcgs-slam-table-9","GPU memory on TUM freiburg1_desk",[2872],[219],[40],[3163,3244,3172,3174,3175,3177],"ESLAM (labelled 'Neural Points + MLP')",[3156,3182,3183,3184,3185,3186],{"slug":3247,"sourceId":3248,"sourceLabel":3249,"sourceYear":374,"table":69,"note":3250,"datasets":3251,"metrics":3254,"families":3255,"methods":3256,"methodIds":3262,"rows":2262,"failures":30},"nerfloam2023-table-1","nerfloam2023","Deng et al., 2023","Simultaneous odometry and mapping; SHINE-Mapping and VDBFusion are fed KISS-ICP poses, NeRF-LOAM is shown with KISS-ICP poses and with its own odometr…",[3252,3253],"MaiCity","Newer College",[2343,74,75,76],[78],[3257,3258,3259,3260,3261],"Ours with KissICP poses","Ours with own odometry","Puma [36] with own odometry","SHINE [50] with KissICP poses","Vdbfusion [37] with KissICP poses",[3248,3263,3264,3265],"shinemapping2023","vizzo2021puma","vizzo2022vdbfusion",{"slug":3267,"sourceId":3248,"sourceLabel":3249,"sourceYear":374,"table":108,"note":3268,"datasets":3269,"metrics":3270,"families":3271,"methods":3272,"methodIds":3276,"rows":608,"failures":30},"nerfloam2023-table-2","Mapping quality with ground-truth poses for all methods (pure mapping ability); voxel size 20 cm; caption states F-score in % with a 10 cm threshold",[3252,3253],[2343,74,75,76],[78],[3273,3274,3275],"Ours with GT pose","SHINE [50] with GT pose","Vdbfusion [37] with GT pose",[3248,3263,3265],{"slug":3278,"sourceId":3248,"sourceLabel":3249,"sourceYear":374,"table":17,"note":3279,"datasets":3280,"metrics":3281,"families":3282,"methods":3283,"methodIds":3290,"rows":429,"failures":154},"nerfloam2023-table-3","Odometry ATE RMSE with SE(3) alignment (Sec. 5.1); '-' means failed; unit not stated; DeLORA and PWC-LONet are pre-trained on KITTI",[469,3252,3253],[568],[25],[3284,3285,3286,81,3287,3288,3289],"DeLORA [26]","GICP [31]","ICP [3] (point-to-point)","PWC-LONet [39]","Puma [36]","SuMA [2]",[478,3248,3291,3292,432,3264],"nubert2021delora","segal2009gicp",{"slug":3294,"sourceId":3248,"sourceLabel":3249,"sourceYear":374,"table":244,"note":3295,"datasets":3296,"metrics":3297,"families":3298,"methods":3299,"methodIds":3308,"rows":301,"failures":63},"nerfloam2023-table-5","Supplementary KITTI odometry, average translational (%) and rotational (deg\u002F100 m) errors over 100 to 800 m subsequences; '*' marks results on trainin…",[469],[438,439],[441],[3284,3300,3285,3301,3302,3303,81,3304,3305,3306,3307],"DeepPCO [41]","ICP-po2pl [30]","ICP-po2po [3]","LONet [15]","PUMA(NN) [36]","PUMA(RC) [36]","PWCLONet [39]","SUMA [2]",[478,3309,3248,3291,3310,3292,432,3264],"lonet2019","rusinkiewicz2001variants",{"slug":3312,"sourceId":3313,"sourceLabel":3314,"sourceYear":107,"table":91,"note":3315,"datasets":3316,"metrics":3317,"families":3318,"methods":3319,"methodIds":3322,"rows":3323,"failures":30},"imlsslam2018-table-i","imlsslam2018","Deschaud, 2018","KITTI odometry training sequences 00-10, HDL64; translation drift (%) with the KITTI metric; LOAM values copied from the LOAM Autonomous Robots paper…",[469],[438],[441],[3320,3321],"LOAM [7] (results taken from paper)","Our SLAM (IMLS-SLAM)",[3313,450],22,{"slug":3325,"sourceId":3313,"sourceLabel":3314,"sourceYear":107,"table":325,"note":3326,"datasets":3327,"metrics":3328,"families":3329,"methods":3330,"methodIds":3333,"rows":63,"failures":30},"imlsslam2018-table-ii","Ablation of object removal; drift on the whole KITTI training dataset",[469],[438],[441],[3331,3332],"IMLS-SLAM, With object removal","IMLS-SLAM, Without object removal",[3313],{"slug":3335,"sourceId":3313,"sourceLabel":3314,"sourceYear":107,"table":279,"note":3336,"datasets":3337,"metrics":3338,"families":3339,"methods":3340,"methodIds":3344,"rows":274,"failures":30},"imlsslam2018-table-iii","Ablation of sampling strategy; drift on the whole KITTI training dataset",[469],[438],[441],[3341,3342,3343],"IMLS-SLAM, Geometric stable sampling [10]","IMLS-SLAM, Our sampling","IMLS-SLAM, Random sampling",[3313],{"slug":3346,"sourceId":3313,"sourceLabel":3314,"sourceYear":107,"table":731,"note":3347,"datasets":3348,"metrics":3349,"families":3350,"methods":3351,"methodIds":3356,"rows":356,"failures":30},"imlsslam2018-table-iv","Ablation of number of scans n kept in the model; drift on the whole KITTI training dataset",[469],[438],[441],[3352,3353,3354,3355],"IMLS-SLAM, n = 1 scan","IMLS-SLAM, n = 10 scans","IMLS-SLAM, n = 100 scans","IMLS-SLAM, n = 5 scans",[3313],{"slug":3358,"sourceId":3313,"sourceLabel":3314,"sourceYear":107,"table":818,"note":3359,"datasets":3360,"metrics":3361,"families":3362,"methods":3363,"methodIds":3367,"rows":274,"failures":30},"imlsslam2018-table-v","Ablation of samples per list s; drift on the whole KITTI training dataset",[469],[438],[441],[3364,3365,3366],"IMLS-SLAM, s = 10 samples\u002Flist","IMLS-SLAM, s = 100 samples\u002Flist","IMLS-SLAM, s = 1000 samples\u002Flist",[3313],{"slug":3369,"sourceId":3313,"sourceLabel":3314,"sourceYear":107,"table":3370,"note":3371,"datasets":3372,"metrics":3374,"families":3375,"methods":3376,"methodIds":3378,"rows":63,"failures":30},"imlsslam2018-text-sec-vi-a","Text Sec. VI-A","Own Velodyne HDL32 acquisition in Paris, two 2 km loops (4 km, 12951 scans) ending at the start point; error = distance between first and last localiz…",[3373],"own HDL32 Paris dataset",[1551],[1553],[3377],"IMLS SLAM",[3313],{"slug":3380,"sourceId":3313,"sourceLabel":3314,"sourceYear":107,"table":3381,"note":3382,"datasets":3383,"metrics":3384,"families":3385,"methods":3386,"methodIds":3390,"rows":654,"failures":154},"imlsslam2018-text-sec-vi-b","Text Sec. VI-B","KITTI odometry, overall values stated in text (training set with ground truth; test set from KITTI website)",[469],[438,23],[441,23],[3387,587,3388,3389],"Ceriani et al. [6] pose interpolation SLAM","LOAM (KITTI website)","LOAM [7]",[3313,2118,450],{"slug":3392,"sourceId":3313,"sourceLabel":3314,"sourceYear":107,"table":3393,"note":3394,"datasets":3395,"metrics":3396,"families":3397,"methods":3398,"methodIds":3402,"rows":416,"failures":30},"imlsslam2018-text-sec-vi-c","Text Sec. VI-C","Processing time per scan on KITTI (normals from 3D points since raw range images are unavailable)",[469],[38],[40],[3377,3399,3400,3401,3389],"IMLS SLAM (k-d tree rebuild)","IMLS SLAM (matching)","IMLS SLAM (normal computation)",[3313,450],{"slug":3404,"sourceId":3405,"sourceLabel":3406,"sourceYear":698,"table":108,"note":3407,"datasets":3408,"metrics":3416,"families":3418,"methods":3419,"methodIds":3428,"rows":618,"failures":30},"distefano2021mobile3dscan-table-2","distefano2021mobile3dscan","Di Stefano et al., 2021","Secondary compilation: built and urban MLS accuracy from cited studies with their ground truth; values as printed",[3409,3410,3411,3412,3413,3414,3415],"cited study: Bock et al. 2015","cited study: Chiappini et al. 2020","cited study: Kaijaluoto et al. 2015","cited study: Kukko et al. 2012","cited study: Lauterbach et al. 2015","cited study: Thomson et al. 2013","cited study: Y. Yan and Hajjar 2021",[3417,75],"c2c_distance",[78],[3420,3421,3422,3423,3424,3425,3426,3427],"FARO Focus 3D X330 and FARO Focus 3D 120S (trolley)","FARO Photon 120","GeoSLAM ZEB1","Kaarta Stencil 2","RIEGL VMX-250","RIEGL VZ400 (backpack)","Velodyne VLP-16 on UAV","Viametris i-MMS",[],{"slug":3430,"sourceId":3405,"sourceLabel":3406,"sourceYear":698,"table":33,"note":3431,"datasets":3432,"metrics":3440,"families":3441,"methods":3442,"methodIds":3448,"rows":224,"failures":274},"distefano2021mobile3dscan-table-4","Secondary compilation: cultural heritage MLS accuracy from cited studies with their ground truth; values as printed",[3433,3434,3435,3436,3437,3438,3439],"cited study: Barba et al. 2019","cited study: Bronzino et al. 2019","cited study: Chiabrando et al. 2019","cited study: Di Stefano, Chiappini, et al. 2020","cited study: Farella et al. 2016","cited study: Patrucco et al. 2019","cited study: Tucci et al. 2018",[3417,75],[78],[3443,3444,3445,3446,3447],"GeoSLAM ZEB 1","GeoSLAM ZEB REVO","GeoSLAM ZEB REVO RT","KAARTA Stencil 2","Leica-Geosystem Pegasus Backpack",[],{"slug":3450,"sourceId":3405,"sourceLabel":3406,"sourceYear":698,"table":1072,"note":3451,"datasets":3452,"metrics":3456,"families":3457,"methods":3458,"methodIds":3462,"rows":416,"failures":63},"distefano2021mobile3dscan-table-6","Secondary compilation: underground MLS accuracy from cited studies with their ground truth; values as printed",[3453,3454,3455],"cited study: Chen et al. 2017","cited study: Dewez et al. 2017","cited study: Eyre et al. 2016",[3417,1819],[78,1821],[3459,3443,3460,3444,3461],"CSIRO Zebedee","GeoSLAM ZEB 40-Hz REVO","NavVis-3D",[],{"slug":3464,"sourceId":3405,"sourceLabel":3406,"sourceYear":698,"table":633,"note":3465,"datasets":3466,"metrics":3473,"families":3474,"methods":3475,"methodIds":3480,"rows":2420,"failures":30},"distefano2021mobile3dscan-table-8","Secondary compilation: environmental monitoring MLS accuracy from cited studies with their ground truth; values as printed",[3467,3468,3469,3470,3471,3472],"cited study: Di Stefano, Cabrelles, et al. 2020","cited study: Donker et al. 2018","cited study: James and Quinton 2014","cited study: Lin et al. 2019","cited study: Vaaja et al. 2011","cited study: Williams et al. 2020",[3417,75,23],[78,23],[3443,3446,3476,3477,3478,3479],"Leica-Geosystem Pegasus","RIEGL VZ-400","ROAMER AkhkaMMS with FARO Photon 120","Velodyne VLP-32C (UAV)",[],{"slug":3482,"sourceId":3483,"sourceLabel":3484,"sourceYear":107,"table":91,"note":3485,"datasets":3486,"metrics":3488,"families":3489,"methods":3490,"methodIds":3494,"rows":356,"failures":30},"droeschel2018ctslam-table-i","droeschel2018ctslam","Droeschel & Behnke, 2018","Best mean map entropy (MME, radius 0.5 m, lower is better) on a selected part of the Deutsches Museum backpack dataset, following Nuechter et al. [27]",[3487],"Deutsches Museum (Cartographer dataset)",[23],[23],[3491,3492,3493,81],"Cartographer [26]","Droeschel et al. [8] (previous method)","Nuechter et al. [27]",[2116,3483],{"slug":3496,"sourceId":3483,"sourceLabel":3484,"sourceYear":107,"table":1452,"note":3497,"datasets":3498,"metrics":3500,"families":3501,"methods":3502,"methodIds":3505,"rows":63,"failures":30},"droeschel2018ctslam-text-sec-v-a","Courtyard MAV data (16 map nodes); refinement run as post-processing, average over 10 runs",[3499],"courtyard MAV flight (own data)",[38],[40],[3503,3504],"Ours (all 16 map nodes)","Ours (single map node)",[3483],{"slug":3507,"sourceId":3508,"sourceLabel":3509,"sourceYear":2267,"table":91,"note":3510,"datasets":3511,"metrics":3515,"families":3516,"methods":3517,"methodIds":3524,"rows":415,"failures":30},"dufomap2024-table-i","dufomap2024","Duberg et al., 2024","Point-wise dynamic point removal accuracy (%) following the DynamicMap benchmark protocol; Removert, ERASOR, OctoMap and DUFOMap evaluated offline, Dy…",[3512,3513,3514],"Argoverse 2","KITTI (SemanticKITTI labels and poses)","Semi-indoor (self-collected)",[23],[23],[3518,3519,3520,3521,3522,3523],"DUFOMap (Ours)","DUFOMap* (Ours, online)","Dynablox [17]","ERASOR [9]","OctoMap [16]","Removert [8]",[3508,3525,3526,3527,3528],"dynablox2023","erasor2021","hornung2013octomap","removert2020",{"slug":3530,"sourceId":3508,"sourceLabel":3509,"sourceYear":2267,"table":325,"note":3531,"datasets":3532,"metrics":3533,"families":3534,"methods":3535,"methodIds":3536,"rows":578,"failures":30},"dufomap2024-table-ii","Run time per point cloud (s, mean plus or minus std): total processing time divided by the number of point clouds; desktop Intel Core i9-12900KF (Sec.…",[1997,3514],[38],[40],[3518,3520,3521,3522,3523],[3508,3525,3526,3527,3528],{"slug":3538,"sourceId":3508,"sourceLabel":3509,"sourceYear":2267,"table":279,"note":3539,"datasets":3540,"metrics":3542,"families":3543,"methods":3544,"methodIds":3546,"rows":3547,"failures":30},"dufomap2024-table-iii","Influence of the pose source on dynamic point removal, KITTI sequence 00: KITTI odometry ground-truth poses, SemanticKITTI poses estimated by SuMa, an…",[3541],"KITTI (SemanticKITTI labels)",[23],[23],[3518,3520,3521,3545,3523],"Octomap [16]",[3508,3525,3526,3527,3528],45,{"slug":3549,"sourceId":3508,"sourceLabel":3509,"sourceYear":2267,"table":731,"note":3550,"datasets":3551,"metrics":3552,"families":3553,"methods":3554,"methodIds":3560,"rows":641,"failures":30},"dufomap2024-table-iv","Ablation on KITTI 00: sensor-noise margin ds (m), localization margin dp (voxels) and voxel size v (m); SA, DA, AA in %",[3513],[23],[23],[3555,3556,3557,3558,3559],"DUFOMap (dp = 1, v = 0.1)","DUFOMap (ds = 0.2, dp = 1, v = 0.1)","DUFOMap (ds = 0.2, dp = 1, v = 0.2)","DUFOMap (ds = 0.2, v = 0.1)","DUFOMap (w\u002Fo ds, dp, v = 0.1)",[3508],{"slug":3562,"sourceId":3508,"sourceLabel":3509,"sourceYear":2267,"table":3563,"note":3564,"datasets":3565,"metrics":3566,"families":3567,"methods":3568,"methodIds":3569,"rows":63,"failures":154},"dufomap2024-text-sec-v-a2","Text Sec.V-A2","Low-power robot computer test using the Dynablox setup with range reduced to 20 m on the semi-indoor data",[3514],[148],[40],[3518,3520],[3508,3525],{"slug":3571,"sourceId":3572,"sourceLabel":3573,"sourceYear":213,"table":3574,"note":3575,"datasets":3576,"metrics":3577,"families":3578,"methods":3579,"methodIds":3586,"rows":120,"failures":30},"segmap2020-fig-7-legend","segmap2020","Dubé et al., 2020","Fig. 7 legend","ROC area for segment-pair classification on 45M labeled descriptor pairs from KITTI 00; areas are printed in the figure legend (curves not digitized)",[469],[23],[23],[3580,3581,3582,3583,3584,3585],"Autoencoder","Classification-only training","Eigen (eigenvalue-based features)","SegMap","SegMini","Triplet loss",[3572],{"slug":3588,"sourceId":3572,"sourceLabel":3573,"sourceYear":213,"table":69,"note":3589,"datasets":3590,"metrics":3591,"families":3592,"methods":3593,"methodIds":3602,"rows":618,"failures":30},"segmap2020-table-1","Average ratio of corresponding points within one voxel between original and reconstructed segments (KITTI 00 segments), by descriptor size",[469],[23],[23],[3594,3595,3596,3597,3598,3599,3600,3601],"Autoencoder baseline, descriptor size 128","Autoencoder baseline, descriptor size 16","Autoencoder baseline, descriptor size 32","Autoencoder baseline, descriptor size 64","SegMap, descriptor size 128","SegMap, descriptor size 16","SegMap, descriptor size 32","SegMap, descriptor size 64",[3572],{"slug":3604,"sourceId":3572,"sourceLabel":3573,"sourceYear":213,"table":108,"note":3605,"datasets":3606,"metrics":3609,"families":3610,"methods":3611,"methodIds":3615,"rows":641,"failures":30},"segmap2020-table-2","Statistics of the three multi-robot experiments: KITTI 00 (5 robots, 114 s), Gustav Knepper powerplant (3 UGVs, 850 s), Phoenix-West foundry (2 UGVs,…",[3607,469,3608],"Foundry search and rescue data","Powerplant search and rescue data",[219,23],[40,23],[3583,3612,3613,3614],"SegMap descriptors","raw local clouds (comparison)","raw segments (comparison)",[3572],{"slug":3617,"sourceId":3572,"sourceLabel":3573,"sourceYear":213,"table":3618,"note":3619,"datasets":3620,"metrics":3621,"families":3622,"methods":3623,"methodIds":3628,"rows":356,"failures":30},"segmap2020-text-sec-5-3","Text Sec. 5.3","Average time to compute one segment descriptor",[469],[38],[40],[3624,3625,3626,3627],"SegMap descriptor (CPU)","SegMap descriptor (GPU)","SegMini descriptor (CPU)","SegMini descriptor (GPU)",[3572],{"slug":3630,"sourceId":3572,"sourceLabel":3573,"sourceYear":213,"table":3631,"note":3632,"datasets":3633,"metrics":3634,"families":3635,"methods":3636,"methodIds":3638,"rows":63,"failures":30},"segmap2020-text-sec-5-6","Text Sec. 5.6","Semantic extractor (vehicle, building, other) trained on 1750 manually labelled KITTI 05 segments, 70\u002F30 split",[469],[23],[23],[3637],"SegMap semantic extractor",[3572],{"slug":3640,"sourceId":3572,"sourceLabel":3573,"sourceYear":213,"table":3641,"note":3642,"datasets":3643,"metrics":3644,"families":3645,"methods":3646,"methodIds":3649,"rows":356,"failures":30},"segmap2020-text-sec-5-9-1","Text Sec. 5.9.1","KITTI 00 split into five simultaneously played robots (114 s), vehicle segments rejected",[469],[148,219,23,38],[40,23],[3583,3647,3648],"SegMap localization before ICP refinement","raw segment point clouds (no descriptor compression)",[3572],{"slug":3651,"sourceId":3652,"sourceLabel":3653,"sourceYear":698,"table":3654,"note":3655,"datasets":3656,"metrics":3658,"families":3659,"methods":3660,"methodIds":3663,"rows":356,"failures":356},"ebadi2021dareslam-text-sec-3-1","ebadi2021dareslam","Ebadi et al., 2021","Text Sec. 3.1","Front-end odometry drift from EVO relative pose error per 300 m travelled in autonomous traverses; values stated in text (Fig. 4 box plots); percentag…",[3657],"authors' DARPA SubT Tunnel Circuit recordings",[330],[332],[3661,3662],"scan-to-scan GICP registration only","scan-to-scan plus scan-to-submap GICP (two-stage front-end from LAMP used in DARE-SLAM)",[3652],{"slug":3665,"sourceId":3652,"sourceLabel":3653,"sourceYear":698,"table":3666,"note":3667,"datasets":3668,"metrics":3670,"families":3671,"methods":3672,"methodIds":3674,"rows":154,"failures":30},"ebadi2021dareslam-text-sec-3-2","Text Sec. 3.2","ROC analysis of the geometric degeneracy detector on 254 manually labelled lidar scans, 61 of them degenerate",[3669],"authors' labelled scans",[23],[23],[3673],"degeneracy detector (log condition number of approximate Hessian)",[3652],{"slug":3676,"sourceId":3652,"sourceLabel":3653,"sourceYear":698,"table":254,"note":3677,"datasets":3678,"metrics":3680,"families":3681,"methods":3682,"methodIds":3684,"rows":154,"failures":30},"ebadi2021dareslam-text-sec-4-1","ROC analysis of occupancy-grid pre-matching; 100 salient grid maps per environment, 20 of which are true loop closures",[3679],"authors' recordings",[23],[23],[3683],"SGLC pre-matching (similarity confidence)",[3652],{"slug":3686,"sourceId":3652,"sourceLabel":3653,"sourceYear":698,"table":3687,"note":3688,"datasets":3689,"metrics":3691,"families":3692,"methods":3693,"methodIds":3695,"rows":154,"failures":30},"ebadi2021dareslam-text-sec-4-2","Text Sec. 4.2","Number of attempted loop closures when the BGLC search radius is expanded from 5 m to 20 m",[3690],"authors' recording",[23],[23],[3694],"BGLC with 20 m search radius",[],{"slug":3697,"sourceId":3698,"sourceLabel":3699,"sourceYear":2267,"table":325,"note":3700,"datasets":3701,"metrics":3703,"families":3704,"methods":3705,"methodIds":3708,"rows":2882,"failures":30},"ebadi2024subt-table-ii","ebadi2024subt","Ebadi et al., 2024","DARPA SubT Final Event, four ANYmal robots; APE mean (std in parentheses) for onboard CompSLAM per robot vs M3RM server (all robots considered togethe…",[3702],"DARPA SubT Final Event",[329],[25],[3706,3707],"CompSLAM (Onboard)","M3RM (Server)",[],{"slug":3710,"sourceId":3698,"sourceLabel":3699,"sourceYear":2267,"table":2225,"note":3711,"datasets":3712,"metrics":3715,"families":3716,"methods":3717,"methodIds":3720,"rows":63,"failures":63},"ebadi2024subt-text-sec-iv-a","General statement across SubT teams' LIDAR-centric odometry in underground tests",[3713,3714],"Bull Rock Cave system","SubT team tests (not itemized)",[23],[23],[3718,3719],"CTU-CRAS-Norlab UAV SLAM (A-LOAM with Kalman-filter state estimation)","modern LIDAR-centric odometry estimators (SubT teams)",[],{"slug":3722,"sourceId":3698,"sourceLabel":3699,"sourceYear":2267,"table":1928,"note":3723,"datasets":3724,"metrics":3726,"families":3727,"methods":3728,"methodIds":3732,"rows":356,"failures":30},"ebadi2024subt-text-sec-iv-b","DARPA-reported map deviation in the SubT Final: percentage of submitted points farther than 1 m from the surveyed point cloud (definition in Sec. IV-C…",[3725,3702],"CU Boulder Engineering Center and parking garage",[1551,75,23],[1553,78,23],[3729,3730,3731],"CoSTAR LAMP with GNC","LIO-SAM (MARBLE)","Team Explorer SLAM (Super Odometry front-end with loop-closing back-end)",[338],{"slug":3734,"sourceId":3698,"sourceLabel":3699,"sourceYear":2267,"table":1278,"note":3735,"datasets":3736,"metrics":3738,"families":3739,"methods":3740,"methodIds":3742,"rows":274,"failures":154},"ebadi2024subt-text-sec-iv-c","DARPA-reported map deviation in the SubT Final: percentage of submitted points farther than 1 m from the surveyed point cloud",[3702,3737],"DARPA SubT Urban Event",[75,23],[78,23],[3741],"CSIRO Wildcat",[799],{"slug":3744,"sourceId":3745,"sourceLabel":3746,"sourceYear":16,"table":325,"note":3747,"datasets":3748,"metrics":3749,"families":3750,"methods":3751,"methodIds":3755,"rows":3757,"failures":30},"eckenhoff2019closedform-table-ii","eckenhoff2019closedform","Eckenhoff et al., 2019","Indirect stereo VIO; absolute RMSE averaged over 10 runs; ground-truth initialisation",[743],[568,23],[25,23],[3752,3753,3754,2803],"DISCRETE","MODEL-1","MODEL-2",[3745,3756,1510],"forster2017preint",88,{"slug":3759,"sourceId":3745,"sourceLabel":3746,"sourceYear":16,"table":731,"note":3760,"datasets":3761,"metrics":3762,"families":3763,"methods":3764,"methodIds":3765,"rows":3766,"failures":30},"eckenhoff2019closedform-table-iv","Direct stereo VINS (iSAM2, loop closures); absolute RMSE averaged over 10 runs; ground-truth initialisation",[743],[568,23],[25,23],[3752,3753,3754],[3745,3756],66,{"slug":3768,"sourceId":3745,"sourceLabel":3746,"sourceYear":16,"table":3769,"note":3770,"datasets":3771,"metrics":3773,"families":3774,"methods":3775,"methodIds":3779,"rows":356,"failures":30},"eckenhoff2019closedform-text-sec-vii-a2-gore-hall","Text Sec.VII-A2 (Gore Hall)","228 m hand-held loop from the first floor up the staircase to the third floor and back, one loop per level; ending error at the return to the start; e…",[3772],"UD Gore Hall",[1551],[1553],[3776,3777,2803,3778],"Model 1","Model 2","discrete preintegration",[3745,3756,1510],{"slug":3781,"sourceId":3745,"sourceLabel":3746,"sourceYear":16,"table":3782,"note":3783,"datasets":3784,"metrics":3786,"families":3787,"methods":3788,"methodIds":3789,"rows":356,"failures":30},"eckenhoff2019closedform-text-sec-vii-a2-smith-hall","Text Sec.VII-A2 (Smith Hall)","230 m hand-held run over the second and first floors returning to the start, with people walking, varying lighting and feature-poor areas; ending erro…",[3785],"UD Smith Hall",[1551],[1553],[3776,3777,2803,3778],[3745,3756,1510],{"slug":3791,"sourceId":3792,"sourceLabel":3793,"sourceYear":538,"table":3794,"note":3795,"datasets":3796,"metrics":3799,"families":3800,"methods":3801,"methodIds":3806,"rows":120,"failures":356},"dpslam2003-text-sec-4","dpslam2003","Eliazar & Parr, 2003","Text Sec. 4","Hallway loop about 16 m x 14 m, 60 m traveled before re-observing the start; 3 cm grid",[3797,3798],"Duke University Computer Science building, 2nd floor","Duke University Computer Science building, 2nd floor (iRobot ATRV Jr., SICK)",[23],[23],[3802,3803,3804,3805],"DP-SLAM","DP-SLAM (handicapped, 1 in 4 laser casts)","DP-SLAM with culling (k = 6)","SLAM using a single map (best particle)",[3792],{"slug":3808,"sourceId":3809,"sourceLabel":3810,"sourceYear":921,"table":91,"note":3811,"datasets":3812,"metrics":3814,"families":3815,"methods":3816,"methodIds":3818,"rows":3819,"failures":30},"rgbdslamv2-2014-table-i","rgbdslamv2_2014","Endres et al., 2014","Detailed results of RGBDSLAMv2 (SIFT on GPU, offline processing of every frame) on TUM RGB-D sequences and an MIT Stata Center sequence; ATE after min…",[3813,2872],"MIT Stata Center RGB-D dataset",[568,148,23],[25,40,23],[3817],"RGBDSLAMv2",[],25,{"slug":3821,"sourceId":3809,"sourceLabel":3810,"sourceYear":921,"table":3822,"note":3823,"datasets":3824,"metrics":3826,"families":3827,"methods":3828,"methodIds":3830,"rows":274,"failures":30},"rgbdslamv2-2014-text-fig-8-caption","Text Fig.8 caption","Frame rate over 288 evaluation runs with SIFT on the fr1 sequences (stated in the Fig. 8 caption)",[3825],"TUM RGB-D fr1",[148],[40],[3829],"RGBDSLAMv2 with SIFT",[],{"slug":3832,"sourceId":3809,"sourceLabel":3810,"sourceYear":921,"table":1193,"note":3833,"datasets":3834,"metrics":3836,"families":3837,"methods":3838,"methodIds":3841,"rows":63,"failures":30},"rgbdslamv2-2014-text-sec-iv-b","Feature-type comparison over the nine fr1 sequences with four parameterisations each (Fig. 7); values stated in the text",[3835],"TUM RGB-D fr1 (nine sequences)",[568],[25],[3839,3840],"RGBDSLAMv2 with ORB or Shi-Tomasi plus SURF","RGBDSLAMv2 with SIFT (GPU)",[],{"slug":3843,"sourceId":3809,"sourceLabel":3810,"sourceYear":921,"table":1221,"note":3844,"datasets":3845,"metrics":3846,"families":3847,"methods":3848,"methodIds":3850,"rows":154,"failures":30},"rgbdslamv2-2014-text-sec-iv-d","Average runtime of the bidirectional EMM evaluation on depth images subsampled to 80 x 60",[2872],[38],[40],[3849],"RGBDSLAMv2 EMM",[],{"slug":3852,"sourceId":2881,"sourceLabel":3853,"sourceYear":921,"table":3854,"note":3855,"datasets":3856,"metrics":3858,"families":3859,"methods":3860,"methodIds":3865,"rows":1042,"failures":416},"lsdslam2014-fig-9","Engel et al., 2014","Fig. 9","Result table printed as Fig. 9: absolute trajectory RMSE (cm) on TUM RGB-D and two simulated sequences from Handa et al.; LSD-SLAM also lists keyframe…",[2872,3857],"synthetic sequences (Handa et al. 2012)",[568],[25],[2924,3861,3862,3863,3864],"direct RGB-D SLAM [14] (DVO SLAM)","keypoint-based RGB-D SLAM [7]","keypoint-based mono-SLAM [15] (PTAM)","semi-dense mono-VO [9]",[2881,3866],"ptam2007",{"slug":3868,"sourceId":1507,"sourceLabel":3869,"sourceYear":107,"table":254,"note":3870,"datasets":3871,"metrics":3873,"families":3874,"methods":3875,"methodIds":3877,"rows":154,"failures":30},"dso2018-text-sec-4-1","Engel et al., 2018","Reduced settings (Np = 800 points, Nf = 6 active frames, 424x320 images, at most 4 Gauss-Newton iterations per keyframe); images pre-loaded, decoded a…",[3872],"TUM monoVO, EuRoC MAV, ICL-NUIM",[23],[23],[3876],"DSO (LQ, 5x RT)",[1507],{"slug":3879,"sourceId":1507,"sourceLabel":3869,"sourceYear":107,"table":3687,"note":3880,"datasets":3881,"metrics":3883,"families":3884,"methods":3885,"methodIds":3887,"rows":154,"failures":30},"dso2018-text-sec-4-2","Default keyframe threshold Tkf = 1; the Sec. 4.2 text states an average of 8 keyframes per second, while the Fig. 18 legend labels the same default (x…",[3882],"TUM monoVO",[23],[23],[3886],"DSO (default settings)",[1507],{"slug":3889,"sourceId":3890,"sourceLabel":3891,"sourceYear":306,"table":17,"note":3892,"datasets":3893,"metrics":3895,"families":3896,"methods":3897,"methodIds":3901,"rows":52,"failures":30},"fahle2022geotechmls-table-3","fahle2022geotechmls","Fahle et al., 2022","Point collection efficiency on a 45 m production-level section at Mine-A; MLS on a mine vehicle at about 10 km\u002Fh",[3894],"authors' Mine-A and Edgar mine datasets",[23],[23],[3898,3899,3900],"Emesent Hovermap (vehicle, in and out)","Faro Focus S70 (static, 3 stations)","Kaarta Stencil 2 (vehicle, in and out)",[],{"slug":3903,"sourceId":3890,"sourceLabel":3891,"sourceYear":306,"table":1072,"note":3904,"datasets":3905,"metrics":3906,"families":3907,"methods":3908,"methodIds":3913,"rows":29,"failures":30},"fahle2022geotechmls-table-6","SLAM precision of Hovermap measured by C2M distance between two SLAM outputs of one scan (intrinsic) or two independent scans (extrinsic); handheld, E…",[3894],[23],[23],[3909,3910,3911,3912],"Hovermap extrinsic, no SLAM registration, opposite direction (EXN, OD)","Hovermap extrinsic, no SLAM registration, same direction (EXN, SD)","Hovermap intrinsic, loop closure (INL)","Hovermap intrinsic, no loop closure (INN)",[],{"slug":3915,"sourceId":3890,"sourceLabel":3891,"sourceYear":306,"table":621,"note":3916,"datasets":3917,"metrics":3918,"families":3920,"methods":3921,"methodIds":3922,"rows":1069,"failures":30},"fahle2022geotechmls-table-7","Absolute target-level accuracy of Kaarta Stencil 2 vs static FARO data; F flat, R rough, H half of drift surface registered, S SOR-filtered; C2M or M3…",[3894],[3919,75],"m3c2",[78],[3423],[],{"slug":3924,"sourceId":3890,"sourceLabel":3891,"sourceYear":306,"table":633,"note":3925,"datasets":3926,"metrics":3927,"families":3928,"methods":3929,"methodIds":3931,"rows":29,"failures":30},"fahle2022geotechmls-table-8","Absolute target-level accuracy of Emesent Hovermap vs static FARO data; C2M or M3C2 distances; MLS co-registered to the static data by manual transfor…",[3894],[3919,75],[78],[3930],"Emesent Hovermap",[],{"slug":3933,"sourceId":3890,"sourceLabel":3891,"sourceYear":306,"table":644,"note":3934,"datasets":3935,"metrics":3936,"families":3937,"methods":3938,"methodIds":3939,"rows":608,"failures":30},"fahle2022geotechmls-table-9","Relative target-level accuracy between two MLS epochs of the same system with M3C2; column labels '15 m', '5 m', 'SOR' as printed (SOR = statistical o…",[3894],[3919],[78],[3930,3423],[],{"slug":3941,"sourceId":3890,"sourceLabel":3891,"sourceYear":306,"table":3942,"note":3943,"datasets":3944,"metrics":3945,"families":3946,"methods":3947,"methodIds":3950,"rows":356,"failures":30},"fahle2022geotechmls-text-sec-4-4","Text Sec. 4.4","Global ICP alignment of 170 m of MLS data to surveyed static data; average over nine alignment runs (CloudCompare and Maptek PointStudio)",[3894],[1551,75,23],[1553,78,23],[3948,3949],"Hovermap (0.12 % drift attributed to Hovermap in Sec. 5.2)","MLS data (system not named in Sec. 4.4)",[],{"slug":3952,"sourceId":3890,"sourceLabel":3891,"sourceYear":306,"table":657,"note":3953,"datasets":3954,"metrics":3955,"families":3956,"methods":3957,"methodIds":3962,"rows":618,"failures":154},"fahle2022geotechmls-text-sec-4-6","Loop closure test: first 240 m of a 650 m Edgar trajectory registered to static data using a 30 m section at the scan start; maximum differences read…",[3894],[75,23],[78,23],[3958,3959,3960,3961],"Emesent Hovermap with Emesent SLAM registration","Hovermap with loop closure","Hovermap without loop closure","Kaarta Stencil 2 with Mine Vision Systems SLAM registration",[],{"slug":3964,"sourceId":3965,"sourceLabel":3966,"sourceYear":306,"table":91,"note":3967,"datasets":3968,"metrics":3969,"families":3970,"methods":3971,"methodIds":3975,"rows":52,"failures":154},"faizullin2022lidarsync-table-i","faizullin2022lidarsync","Faizullin et al., 2022","Packet-to-packet period of VLP-16 timestamps under three timestamping techniques; the text states about 10 min and more than half a million UDP packet…",[3690],[23],[23],[3972,3973,3974],"MCU-based clock (ours)","ROS arrival-time timestamping","internal LiDAR clock (authors' driver patch)",[3965],{"slug":3977,"sourceId":3965,"sourceLabel":3966,"sourceYear":306,"table":454,"note":3978,"datasets":3979,"metrics":3981,"families":3982,"methods":3983,"methodIds":3984,"rows":154,"failures":154},"faizullin2022lidarsync-text-sec-iv","Synchronization accuracy estimated from LiDAR and IMU documentation and MCU delays; not measured",[3980],"not_applicable",[23],[23],[3972],[3965],{"slug":3986,"sourceId":3987,"sourceLabel":3988,"sourceYear":374,"table":17,"note":3989,"datasets":3990,"metrics":3992,"families":3993,"methods":3994,"methodIds":3997,"rows":578,"failures":30},"feng2023bridgeslam-table-3","feng2023bridgeslam","Feng et al., 2023","Point-cloud quality of the inspection zone (pier) from SfM-based and SLAM-based (R3LIVE) reconstruction of the same field data, following Chen et al.;…",[3991],"authors' bridge pier field data",[1819,23],[23,1821],[3995,3996],"SLAM-based (multi-sensor fusion SLAM, R3LIVE)","SfM-based",[2387],{"slug":3999,"sourceId":3987,"sourceLabel":3988,"sourceYear":374,"table":33,"note":4000,"datasets":4001,"metrics":4002,"families":4003,"methods":4004,"methodIds":4006,"rows":654,"failures":30},"feng2023bridgeslam-table-4","Whole-scene reconstruction with the point counts of the two methods kept relatively close; SfM total = SfM 10 min 54 s + MVS 8 min 24 s + meshing 1 mi…",[3991],[23],[23],[4005,3996],"SLAM-based (R3LIVE)",[2387],{"slug":4008,"sourceId":3987,"sourceLabel":3988,"sourceYear":374,"table":244,"note":4009,"datasets":4010,"metrics":4011,"families":4012,"methods":4013,"methodIds":4016,"rows":4017,"failures":120},"feng2023bridgeslam-table-5","Crack width computed after projecting crack feature points onto the SLAM-derived mesh, comparing ESRGAN-based 4x super-resolution with bilinear interp…",[3991],[1819,23],[23,1821],[4014,4015],"bilinear interpolation","super-resolution based on ESRGAN (proposed pipeline)",[3987],81,{"slug":4019,"sourceId":3987,"sourceLabel":3988,"sourceYear":374,"table":4020,"note":4021,"datasets":4022,"metrics":4024,"families":4025,"methods":4026,"methodIds":4028,"rows":63,"failures":30},"feng2023bridgeslam-text-sec-3-3","Text Sec.3.3","Demonstration on a light rail line bridge scene: acquisition time with multi-sensor fusion SLAM",[4023],"authors' light rail bridge data",[23],[23],[4027],"multi-sensor fusion SLAM (R3LIVE)",[2387],{"slug":4030,"sourceId":3987,"sourceLabel":3988,"sourceYear":374,"table":4031,"note":4032,"datasets":4033,"metrics":4034,"families":4035,"methods":4036,"methodIds":4037,"rows":154,"failures":30},"feng2023bridgeslam-text-sec-6-4-2","Text Sec.6.4.2","Smallest crack width that the proposed pipeline could calculate in the pier case study (not listed in Table 5)",[3991],[23],[23],[4015],[3987],{"slug":4039,"sourceId":4040,"sourceLabel":4041,"sourceYear":562,"table":17,"note":4042,"datasets":4043,"metrics":4045,"families":4046,"methods":4047,"methodIds":4053,"rows":396,"failures":30},"feng2025-construction-lidar-eval-table-3","feng2025_construction_lidar_eval","Feng et al., 2025","Gazebo simulation of a typical hospital floor built from CAD drawings via BIM (292 x 142 m, 6 m storey); robot at 1 m\u002Fs along a 1,293 m, 1,383 s path…",[4044],"Feng et al. simulated construction-site dataset (Gazebo)",[329],[25],[4048,1437,317,4049,4050,334,461,4051,2197,4052],"F-LOAM","HDL-Graph-SLAM","ISC-LOAM","LOG-LIO","Point-LIO",[304,321,393,394,395,338,2118,4054],"pointlio2023",{"slug":4056,"sourceId":4040,"sourceLabel":4041,"sourceYear":562,"table":33,"note":4057,"datasets":4058,"metrics":4060,"families":4061,"methods":4062,"methodIds":4063,"rows":396,"failures":30},"feng2025-construction-lidar-eval-table-4","real construction site, standard floor of a hospital outpatient building in Xi'an (structure topped out, MEP and interior finishing works); teleoperat…",[4059],"Feng et al. real construction-site dataset (Xi'an hospital)",[329],[25],[4048,1437,317,4049,4050,334,461,4051,2197,4052],[304,321,393,394,395,338,2118,4054],{"slug":4065,"sourceId":4066,"sourceLabel":4067,"sourceYear":68,"table":69,"note":4068,"datasets":4069,"metrics":4071,"families":4072,"methods":4073,"methodIds":4074,"rows":641,"failures":30},"feng2026integratedslam-table-1","feng2026integratedslam","Feng et al., 2026","EVO ATE vs Gazebo ground truth; LiDAR-only methods; Min column omitted",[4070],"authors' Gazebo simulation of a hospital outpatient building (Xi'an)",[329,568,22],[25],[4048,2197,81],[4066,393,395],{"slug":4076,"sourceId":4066,"sourceLabel":4067,"sourceYear":68,"table":108,"note":4077,"datasets":4078,"metrics":4079,"families":4080,"methods":4081,"methodIds":4082,"rows":598,"failures":30},"feng2026integratedslam-table-2","Wall-to-column distances in the SLAM map vs CAD drawing dimensions at 10 reference pairs; percentage error; map sizes omitted",[4070],[1819],[1821],[4048,2197,81],[4066,393,395],{"slug":4084,"sourceId":4066,"sourceLabel":4067,"sourceYear":68,"table":17,"note":4085,"datasets":4086,"metrics":4087,"families":4088,"methods":4089,"methodIds":4095,"rows":641,"failures":30},"feng2026integratedslam-table-3","Ablation in simulation: G ground segmentation, P FEC clustering, L Scan Context++ loop closure; Min, Median, STD omitted",[4070],[329,568,22],[25],[4090,4091,4092,4093,4094],"G","G + L","G + P","G + P (w\u002Fo two-step registration)","G + P + L",[4066],{"slug":4097,"sourceId":4066,"sourceLabel":4067,"sourceYear":68,"table":33,"note":4098,"datasets":4099,"metrics":4100,"families":4101,"methods":4102,"methodIds":4103,"rows":641,"failures":30},"feng2026integratedslam-table-4","EVO ATE in simulation with moving objects (worker cylinder 0.80 m\u002Fs, trolley 0.50 m\u002Fs, lifting platform 0.20 m\u002Fs); Min omitted",[4070],[329,568,22],[25],[4048,2197,81],[4066,393,395],{"slug":4105,"sourceId":4066,"sourceLabel":4067,"sourceYear":68,"table":244,"note":4106,"datasets":4107,"metrics":4109,"families":4110,"methods":4111,"methodIds":4112,"rows":641,"failures":30},"feng2026integratedslam-table-5","EVO ATE on the real site; ground-truth source not described in the paper; Min (0.00 for all) omitted",[4108],"authors' real-site recording, hospital outpatient building (Xi'an)",[329,568,22],[25],[4048,2197,81],[4066,393,395],{"slug":4114,"sourceId":4066,"sourceLabel":4067,"sourceYear":68,"table":1072,"note":4115,"datasets":4116,"metrics":4117,"families":4118,"methods":4119,"methodIds":4120,"rows":598,"failures":30},"feng2026integratedslam-table-6","Wall-to-column distances in the SLAM map vs construction drawing dimensions at 10 reference pairs (drawings, not an independent survey); percentage er…",[4108],[1819],[1821],[4048,2197,81],[4066,393,395],{"slug":4122,"sourceId":4066,"sourceLabel":4067,"sourceYear":68,"table":621,"note":4123,"datasets":4124,"metrics":4125,"families":4126,"methods":4127,"methodIds":4128,"rows":641,"failures":30},"feng2026integratedslam-table-7","Ablation on the real site; Min, Median, STD omitted",[4108],[329,568,22],[25],[4090,4091,4092,4093,4094],[4066],{"slug":4130,"sourceId":4066,"sourceLabel":4067,"sourceYear":68,"table":633,"note":4131,"datasets":4132,"metrics":4133,"families":4134,"methods":4135,"methodIds":4136,"rows":224,"failures":30},"feng2026integratedslam-table-8","Runtime on NVIDIA Jetson Xavier NX with the real-site data; map accuracy is the mean of Table 6 percentage errors; ATE RMSE column duplicates Table 5…",[4108],[113,1819,219,38],[40,1821],[4048,2197,81],[4066,393,395],{"slug":4138,"sourceId":4066,"sourceLabel":4067,"sourceYear":68,"table":4139,"note":4140,"datasets":4141,"metrics":4142,"families":4143,"methods":4144,"methodIds":4145,"rows":274,"failures":30},"feng2026integratedslam-text-sec-5-2-4","Text Sec. 5.2.4","Modular runtime of the proposed system stated in text",[4108],[38],[40],[81],[4066],{"slug":4147,"sourceId":4148,"sourceLabel":4149,"sourceYear":2267,"table":91,"note":4150,"datasets":4151,"metrics":4153,"families":4154,"methods":4155,"methodIds":4158,"rows":102,"failures":30},"madicp2024-table-i","madicp2024","Ferrari et al., 2024","Ablation of the information-aware local-map update on Newer College OS0 sequences; KITTI-style RPE over 10-80 m segments",[4152],"Newer College (OS0-128)",[438],[441],[4156,4157],"Ours (MAD-ICP)","Ours w\u002Fo IA (naive update pushing the last frame)",[4148],{"slug":4160,"sourceId":4148,"sourceLabel":4149,"sourceYear":2267,"table":325,"note":4161,"datasets":4162,"metrics":4166,"families":4167,"methods":4168,"methodIds":4170,"rows":4171,"failures":224},"madicp2024-table-ii","KITTI benchmark RPE (%); segments 100-800 m for KITTI, MulRan and NC1, 10-80 m for NC0 and Hilti; averages exclude failures; per-sequence KITTI 00-10…",[4163,1997,671,4164,4165,3147],"Hilti 2021 (OS0-64)","Newer College NC0 (OS0-128)","Newer College NC1 (OS1-64)",[438],[441],[3131,4048,571,4169,4156],"MULLS",[596,393,577,4148,597],95,{"slug":4173,"sourceId":4148,"sourceLabel":4149,"sourceYear":2267,"table":279,"note":4174,"datasets":4175,"metrics":4177,"families":4178,"methods":4179,"methodIds":4180,"rows":416,"failures":30},"madicp2024-table-iii","VBR robustness benchmark: area under the cumulative RPE curve up to 10% over all Table II sequences; higher is better",[4176],"all Table II datasets",[23],[23],[3131,4048,571,4169,4156],[596,393,577,4148,597],{"slug":4182,"sourceId":4183,"sourceLabel":4184,"sourceYear":4185,"table":91,"note":4186,"datasets":4187,"metrics":4189,"families":4190,"methods":4191,"methodIds":4194,"rows":2262,"failures":30},"fischler1981ransac-table-i","fischler1981ransac","Fischler & Bolles, 1981",1981,"Ten typical of 50 synthetic LDPs, 30 landmark-to-image correspondences each; gross errors at least 10 px off, good ones with 1 px std; RANSAC avoided…",[4188],"50 synthetic location determination problems",[23],[23],[4192,4193],"RANSAC\u002FLD","problem set-up (ground truth)",[4183],{"slug":4196,"sourceId":4183,"sourceLabel":4184,"sourceYear":4185,"table":4197,"note":4198,"datasets":4199,"metrics":4201,"families":4202,"methods":4203,"methodIds":4206,"rows":274,"failures":154},"fischler1981ransac-text-sec-iv-c","Text Sec. IV.C","One LDP with 20 landmarks, 5 gross errors (>10 px) and 1 px noise on good correspondences",[4200],"synthetic LDP (20 landmarks)",[23],[23],[4204,4205],"RANSAC","least-squares pruning heuristic (delete largest deviation, 3-sigma test)",[4183],{"slug":4208,"sourceId":4183,"sourceLabel":4184,"sourceYear":4185,"table":4209,"note":4210,"datasets":4211,"metrics":4212,"families":4213,"methods":4214,"methodIds":4215,"rows":154,"failures":30},"fischler1981ransac-text-sec-iv-d","Text Sec. IV.D","Execution time of the program for the synthetic LDPs",[4188],[23],[23],[4192],[4183],{"slug":4217,"sourceId":4183,"sourceLabel":4184,"sourceYear":4185,"table":4218,"note":4219,"datasets":4220,"metrics":4222,"families":4223,"methods":4224,"methodIds":4225,"rows":618,"failures":30},"fischler1981ransac-text-sec-iv-e","Text Sec. IV.E","Aerial image from about 4,000 ft with 6 in. lens, digitized 2,000 x 2,000 px (about 2 ft per px); 25 landmarks found by cross correlation, 3 gross err…",[4221],"real aerial image",[23],[23],[4192],[4183],{"slug":4227,"sourceId":3756,"sourceLabel":4228,"sourceYear":345,"table":4229,"note":4230,"datasets":4231,"metrics":4233,"families":4234,"methods":4235,"methodIds":4237,"rows":154,"failures":30},"forster2017preint-text-sec-viii-a1","Forster et al., 2017a","Text Sec.VIII-A1","Simulated 120 m circular trajectory, 50 Monte Carlo runs, camera at 2.5 Hz (keyframes)",[4232],"simulation (circular trajectory with sinusoidal vertical motion)",[38],[40],[4236],"Proposed (iSAM2)",[3756],{"slug":4239,"sourceId":3756,"sourceLabel":4228,"sourceYear":345,"table":4240,"note":4241,"datasets":4242,"metrics":4244,"families":4245,"methods":4246,"methodIds":4249,"rows":274,"failures":30},"forster2017preint-text-sec-viii-b2-drift","Text Sec.VIII-B2 (drift)","Relative odometric error over trajectory segments of the 430 m indoor sequence (metric of Geiger et al.), average drift at 360 m; OKVIS and MSCKF traj…",[4243],"430 m indoor sequence recorded with a forward-looking VI-Sensor, with Vicon ground truth; dataset and OKVIS and MSCKF trajectories obtained from the OKVIS authors",[330],[332],[4247,2803,4248],"MSCKF (implementation of the MSCKF filter)","Proposed (SVO front-end, preintegrated IMU and structureless vision factors, iSAM2)",[3756,1509,1510],{"slug":4251,"sourceId":3756,"sourceLabel":4228,"sourceYear":345,"table":4252,"note":4253,"datasets":4254,"metrics":4255,"families":4256,"methods":4257,"methodIds":4260,"rows":63,"failures":30},"forster2017preint-text-sec-viii-b2-timing","Text Sec.VIII-B2 (timing)","Processing time on the real indoor sequence",[4243],[38],[40],[4258,4259],"Proposed back-end (iSAM2)","Proposed front-end (SVO)",[3756],{"slug":4262,"sourceId":3756,"sourceLabel":4228,"sourceYear":345,"table":4263,"note":4264,"datasets":4265,"metrics":4267,"families":4268,"methods":4269,"methodIds":4272,"rows":63,"failures":30},"forster2017preint-text-sec-viii-b3-multi-floor","Text Sec.VIII-B3 (multi-floor)","160 m trajectory from the ground floor to the third floor of an office building and back to the start",[4266],"own indoor multi-floor sequence",[1551],[1553],[4270,4271],"Google Tango Peanut (mapper version 3.15)","Proposed",[3756],{"slug":4274,"sourceId":3756,"sourceLabel":4228,"sourceYear":345,"table":4275,"note":4276,"datasets":4277,"metrics":4279,"families":4280,"methods":4281,"methodIds":4282,"rows":63,"failures":154},"forster2017preint-text-sec-viii-b3-outdoor-loop","Text Sec.VIII-B3 (outdoor loop)","300 m walk around an office building with identical start and end point; VI-Sensor rigidly attached to a Tango device; text gives 1.5 m for the propos…",[4278],"own outdoor sequence",[1551],[1553],[4270,4271],[3756],{"slug":4284,"sourceId":1512,"sourceLabel":4285,"sourceYear":345,"table":91,"note":4286,"datasets":4287,"metrics":4288,"families":4289,"methods":4290,"methodIds":4301,"rows":4302,"failures":3323},"svo2017-table-i","Forster et al., 2017b","EuRoC; absolute translation error RMSE of keyframe positions after least-squares translation and scale alignment, averaged over five runs; loop closur…",[1482],[568],[25],[1488,4291,4292,4293,4294,1500,4295,4296,4297,1501,4298,4299,4300],"DSO (monocular, real-time)","LSD-SLAM (monocular, no loop-closure)","ORB-SLAM (monocular, no loop, real-time)","ORB-SLAM (monocular, no loop-closure)","SVO (monocular, bundle adjustment)","SVO (monocular, edgelets + prior)","SVO (monocular, edgelets)","SVO (stereo, bundle adjustment)","SVO (stereo, edgelets + prior)","SVO (stereo, edgelets)",[1507,2881,119,1512],143,{"slug":4304,"sourceId":1512,"sourceLabel":4285,"sourceYear":345,"table":325,"note":4305,"datasets":4306,"metrics":4307,"families":4308,"methods":4309,"methodIds":4320,"rows":598,"failures":30},"svo2017-table-ii","Processing time per frame and average CPU load at a constant 20 Hz input, averaged over three runs of EuRoC Machine Hall 01; all algorithms multi-thre…",[1482],[113,38],[40],[4310,4311,4312,4313,4314,4315,4316,4317,4318,4319],"LSD Mono SLAM (No loop closure)","ORB Mono SLAM (No loop closure)","SVO Mono","SVO Mono + Bundle Adjustment","SVO Mono + Prior","SVO Mono + Prior + Edgelet","SVO Stereo","SVO Stereo + Bundle Adjustment","SVO Stereo + Prior","SVO Stereo + Prior + Edgelet",[2881,119,1512],{"slug":4322,"sourceId":4323,"sourceLabel":4324,"sourceYear":306,"table":325,"note":4325,"datasets":4326,"metrics":4327,"families":4328,"methods":4329,"methodIds":4337,"rows":598,"failures":274},"artslam2022-table-ii","artslam2022","Frosi & Matteucci, 2022","KITTI odometry 07 (with loop); ATE after timestamp and index association; ART-SLAM variants: plain, with Scan Context, with IMU (de-skewing and orient…",[469],[329],[25],[4330,4331,4332,4333,4334,4335,334,461,2197,4336],"A-LOAM","ART-SLAM","ART-SLAM (GPS)","ART-SLAM (IMU)","ART-SLAM (SC)","HDL","LeGO-LOAM-BOR",[392,4323,394,395,338,2118],{"slug":4339,"sourceId":4323,"sourceLabel":4324,"sourceYear":306,"table":279,"note":4340,"datasets":4341,"metrics":4343,"families":4344,"methods":4345,"methodIds":4346,"rows":598,"failures":274},"artslam2022-table-iii","KITTI raw city sequence 05 (short, no ground truth); raw GPS used as reference; values as printed (LIO-SAM mean exceeds RMSE)",[4342],"KITTI raw",[329],[25],[4330,4331,4332,4333,4334,4335,334,461,2197,4336],[392,4323,394,395,338,2118],{"slug":4348,"sourceId":4323,"sourceLabel":4324,"sourceYear":306,"table":731,"note":4349,"datasets":4350,"metrics":4351,"families":4352,"methods":4353,"methodIds":4354,"rows":598,"failures":52},"artslam2022-table-iv","KITTI odometry 00 (long, with loops); ATE after timestamp and index association",[469],[329],[25],[4330,4331,4332,4333,4334,4335,334,461,2197,4336],[392,4323,394,395,338,2118],{"slug":4356,"sourceId":4323,"sourceLabel":4324,"sourceYear":306,"table":818,"note":4357,"datasets":4358,"metrics":4359,"families":4360,"methods":4361,"methodIds":4382,"rows":2119,"failures":30},"artslam2022-table-v","Average processing time per frame (ms) of mandatory modules for ART-SLAM variants",[469,4342],[38],[40],[4362,4363,4364,4365,4366,4367,4368,4369,4370,4371,4372,4373,4374,4375,4376,4377,4378,4379,4380,4381],"ART-SLAM (GPS), Floor detector","ART-SLAM (GPS), Graph optimization","ART-SLAM (GPS), Loop detection","ART-SLAM (GPS), Pre-filterer","ART-SLAM (GPS), Tracker","ART-SLAM (IMU), Floor detector","ART-SLAM (IMU), Graph optimization","ART-SLAM (IMU), Loop detection","ART-SLAM (IMU), Pre-filterer","ART-SLAM (IMU), Tracker","ART-SLAM (SC), Floor detector","ART-SLAM (SC), Graph optimization","ART-SLAM (SC), Loop detection","ART-SLAM (SC), Pre-filterer","ART-SLAM (SC), Tracker","ART-SLAM, Floor detector","ART-SLAM, Graph optimization","ART-SLAM, Loop detection","ART-SLAM, Pre-filterer","ART-SLAM, Tracker",[4323],{"slug":4384,"sourceId":4323,"sourceLabel":4324,"sourceYear":306,"table":4385,"note":4386,"datasets":4387,"metrics":4389,"families":4390,"methods":4391,"methodIds":4396,"rows":356,"failures":30},"artslam2022-text-sec-iii-a","Text Sec. III-A","Chilean underground mine dataset: 44 scans of about 25 M points taken 30 to 40 m apart; tracker initialized with ground truth plus uniform noise withi…",[4388],"Chilean underground mine dataset",[23,38],[40,23],[4392,4393,4394,4395],"ART-SLAM (noisy ground-truth initial guess)","ART-SLAM, pre-filtering","ART-SLAM, tracking","scan acquisition (sensor)",[4323],{"slug":4398,"sourceId":4399,"sourceLabel":4400,"sourceYear":1234,"table":4401,"note":4402,"datasets":4403,"metrics":4406,"families":4407,"methods":4408,"methodIds":4410,"rows":274,"failures":154},"furgale2013unifiedcalib-text-sec-v","furgale2013unifiedcalib","Furgale et al., 2013","Text Sec. V","Runtime for an about 80 s dataset (over 12,400 design variables, 144,000 error terms, 50,000 x 50,000 sparse system)",[4404,4405],"authors' calibration dataset","authors' calibration datasets",[23],[23],[4409],"joint estimation (J)",[4399],{"slug":4412,"sourceId":4399,"sourceLabel":4400,"sourceYear":1234,"table":1464,"note":4413,"datasets":4414,"metrics":4416,"families":4417,"methods":4418,"methodIds":4423,"rows":52,"failures":154},"furgale2013unifiedcalib-text-sec-v-b","40 real datasets (4 exposure times x 10, about 90 s each); slope of estimated time offset versus exposure time (theory 0.5) and RMS error to a line of…",[4415],"authors' custom visual-inertial sensor datasets",[23],[23],[4419,4420,4421,4422],"camera plus accelerometer only (A)","camera plus gyroscopes only (G)","joint estimation, camera plus gyroscopes plus accelerometers (J)","separated estimation (S), reference implementation of Mair et al. 2011",[4399],{"slug":4425,"sourceId":4426,"sourceLabel":4427,"sourceYear":681,"table":157,"note":4428,"datasets":4429,"metrics":4431,"families":4432,"methods":4433,"methodIds":4435,"rows":63,"failures":154},"furgale2015ct-text-sec-6-3-1","furgale2015ct","Furgale et al., 2015","Simulated 60 s sinusoidal trajectory, 1000 trials (120 monocular images, 5950 IMU measurements, 300 pose basis functions)",[4430],"simulation",[23],[23],[4434],"continuous-time batch estimator (B-spline)",[4426],{"slug":4437,"sourceId":4426,"sourceLabel":4427,"sourceYear":681,"table":4438,"note":4439,"datasets":4440,"metrics":4442,"families":4443,"methods":4444,"methodIds":4445,"rows":154,"failures":30},"furgale2015ct-text-sec-6-3-2","Text Sec.6.3.2","Real calibration dataset of about 2 min 20 s (1639 stereo images, 14,211 IMU measurements), 15 bias knots, pose spline with 300 knots, iterated to con…",[4441],"UTIAS hand-held calibration dataset",[23],[23],[4434],[4426],{"slug":4447,"sourceId":4448,"sourceLabel":4449,"sourceYear":562,"table":108,"note":4450,"datasets":4451,"metrics":4454,"families":4455,"methods":4456,"methodIds":4459,"rows":578,"failures":30},"gan2025decoupled-table-2","gan2025decoupled","Gan et al., 2025","Mean distance between wall pairs from point clouds vs laser rangefinder; TLS values from Zhai et al. 2024",[4452,4453],"TLS data from Zhai et al. 2024","own indoor test site",[1819],[1821],[4457,4458],"Robotic scan (from this study)","TLS (from Zhai et al., 2024)",[4448],{"slug":4461,"sourceId":4448,"sourceLabel":4449,"sourceYear":562,"table":17,"note":4462,"datasets":4463,"metrics":4464,"families":4465,"methods":4466,"methodIds":4469,"rows":1980,"failures":416},"gan2025decoupled-table-3","Covered grid ratio per building element (CloudCompare), grid sizes 152, 25, 13 mm per GSA levels; TLS from Zhai et al. 2024",[4452,4453],[76],[78],[4467,4468],"Robotic scan","TLS",[4448],{"slug":4471,"sourceId":4448,"sourceLabel":4449,"sourceYear":562,"table":33,"note":4472,"datasets":4473,"metrics":4474,"families":4475,"methods":4476,"methodIds":4477,"rows":618,"failures":30},"gan2025decoupled-table-4","Point cloud surface density at Level 3 (13 mm) per element, robotic scan column only (TLS column omitted: unit factor printed inconsistently); minimum…",[4453],[23],[23],[4467],[4448],{"slug":4479,"sourceId":4448,"sourceLabel":4449,"sourceYear":562,"table":3060,"note":4480,"datasets":4481,"metrics":4482,"families":4483,"methods":4484,"methodIds":4487,"rows":120,"failures":274},"gan2025decoupled-text-sec-4-2","Average discrepancy over the 5 distances in Table 2",[4452,4453],[1819,23],[23,1821],[4485,4467,4468,4486],"Robot-assisted automated scanning","robot-assisted automated scanning, subsequent data mapping (Lidarslam_ros2 per Sec. 3.3)",[4448],{"slug":4489,"sourceId":4490,"sourceLabel":4491,"sourceYear":107,"table":91,"note":4492,"datasets":4493,"metrics":4495,"families":4496,"methods":4497,"methodIds":4501,"rows":2388,"failures":154},"ldso2018-table-i","ldso2018","Gao et al., 2018","KITTI Odometry training sequences, monocular setting; ATE (m) after Sim(3) alignment to ground truth; x = failure",[4494],"KITTI Odometry",[329],[25],[4498,4499,4500],"LDSO","Mono DSO","ORB-SLAM2 (monocular)",[1507,1511],{"slug":4503,"sourceId":4490,"sourceLabel":4491,"sourceYear":107,"table":325,"note":4504,"datasets":4505,"metrics":4507,"families":4508,"methods":4509,"methodIds":4513,"rows":274,"failures":30},"ldso2018-table-ii","Average computation time of the point selection step per keyframe; laptop with Intel i7-4770HQ, 16 GB RAM, Ubuntu 18.04",[4506],"not_reported (average over keyframes)",[38],[40],[4510,4511,4512],"DSO pick","LDSO pick","Random pick",[1507],{"slug":4515,"sourceId":4516,"sourceLabel":4517,"sourceYear":16,"table":91,"note":4518,"datasets":4519,"metrics":4521,"families":4522,"methods":4523,"methodIds":4525,"rows":120,"failures":30},"gawel2019fabricatorloc-table-i","gawel2019fabricatorloc","Gawel et al., 2019","Autonomous approach from random locations several metres away, HAL with three laser distance sensors, then marking a 50 mm spaced 3 x 3 dot pattern on…",[4520],"authors' construction-like test site",[23],[23],[4524],"proposed system (LiDAR-to-model ICP + MHE + HAL + whole-body MPC)",[4516],{"slug":4527,"sourceId":4516,"sourceLabel":4517,"sourceYear":16,"table":4528,"note":4529,"datasets":4530,"metrics":4531,"families":4532,"methods":4533,"methodIds":4535,"rows":154,"failures":154},"gawel2019fabricatorloc-text-sec-iii","Text Sec.III","Rate of the whole-body MPC that generates base and end-effector reference trajectories",[3980],[148],[40],[4534],"whole-body MPC (OCS2)",[4516],{"slug":4537,"sourceId":4538,"sourceLabel":4539,"sourceYear":4540,"table":4541,"note":4542,"datasets":4543,"metrics":4545,"families":4546,"methods":4547,"methodIds":4558,"rows":578,"failures":30},"stereoscan2011-fig-6-tables","stereoscan2011","Geiger et al., 2011",2011,"Fig. 6 tables","Timing tables embedded in Fig. 6 for the online setting (2 scales, NMS neighbourhood 3, about 500 to 2000 feature matches, 50 RANSAC iterations); Karl…",[4544],"Karlsruhe stereo dataset",[38],[40],[4548,4549,4550,4551,4552,4553,4554,4555,4556,4557],"StereoScan feature matching (Filter)","StereoScan feature matching (Matching 1)","StereoScan feature matching (Matching 2)","StereoScan feature matching (NMS)","StereoScan feature matching (Refinement)","StereoScan feature matching (Total time)","StereoScan visual odometry (Kalman filter)","StereoScan visual odometry (RANSAC)","StereoScan visual odometry (Refinement)","StereoScan visual odometry (Total time)",[],{"slug":4560,"sourceId":4538,"sourceLabel":4539,"sourceYear":4540,"table":4528,"note":4561,"datasets":4562,"metrics":4563,"families":4564,"methods":4565,"methodIds":4569,"rows":274,"failures":154},"stereoscan2011-text-sec-iii","Throughput of the two worker threads stated in the abstract and Sec. III",[4544],[148,23],[40,23],[4566,4567,4568],"StereoScan odometry thread","StereoScan point fusion","StereoScan stereo thread (ELAS)",[],{"slug":4571,"sourceId":4538,"sourceLabel":4539,"sourceYear":4540,"table":1193,"note":4572,"datasets":4573,"metrics":4574,"families":4575,"methods":4576,"methodIds":4579,"rows":63,"failures":30},"stereoscan2011-text-sec-iv-b","Visual odometry runtime for 200 feature matches as stated in the text",[4544],[38],[40],[4577,4578],"Kitt et al. 2010 (CVMLIB-based version of the algorithm in [16])","StereoScan visual odometry",[],{"slug":4581,"sourceId":4582,"sourceLabel":4583,"sourceYear":1111,"table":4020,"note":4584,"datasets":4585,"metrics":4587,"families":4588,"methods":4589,"methodIds":4591,"rows":63,"failures":30},"geiger2012kitti-text-sec-3-3","geiger2012kitti","Geiger et al., 2012","Average over all sub-sequences of the VO benchmark; methods without loop closure; other methods shown only in Fig. 5 curves.",[4586],"KITTI odometry (2012 benchmark)",[438,23],[441,23],[4590],"VISO2-S",[],{"slug":4593,"sourceId":4594,"sourceLabel":4595,"sourceYear":538,"table":4596,"note":4597,"datasets":4598,"metrics":4600,"families":4601,"methods":4602,"methodIds":4605,"rows":63,"failures":154},"gelfand2003stable-fig-10-caption","gelfand2003stable","Gelfand et al., 2003","Fig. 10 caption","Residual error in the overlap of the pair in Fig. 9 after all views were processed by Pulli's global relaxation",[4599],"Forma Urbis Romae",[23],[23],[4603,4604],"pairwise alignment with geometrically stable sampling","pairwise alignment with uniform sampling",[4594],{"slug":4607,"sourceId":4594,"sourceLabel":4595,"sourceYear":538,"table":4608,"note":4609,"datasets":4610,"metrics":4612,"families":4613,"methods":4614,"methodIds":4617,"rows":63,"failures":30},"gelfand2003stable-fig-4-caption","Fig. 4 caption","Two synthetic planar patches with 1 mm deep X-shaped grooves, independent zero-mean Gaussian noise (variance 0.05 mm)",[4611],"synthetic incised plane",[23],[23],[4615,4616],"covariance (stable) sampling, 30% of points selected","initial (before sample selection)",[4594],{"slug":4619,"sourceId":4594,"sourceLabel":4595,"sourceYear":538,"table":4620,"note":4621,"datasets":4622,"metrics":4624,"families":4625,"methods":4626,"methodIds":4627,"rows":63,"failures":30},"gelfand2003stable-fig-6-caption","Fig. 6 caption","Two synthetic spherical patches with 1 mm deep grooves and 0.05 mm noise variance; three unstable rotations",[4623],"synthetic incised sphere",[23],[23],[4615,4616],[4594],{"slug":4629,"sourceId":4594,"sourceLabel":4595,"sourceYear":538,"table":3794,"note":4630,"datasets":4631,"metrics":4632,"families":4633,"methods":4634,"methodIds":4637,"rows":63,"failures":30},"gelfand2003stable-text-sec-4","Per-iteration cost relative to ICP with uniform sampling when meshes overlap by half their area",[2070],[23],[23],[4635,4636],"stable sampling with delayed overlap test","stable sampling with mesh or k-d tree crawling from seed points",[4594],{"slug":4639,"sourceId":4594,"sourceLabel":4595,"sourceYear":538,"table":4640,"note":4641,"datasets":4642,"metrics":4643,"families":4644,"methods":4645,"methodIds":4648,"rows":356,"failures":154},"gelfand2003stable-text-sec-5","Text Sec. 5","Two scans of Forma Urbis Romae fragment 033abc, about 300,000 points per mesh, 10% of points subsampled, started from rough manual positioning",[4599],[23],[23],[4646,4647],"ICP with geometrically stable sampling","ICP with uniform sampling",[4594],{"slug":4650,"sourceId":4651,"sourceLabel":4652,"sourceYear":107,"table":325,"note":4653,"datasets":4654,"metrics":4656,"families":4657,"methods":4658,"methodIds":4661,"rows":618,"failures":30},"lips2018-table-ii","lips2018","Geneva et al., 2018","Average RMSE over 80 Monte-Carlo simulations on the 180 m simulated indoor trajectory; known plane correspondences; iSAM2; closest-point (CP) versus r…",[4655],"LIPS simulator (extruded floor plan)",[568,23],[25,23],[4659,4660],"Closest Point","Quaternion [6] (Kaess relative quaternion factor)",[4651],{"slug":4663,"sourceId":4651,"sourceLabel":4652,"sourceYear":107,"table":4664,"note":4665,"datasets":4666,"metrics":4668,"families":4669,"methods":4670,"methodIds":4672,"rows":154,"failures":30},"lips2018-text-sec-vi-d","Text Sec.VI-D","Real sensor unit moved in front of planar boards and returned to the start; difference between start and end poses after a 30 m path",[4667],"authors' real-world test (planar boards)",[1551],[1553],[4671],"LIPS",[4651],{"slug":4674,"sourceId":4675,"sourceLabel":4676,"sourceYear":213,"table":91,"note":4677,"datasets":4678,"metrics":4680,"families":4681,"methods":4682,"methodIds":4687,"rows":29,"failures":30},"openvins2020-table-i","openvins2020","Geneva et al., 2020","Simulation, average over twenty Monte Carlo runs; true or bad initial calibration, with or without online calibration; monocular FEJ-MSCKF with SLAM l…",[4679],"OpenVINS simulator",[22,23],[25,23],[4683,4684,4685,4686],"OpenVINS (bad calibration, online calibration off)","OpenVINS (bad calibration, online calibration on)","OpenVINS (true calibration, online calibration off)","OpenVINS (true calibration, online calibration on)",[],{"slug":4689,"sourceId":4675,"sourceLabel":4676,"sourceYear":213,"table":325,"note":4690,"datasets":4691,"metrics":4692,"families":4693,"methods":4694,"methodIds":4708,"rows":4709,"failures":30},"openvins2020-table-ii","EuRoC MAV Vicon-room sequences, mean ATE over ten runs per method (orientation deg \u002F position m); VIO outputs only; V2_03 excluded; alignment method n…",[743],[22,23],[25,23],[4695,4696,4697,4698,4699,4700,4701,4702,4703,4704,4705,4706,4707],"mono okvis","mono ov slam","mono ov vio","mono rovioli (ROVIO in maplab)","mono rvio (R-VIO)","mono vinsfusion vio","stereo basalt (VIO)","stereo iceba (ICE-BA)","stereo okvis","stereo ov slam","stereo ov vio","stereo smsckf (S-MSCKF)","stereo vinsfusion vio",[1510,1513],130,{"slug":4711,"sourceId":4675,"sourceLabel":4676,"sourceYear":213,"table":1430,"note":4712,"datasets":4713,"metrics":4714,"families":4715,"methods":4716,"methodIds":4717,"rows":356,"failures":30},"openvins2020-text-sec-v-b","Real-time factor on the first EuRoC sequence, single-threaded Intel Xeon E3-1505M v6 at 3.00 GHz",[743],[23],[23],[4696,4697,4704,4705],[],{"slug":4719,"sourceId":4720,"sourceLabel":4721,"sourceYear":562,"table":108,"note":4722,"datasets":4723,"metrics":4725,"families":4726,"methods":4727,"methodIds":4748,"rows":608,"failures":608},"ghadimzadeh2025slamnde-table-2","ghadimzadeh2025slamnde","Ghadimzadeh Alamdari et al., 2025","Run outcome ('Result' column) of each reviewed vision-based method on the Luleå tunnel test dataset; '+' marks methods not integrated with ROS; the '*…",[4724],"Luleå SubT tunnel dataset (Koval et al. 2022)",[23],[23],[4728,4729,4730,4731,4732,4733,2924,4734,4735,4736,2803,4737,4738,4739,2805,4740,4741,4742,4743,4744,4745,1606,4746,4747],"DSO","DTAM","Dense visual SLAM","Elastic Fusion SLAM","Kimera","Kinetic Fusion","MSCKF","Mono-SLAM","Multi-sensor fusion","ORB-SLAM (footnote 1)","OV2SLAM","PTAM","Realtime onboard VI estimation","S-MSCKF","S-PTAM","SOFT-SLAM","STCM-SLAM","SVO","VIORB","Yolo-SLAM",[1507,4749,2915,1508,4750,2881,2971,1509,1510,119,3866,1512,251],"dtam2011","kinectfusion2011",{"slug":4752,"sourceId":4720,"sourceLabel":4721,"sourceYear":562,"table":17,"note":4753,"datasets":4754,"metrics":4755,"families":4756,"methods":4757,"methodIds":4782,"rows":4788,"failures":4788},"ghadimzadeh2025slamnde-table-3","Run outcome ('Result' column) of each reviewed LiDAR-based and combined method on the Luleå tunnel test dataset; '*' marks incompatible with VLP-16, '…",[4724],[23],[23],[4758,4759,4760,4761,4762,4048,4763,4764,4765,4049,4766,4767,587,4050,4768,4769,334,4770,4771,4772,4773,4774,2380,2197,335,4775,4169,4776,4777,4778,4779,2381,4780,3583,1976,1977,4781],"CamVox","Cartographer","D-LIOM","DV-LOAM","DVL-SLAM","FAST-LIVO(s)","Fast-LIO 1","Fast-LIO 2 and SC-Fast-LIO 2","Hand-held mobile mapping","HectorGrapher","LIMO","LINS","LIOM","LOAM and A-LOAM","LOAM-Livox","LOCUS and LOCUS 2","LOL","M-LOAM","Multiverse Odometry","Optimized-SC-F-LOAM","PIN-SLAM","R2LIVE","SC-LeGO-LOAM","Super Odometry",[392,2116,1476,321,815,393,394,395,337,4783,2117,338,4784,2386,597,4785,4786,2387,432,1968,4787],"lins2020","loamlivox2020","pinslam2024","r2live2021","superodom2021",37,{"slug":4790,"sourceId":4720,"sourceLabel":4721,"sourceYear":562,"table":4791,"note":4792,"datasets":4793,"metrics":4794,"families":4795,"methods":4796,"methodIds":4798,"rows":416,"failures":30},"ghadimzadeh2025slamnde-text-sec-7-1-2","Text Sec.7.1.2","Trajectory RMSE on the Luleå tunnel B-to-C path (181 s) against UWB ground truth available at 0-30 s and 160-181 s; values stated in text, full set on…",[4724],[568,219],[25,40],[4797,334,2380,1606],"Fast-LIO 2",[321,338,2386,251],{"slug":4800,"sourceId":4801,"sourceLabel":4802,"sourceYear":4803,"table":633,"note":4804,"datasets":4805,"metrics":4807,"families":4808,"methods":4809,"methodIds":4815,"rows":578,"failures":30},"girardeaumontaut2005c2c-table-8","girardeaumontaut2005c2c","Girardeau-Montaut et al., 2005",2005,"Indicative computation time per process; numbers in braces in the table are octree levels chosen to give about the same number of points per cell.",[4806],"authors' TLS data",[23],[23],[4810,4811,4812,4813,4814],"octree computation","strategy 1 'average distance'","strategy 2 'best fitting plane orientation', one pass","strategy 2 'best fitting plane orientation', recursive","strategy 3 'Hausdorff distance' (nearest-neighbour C2C)",[4801],{"slug":4817,"sourceId":4801,"sourceLabel":4802,"sourceYear":4803,"table":644,"note":4818,"datasets":4819,"metrics":4820,"families":4821,"methods":4822,"methodIds":4823,"rows":641,"failures":416},"girardeaumontaut2005c2c-table-9","Hausdorff (nearest-neighbour) distance computation time as a function of octree level; n.a. cells printed without values.",[4806],[23],[23],[4814],[4801],{"slug":4825,"sourceId":4826,"sourceLabel":4827,"sourceYear":4828,"table":254,"note":4829,"datasets":4830,"metrics":4832,"families":4833,"methods":4834,"methodIds":4836,"rows":63,"failures":63},"glennie2016vlp16-text-sec-4-1","glennie2016vlp16","Glennie et al., 2016",2016,"Warm-up test: units frozen overnight, then ranged to flat walls about 5 m away until internal temperature reached 40 deg C; residuals to best-fit plan…",[4831],"authors' temperature test",[23],[23],[4835],"Velodyne VLP-16",[],{"slug":4838,"sourceId":4826,"sourceLabel":4827,"sourceYear":4828,"table":3687,"note":4839,"datasets":4840,"metrics":4842,"families":4843,"methods":4844,"methodIds":4849,"rows":356,"failures":154},"glennie2016vlp16-text-sec-4-2","Static planar calibration: 24 clouds from 2 stations, 125 common planes (about 200,000 points); RMSE of planar residuals per unit after each unit's ge…",[4841],"authors' static calibration data",[23,1830],[78,23],[4845,4846,4847,4848],"VLP-16 unit 1 after geometric calibration","VLP-16 unit 2 after geometric calibration","VLP-16 unit 3 after geometric calibration","planar geometric calibration (Skaloud and Lichti model)",[],{"slug":4851,"sourceId":4826,"sourceLabel":4827,"sourceYear":4828,"table":4852,"note":4853,"datasets":4854,"metrics":4856,"families":4857,"methods":4858,"methodIds":4860,"rows":63,"failures":30},"glennie2016vlp16-text-sec-4-2-fig-5","Text Sec. 4.2 (Fig. 5)","Range residuals per laser after static calibration, VLP-16 versus the first 16 lasers of an HDL-32E calibrated in Glennie et al. 2013",[4855],"authors' static calibration data; Glennie et al. 2013",[1830],[78],[4859,4835],"Velodyne HDL-32E (first 16 lasers)",[],{"slug":4862,"sourceId":4826,"sourceLabel":4827,"sourceYear":4828,"table":4863,"note":4864,"datasets":4865,"metrics":4867,"families":4868,"methods":4869,"methodIds":4870,"rows":63,"failures":63},"glennie2016vlp16-text-sec-4-3","Text Sec. 4.3","Long-term static test: three about 3 h data sets per unit, ranges to a flat target about 4 m away extracted once per minute, 20 deg C ambient",[4866],"authors' long-term static data",[23],[23],[4835],[],{"slug":4872,"sourceId":4873,"sourceLabel":4874,"sourceYear":1694,"table":1072,"note":4875,"datasets":4876,"metrics":4878,"families":4879,"methods":4880,"methodIds":4883,"rows":3323,"failures":30},"glennie2007rigorous-table-6","glennie2007rigorous","Glennie, 2007","ALTMS fixed-wing LiDAR at 1000 m AGL, 16 flight lines over 16 airport targets; vertical from TIN of ground returns, horizontal from digitized 1 m inte…",[4877],"Terrapoint production test, early 2006",[75],[78],[4881,4882],"1st-order error model (expected)","ALTMS data versus ground control",[4873],{"slug":4885,"sourceId":4873,"sourceLabel":4874,"sourceYear":1694,"table":621,"note":4886,"datasets":4887,"metrics":4889,"families":4890,"methods":4891,"methodIds":4893,"rows":356,"failures":30},"glennie2007rigorous-table-7","Helicopter system (Riegl Q-140, Honeywell HG1700) at 100 m AGL over a calibration site flown in four directions; RMS misclosure of 115 tie points afte…",[4888],"Terrapoint helicopter boresight adjustment",[23],[23],[4881,4892],"helicopter LiDAR boresight residuals",[4873],{"slug":4895,"sourceId":4873,"sourceLabel":4874,"sourceYear":1694,"table":633,"note":4896,"datasets":4897,"metrics":4899,"families":4900,"methods":4901,"methodIds":4903,"rows":416,"failures":30},"glennie2007rigorous-table-8","Ground-based kinematic LiDAR (Honeywell HG1700 IMU), vertical comparison with dense ground control at 10 to 25 m range, repeated from Glennie et al. 2…",[4898],"Glennie et al. 2006 ground system test",[75],[78],[4902],"ground-based LiDAR versus ground control",[],{"slug":4905,"sourceId":4873,"sourceLabel":4874,"sourceYear":1694,"table":4906,"note":4907,"datasets":4908,"metrics":4909,"families":4910,"methods":4911,"methodIds":4912,"rows":154,"failures":154},"glennie2007rigorous-text-comparison-ground-based-system","Text Comparison: Ground based system","Model prediction for the ground system of Table 8",[4898],[75],[78],[4881],[4873],{"slug":4914,"sourceId":4915,"sourceLabel":4916,"sourceYear":1111,"table":33,"note":4917,"datasets":4918,"metrics":4920,"families":4921,"methods":4922,"methodIds":4926,"rows":52,"failures":30},"glennie2012hdl64-table-4","glennie2012hdl64","Glennie, 2012","Planar misclosure over 75 planes (about 750 returns each, 5 to 100 m range) from one kinematic dataset; columns: factory interior calibration with glo…",[4919],"authors' parking-lot calibration dataset (23 March 2010)",[1830],[78],[4923,4924,4925],"Global boresight only","Kinematic Calibration","Kinematic Calibration with H_o^i from Static Analysis (text: horizontal rotation correction from static calibration)",[4915],{"slug":4928,"sourceId":4915,"sourceLabel":4916,"sourceYear":1111,"table":244,"note":4929,"datasets":4930,"metrics":4931,"families":4932,"methods":4933,"methodIds":4934,"rows":52,"failures":30},"glennie2012hdl64-table-5","Same planar misclosure statistics as Table 4 but limited to points within 25 m of the scanner",[4919],[1830],[78],[4923,4924,4925],[4915],{"slug":4936,"sourceId":4915,"sourceLabel":4916,"sourceYear":1111,"table":621,"note":4937,"datasets":4938,"metrics":4940,"families":4941,"methods":4942,"methodIds":4947,"rows":356,"failures":30},"glennie2012hdl64-table-7","Planar RMSE residuals for points within 25 m; Riegl datasets collected at other times on the same platform, trajectory and similar GPS constellation,…",[4939],"authors' parking-lot calibration datasets",[1830],[78],[4943,4944,4945,4946],"Riegl LMS-Q120i (boresight only)","Riegl VZ-400 (boresight only)","Velodyne Kinematic Calibration","Velodyne Kinematic Calibration H From Static Calibration",[4915],{"slug":4949,"sourceId":4915,"sourceLabel":4916,"sourceYear":1111,"table":4950,"note":4951,"datasets":4952,"metrics":4953,"families":4954,"methods":4955,"methodIds":4957,"rows":154,"failures":154},"glennie2012hdl64-text-comparison-section","Text Comparison section","Effect of removing returns with incidence angle above 70 deg on the overall planar misclosure (all ranges, full calibration with static correction)",[4919],[1830],[78],[4956],"Kinematic calibration with static horizontal correction, high-incidence points removed",[4915],{"slug":4959,"sourceId":4960,"sourceLabel":4961,"sourceYear":107,"table":539,"note":4962,"datasets":4963,"metrics":4964,"families":4965,"methods":4966,"methodIds":4969,"rows":578,"failures":30},"limo2018-text-sec-vi","limo2018","Graeter et al., 2018","KITTI odometry benchmark evaluation set results as published on the official server (as of 1 March 2018); official KITTI metric; Liviodo is the frame-…",[469],[438,148,23,38],[40,441,23],[4768,4967,4968],"Liviodo (frame-to-frame motion only)","ResNet38 semantic segmentation (component of LIMO)",[4960],{"slug":4971,"sourceId":4972,"sourceLabel":4973,"sourceYear":1694,"table":325,"note":4974,"datasets":4975,"metrics":4979,"families":4980,"methods":4981,"methodIds":4984,"rows":120,"failures":30},"gmapping2007-table-ii","gmapping2007","Grisetti et al., 2007","Number of particles an RBPF needs to produce a topologically correct map in at least 60% of runs, informed proposal vs the proposal of Haehnel et al.…",[4976,4977,4978],"Freiburg Campus","Intel Research Lab","MIT Killian Court",[23],[23],[4982,4983],"approach of [16] (Haehnel et al.)","our approach",[4972],{"slug":4986,"sourceId":4972,"sourceLabel":4973,"sourceYear":1694,"table":279,"note":4987,"datasets":4988,"metrics":4989,"families":4990,"methods":4991,"methodIds":4993,"rows":274,"failures":30},"gmapping2007-table-iii","Average execution time per operation on a standard PC (2.8 GHz), default parameters, 30 particles, update every 0.5 m or 25 deg; Intel Research Lab lo…",[4977],[23],[23],[4992],"improved RBPF (30 particles)",[4972],{"slug":4995,"sourceId":4972,"sourceLabel":4973,"sourceYear":1694,"table":4996,"note":4997,"datasets":4998,"metrics":5000,"families":5001,"methods":5002,"methodIds":5005,"rows":63,"failures":30},"gmapping2007-text-sec-vi-d","Text Sec. VI-D","Simulated short-range laser (4 m maximum range) in a corridor; share of runs producing a topologically correct map, with vs without odometry in the pr…",[4999],"simulated corridor trajectories",[1933],[1935],[5003,5004],"previous approach [14] without odometry in the proposal","proposal considering odometry (this paper)",[4972],{"slug":5007,"sourceId":4972,"sourceLabel":4973,"sourceYear":1694,"table":5008,"note":5009,"datasets":5010,"metrics":5011,"families":5012,"methods":5013,"methodIds":5014,"rows":63,"failures":154},"gmapping2007-text-sec-vi-f","Text Sec. VI-F","Intel Research Lab log (45 min), 30 particles, map about 40 m by 40 m at 5 cm resolution, standard PC 2.8 GHz",[4977],[219,23],[40,23],[4992],[4972],{"slug":5016,"sourceId":5017,"sourceLabel":5018,"sourceYear":1032,"table":1452,"note":5019,"datasets":5020,"metrics":5021,"families":5022,"methods":5023,"methodIds":5025,"rows":154,"failures":30},"grisetti2010tutorial-text-sec-v-a","grisetti2010tutorial","Grisetti et al., 2010","Final Intel Research Lab pose graph with 1,802 nodes and 3,546 edges, optimized on a standard laptop",[4977],[23],[23],[5024],"Gauss-Newton pose-graph optimization with sparse Cholesky (Algorithm 1)",[5017],{"slug":5027,"sourceId":5017,"sourceLabel":5018,"sourceYear":1032,"table":1464,"note":5028,"datasets":5029,"metrics":5031,"families":5032,"methods":5033,"methodIds":5036,"rows":63,"failures":63},"grisetti2010tutorial-text-sec-v-b","Simulated 3D robot moving on a sphere with significant measurement error, initialized from odometry; Gauss-Newton with Euler angles vs manifold linear…",[5030],"simulated sphere pose graph",[23],[23],[5034,5035],"Gauss-Newton with Euler angles (Algorithm 1)","Gauss-Newton with manifold linearization (Algorithm 2)",[5017],{"slug":5038,"sourceId":5039,"sourceLabel":5040,"sourceYear":345,"table":5041,"note":5042,"datasets":5043,"metrics":5044,"families":5045,"methods":5046,"methodIds":5049,"rows":356,"failures":30},"grupp2017evo-text-doc-performance-md","grupp2017evo","Grupp, 2017","Text doc\u002Fperformance.md","TUM fr2_desk ground truth (20957 poses) vs ORB trajectory, 2223 compared pairs for evaluate_ate.py; document says same settings; small numerical diffe…",[2872],[568,23],[25,23],[5047,5048],"evaluate_ate.py (TUM RGB-D tools)","evo_ape --align",[5039,5050],"sturm2012tum",{"slug":5052,"sourceId":5053,"sourceLabel":5054,"sourceYear":562,"table":91,"note":5055,"datasets":5056,"metrics":5057,"families":5058,"methods":5059,"methodIds":5061,"rows":416,"failures":154},"kissslam2025-table-i","kissslam2025","Guadagnino et al., 2025a","ATE from evo; the paired relative KITTI metric (%) is omitted; '-' means the run failed because errors exceeded a sequence-specific threshold (version…",[310],[329],[25],[3131,4169,5060,4778,1976],"Ours (KISS-SLAM)",[596,5053,597,4785,432],{"slug":5063,"sourceId":5053,"sourceLabel":5054,"sourceYear":562,"table":325,"note":5055,"datasets":5064,"metrics":5065,"families":5066,"methods":5067,"methodIds":5068,"rows":608,"failures":274},"kissslam2025-table-ii",[671],[329],[25],[3131,571,4169,5060,4778,1976],[596,577,5053,597,4785,432],{"slug":5070,"sourceId":5053,"sourceLabel":5054,"sourceYear":562,"table":279,"note":5055,"datasets":5071,"metrics":5073,"families":5074,"methods":5075,"methodIds":5076,"rows":3547,"failures":578},"kissslam2025-table-iii",[5072],"HeLiPR",[329],[25],[3131,4169,5060,4778,1976],[596,5053,597,4785,432],{"slug":5078,"sourceId":5053,"sourceLabel":5054,"sourceYear":562,"table":731,"note":5055,"datasets":5079,"metrics":5081,"families":5082,"methods":5083,"methodIds":5084,"rows":1339,"failures":416},"kissslam2025-table-iv",[5080],"Apollo",[329],[25],[3131,4169,5060,4778,1976],[596,5053,597,4785,432],{"slug":5086,"sourceId":5053,"sourceLabel":5054,"sourceYear":562,"table":818,"note":5055,"datasets":5087,"metrics":5088,"families":5089,"methods":5090,"methodIds":5091,"rows":1339,"failures":154},"kissslam2025-table-v",[3253],[329],[25],[3131,4169,5060,4778,1976],[596,5053,597,4785,432],{"slug":5093,"sourceId":5053,"sourceLabel":5054,"sourceYear":562,"table":838,"note":5094,"datasets":5095,"metrics":5097,"families":5098,"methods":5099,"methodIds":5102,"rows":578,"failures":30},"kissslam2025-table-vii","2D Monte-Carlo localization (RVP-Loc, Clearpath Dingo with SICK TiM781S) on a 2D map sliced from the KISS-SLAM 3D occupancy grid versus a GMapping map…",[5096],"authors' office sequences",[568],[25],[5100,5101],"GMapping map","Ours (KISS-SLAM map)",[4972,5053],{"slug":5104,"sourceId":5105,"sourceLabel":5106,"sourceYear":562,"table":325,"note":5107,"datasets":5108,"metrics":5110,"families":5111,"methods":5112,"methodIds":5120,"rows":5121,"failures":30},"kinematicicp2025-table-ii","kinematicicp2025","Guadagnino et al., 2025b","RPE is the KITTI average translation error over 1, 2, 5, 10, 20, 50 and 100 m segments (%); ATE is RMS absolute translation error after alignment (m);…",[5109],"authors' warehouse and campus sequences",[568,438],[25,441],[5113,5114,5115,5116,5117,5118,5119],"EKF (robot_localization fusing WO + 2D KISS-ICP)","Fuse (fixed-lag smoother fusing WO + 2D KISS-ICP)","KISS-ICP [31]","Kinematic-ICP","WO + 2D KISS-ICP","WO + 3D KISS-ICP","Wheel Odometry",[5105,577],98,{"slug":5123,"sourceId":5105,"sourceLabel":5106,"sourceYear":562,"table":279,"note":5124,"datasets":5125,"metrics":5126,"families":5127,"methods":5128,"methodIds":5136,"rows":429,"failures":30},"kinematicicp2025-table-iii","Ablation on regularization of the wheel-odometry translation prior; all rows are Kinematic-ICP variants",[5109],[568,438],[25,441],[5129,5130,5131,5132,5133,5134,5135],"Fixed beta = 0.01","Fixed beta = 0.1","Fixed beta = 1.0","Fixed beta = 10.0","Fixed beta = 100.0","Kinematic-ICP (adaptive regularization)","No Regularization",[5105],{"slug":5138,"sourceId":5105,"sourceLabel":5106,"sourceYear":562,"table":5139,"note":5140,"datasets":5141,"metrics":5143,"families":5144,"methods":5145,"methodIds":5147,"rows":63,"failures":30},"kinematicicp2025-text-sec-v-c","Text Sec. V-C","Runtime comparison stated in text; Fuse value given as approximately 10 Hz",[5142],"authors' sequences",[148],[40],[5146,5116],"Fuse",[5105],{"slug":5149,"sourceId":5150,"sourceLabel":5151,"sourceYear":5152,"table":5153,"note":5154,"datasets":5155,"metrics":5157,"families":5158,"methods":5159,"methodIds":5163,"rows":274,"failures":274},"gutmann-konolige1999-lrgc-text-sec-2-2","gutmann_konolige1999_lrgc","Gutmann & Konolige, 1999",1999,"Text Sec. 2.2","Local registration over the last K poses versus update of all poses on a 150-pose map (about 0.3 m between poses); pose error from incremental differe…",[5156,2070],"150-pose map (source log not identified in the paper; about 0.3 m between poses)",[23,38],[40,23],[5160,5161,5162],"LRGC local registration","LRGC local registration (K >= 7)","LRGC loop closing",[5150],{"slug":5165,"sourceId":5166,"sourceLabel":5167,"sourceYear":2267,"table":69,"note":5168,"datasets":5169,"metrics":5170,"families":5171,"methods":5172,"methodIds":5181,"rows":1790,"failures":618},"gsicpslam2024-table-1","gsicpslam2024","Ha et al., 2024","Replica ATE RMSE; * = reproduced with official code; GS-SLAM from its paper, Photo-SLAM only average from its paper",[3223],[568],[25],[5173,5174,5175,5176,5177,5178,5179,5180],"GS-SLAM [ 44 ]","Gaussian Splatting SLAM* [22] (ECCV version only)","NICE-SLAM* [ 47 ]","ORB-SLAM3 [4] (ECCV version only)","Ours (limited to 30 FPS)","Photo-SLAM [ 12 ]","Point-SLAM* [ 32 ]","SplaTAM* [ 14 ]",[3183,3184,763,5182,3185,3186],"photoslam2024",{"slug":5184,"sourceId":5166,"sourceLabel":5167,"sourceYear":2267,"table":108,"note":5185,"datasets":5186,"metrics":5187,"families":5188,"methods":5189,"methodIds":5191,"rows":2882,"failures":30},"gsicpslam2024-table-2","TUM RGB-D ATE RMSE; decoupled (ORB-SLAM3, Photo-SLAM) versus coupled single-map systems; * = reproduced with official code",[2872],[568],[25],[5173,5174,5175,5190,5177,5178,5179,5180],"ORB-SLAM3 [ 4 ]",[3183,3184,763,5182,3185,3186],{"slug":5193,"sourceId":5166,"sourceLabel":5167,"sourceYear":2267,"table":17,"note":5194,"datasets":5195,"metrics":5196,"families":5197,"methods":5198,"methodIds":5202,"rows":429,"failures":154},"gsicpslam2024-table-3","Replica rendering quality and whole-system FPS (total frames divided by total time), averaged over 8 scenes; per-scene values not extracted; * = repro…",[3223],[148,23],[40,23],[5173,5199,5200,5177,5201,5179,5180],"Gaussian Splatting SLAM [22] (ECCV version only)","Orbeez-SLAM* [ 8 ]","Ours (no tracking speed limit)",[3183,3185,3186],{"slug":5204,"sourceId":5166,"sourceLabel":5167,"sourceYear":2267,"table":33,"note":5205,"datasets":5206,"metrics":5207,"families":5208,"methods":5209,"methodIds":5211,"rows":608,"failures":154},"gsicpslam2024-table-4","TUM RGB-D rendering quality and whole-system FPS (one value per method; the sequences are not stated); Photo-SLAM from its paper, system FPS not repor…",[2872],[148,23],[40,23],[5175,5177,5210,5178,5179,5180],"Ours (unlimited tracking speed)",[3184,5182,3185,3186],{"slug":5213,"sourceId":5166,"sourceLabel":5167,"sourceYear":2267,"table":5214,"note":5215,"datasets":5216,"metrics":5217,"families":5218,"methods":5219,"methodIds":5221,"rows":274,"failures":30},"gsicpslam2024-text-supp-sec-c-2","Text Supp. Sec. C.2","Geometric quality reported in the ECCV supplementary as average depth L1 error of rendered depth over all scenes (maximum scene depth 5.5 m on Replica…",[3223,2872],[23],[23],[5220,81],"GS-SLAM [12]",[],{"slug":5223,"sourceId":5224,"sourceLabel":5225,"sourceYear":107,"table":91,"note":5226,"datasets":5227,"metrics":5228,"families":5229,"methods":5230,"methodIds":5236,"rows":1042,"failures":30},"flashfusion2018-table-i","flashfusion2018","Han & Fang, 2018","Localization accuracy on TUM RGB-D as ATE RMSE (Sturm et al.) in cm; alignment not stated",[2872],[568],[25],[5231,5232,5233,5234,5235],"BundleFusion (off-line)","BundleFusion (on-line)","ElasticFusion","FlashFusion","RGBD SLAM [3] (Endres et al.)",[2897,2915,5224],{"slug":5238,"sourceId":5224,"sourceLabel":5225,"sourceYear":107,"table":325,"note":5239,"datasets":5240,"metrics":5241,"families":5242,"methods":5243,"methodIds":5244,"rows":1042,"failures":30},"flashfusion2018-table-ii","Localization accuracy on ICL-NUIM (with noise) as ATE RMSE in cm; alignment not stated",[2870],[568],[25],[5231,5232,5233,5234,5235],[2897,2915,5224],{"slug":5246,"sourceId":5224,"sourceLabel":5225,"sourceYear":107,"table":279,"note":5247,"datasets":5248,"metrics":5249,"families":5250,"methods":5251,"methodIds":5255,"rows":608,"failures":30},"flashfusion2018-table-iii","Surface reconstruction accuracy on ICL-NUIM (with noise): difference between reconstructed meshes and the ground-truth model computed with the SurfReg…",[2870],[75],[78],[5252,5233,5253,5234,5254,5235],"BundleFusion","FastFusion [17] (Steinbruecker et al.)","InfiniTAM [10] (Kaehler et al. ECCV 2016)",[2897,2915,5224],{"slug":5257,"sourceId":5224,"sourceLabel":5225,"sourceYear":107,"table":731,"note":5258,"datasets":5259,"metrics":5260,"families":5261,"methods":5262,"methodIds":5264,"rows":224,"failures":30},"flashfusion2018-table-iv","Efficiency comparison between CPU-based CHISEL and FlashFusion on TUM fr3\u002Foffice at three voxel resolutions; time per operation in ms",[2872],[38],[40],[5263,5234],"CHISEL",[5265,5224],"chisel2015",{"slug":5267,"sourceId":5224,"sourceLabel":5225,"sourceYear":107,"table":5268,"note":5269,"datasets":5270,"metrics":5271,"families":5272,"methods":5273,"methodIds":5275,"rows":63,"failures":30},"flashfusion2018-text-sec-iv-b2","Text Sec.IV-B2","Sparse voxel sampling versus conventional candidate-chunk selection on fr3\u002Foffice, averaged over frames",[2872],[23],[23],[5274],"FlashFusion sparse voxel sampling",[5224],{"slug":5277,"sourceId":5224,"sourceLabel":5225,"sourceYear":107,"table":5278,"note":5279,"datasets":5280,"metrics":5281,"families":5282,"methods":5283,"methodIds":5286,"rows":63,"failures":30},"flashfusion2018-text-sec-iv-b3","Text Sec.IV-B3","Text statements on FlashFusion component rates and times",[2070],[38],[40],[5284,5285],"FlashFusion (TSDF integration)","FlashFusion (valid chunk selection)",[5224],{"slug":5288,"sourceId":5224,"sourceLabel":5225,"sourceYear":107,"table":1430,"note":5279,"datasets":5289,"metrics":5290,"families":5291,"methods":5292,"methodIds":5296,"rows":274,"failures":30},"flashfusion2018-text-sec-v-b",[2872,2070],[148,38],[40],[5293,5294,5295],"FastFusion [17]","FlashFusion (meshing)","FlashFusion (tracking thread)",[5224],{"slug":5298,"sourceId":5224,"sourceLabel":5225,"sourceYear":107,"table":539,"note":5279,"datasets":5299,"metrics":5300,"families":5301,"methods":5302,"methodIds":5304,"rows":63,"failures":30},"flashfusion2018-text-sec-vi",[2070],[148],[40],[5303],"FlashFusion (TSDF fusion)",[5224],{"slug":5306,"sourceId":5307,"sourceLabel":5308,"sourceYear":68,"table":33,"note":5309,"datasets":5310,"metrics":5312,"families":5313,"methods":5314,"methodIds":5319,"rows":224,"failures":30},"han2026nifcyl-table-4","han2026nifcyl","Han et al., 2026","Synthetic deformation on 5 regions (88,842 points) of a real mine tunnel cloud; M3C2 at three normal-scale ranges with empty values excluded",[5311],"synthetic deformation benchmark from Hovermap ST scan",[23],[23],[5315,5316,5317,5318],"M3C2 large scale (1.05 to 1.55 m)","M3C2 medium scale (0.55 to 1.05 m)","M3C2 small scale (0.05 to 0.55 m)","NIFCyl",[5307,5320],"lague2013m3c2",{"slug":5322,"sourceId":5307,"sourceLabel":5308,"sourceYear":68,"table":244,"note":5323,"datasets":5324,"metrics":5326,"families":5327,"methods":5328,"methodIds":5329,"rows":618,"failures":30},"han2026nifcyl-table-5","Robustness of NIFCyl on the synthetic dataset under random point removal and added zero-mean Gaussian noise (sigma as % of deformation)",[5325],"synthetic deformation benchmark",[23],[23],[5318],[5307],{"slug":5331,"sourceId":5307,"sourceLabel":5308,"sourceYear":68,"table":1072,"note":5332,"datasets":5333,"metrics":5336,"families":5337,"methods":5338,"methodIds":5339,"rows":52,"failures":154},"han2026nifcyl-table-6","Deformation residuals of NIFCyl in undeformed regions (Gaussian fit); synthetic N=113,059, experimental N=121,151, real N=104,031",[5334,5335,5325],"own field data, Kalgoorlie underground mine","own field test, Kalgoorlie underground mine",[23],[23],[5318],[5307],{"slug":5341,"sourceId":5307,"sourceLabel":5308,"sourceYear":68,"table":5342,"note":5343,"datasets":5344,"metrics":5345,"families":5346,"methods":5347,"methodIds":5349,"rows":356,"failures":30},"han2026nifcyl-text-sec-4-4","Text Sec.4.4","Field test with two scans within half an hour; paper box (9.5 cm) on flat cardboard and box over half basketball on cardboard; Sec. 4.4 does not state…",[5335],[23],[23],[5348,5318],"M3C2",[5307,5320],{"slug":5351,"sourceId":5307,"sourceLabel":5308,"sourceYear":68,"table":55,"note":5352,"datasets":5353,"metrics":5354,"families":5355,"methods":5356,"methodIds":5357,"rows":154,"failures":154},"han2026nifcyl-text-sec-4-5","Real deformation after two months (scaling work); single manual centre-to-centre measure of 2.513 m between flat regions A and B",[5334],[23],[23],[5318],[5307],{"slug":5359,"sourceId":5360,"sourceLabel":5361,"sourceYear":921,"table":325,"note":5362,"datasets":5363,"metrics":5364,"families":5365,"methods":5366,"methodIds":5369,"rows":1042,"failures":30},"handa2014iclnuim-table-ii","handa2014iclnuim","Handa et al., 2014","Surface reconstruction error, noise-free living room: CloudCompare cloud\u002Fmesh distance after manual coarse alignment and ICP fine alignment to the den…",[2870],[75],[78],[5367,5368],"Kintinuous pipeline with DVO odometry [13]","Kintinuous pipeline with ICP odometry (as in KinectFusion and Kintinuous [3], [4])",[2448],{"slug":5371,"sourceId":5360,"sourceLabel":5361,"sourceYear":921,"table":279,"note":5372,"datasets":5373,"metrics":5374,"families":5375,"methods":5376,"methodIds":5380,"rows":1042,"failures":30},"handa2014iclnuim-table-iii","ATE of five odometers inside the Kintinuous pipeline on the noise-free living-room sequences; RMSE row per Sturm et al. [7], [8]; alignment procedure…",[2870],[568],[25],[5367,5377,5368,5378,5379],"Kintinuous pipeline with FOVIS odometry [14]","Kintinuous pipeline with ICP+RGB-D odometry [16]","Kintinuous pipeline with RGB-D odometry [15]",[2448],{"slug":5382,"sourceId":5360,"sourceLabel":5361,"sourceYear":921,"table":731,"note":5383,"datasets":5384,"metrics":5385,"families":5386,"methods":5387,"methodIds":5388,"rows":1042,"failures":30},"handa2014iclnuim-table-iv","ATE of five odometers inside the Kintinuous pipeline on the noise-free office-room sequences; RMSE row; alignment not stated",[2870],[568],[25],[5367,5377,5368,5378,5379],[2448],{"slug":5390,"sourceId":5360,"sourceLabel":5361,"sourceYear":921,"table":818,"note":5391,"datasets":5392,"metrics":5393,"families":5394,"methods":5395,"methodIds":5396,"rows":1042,"failures":30},"handa2014iclnuim-table-v","ATE of five odometers on the living-room sequences with simulated depth and RGB noise (Sec. V); RMSE row; alignment not stated",[2870],[568],[25],[5367,5377,5368,5378,5379],[2448],{"slug":5398,"sourceId":5360,"sourceLabel":5361,"sourceYear":921,"table":827,"note":5399,"datasets":5400,"metrics":5401,"families":5402,"methods":5403,"methodIds":5404,"rows":1042,"failures":30},"handa2014iclnuim-table-vi","ATE of five odometers on the office-room sequences with simulated depth and RGB noise; RMSE row; alignment not stated",[2870],[568],[25],[5367,5377,5368,5378,5379],[2448],{"slug":5406,"sourceId":5360,"sourceLabel":5361,"sourceYear":921,"table":838,"note":5407,"datasets":5408,"metrics":5409,"families":5410,"methods":5411,"methodIds":5412,"rows":29,"failures":30},"handa2014iclnuim-table-vii","Surface reconstruction error, living room with simulated noise, all using ICP odometry; same CloudCompare cloud\u002Fmesh procedure",[2870],[75],[78],[5368],[2448],{"slug":5414,"sourceId":5415,"sourceLabel":5416,"sourceYear":2267,"table":91,"note":5417,"datasets":5418,"metrics":5423,"families":5424,"methods":5425,"methodIds":5429,"rows":29,"failures":29},"hatleskog2024probdegen-table-i","hatleskog2024probdegen","Hatleskog & Alexis, 2024","Qualitative presence or absence of degeneracy-induced drift in partial maps; no numeric values",[5419,5420,5421,5422],"Fyllingsdalen Bicycle Tunnel","RelyOn Nutec","Rümlang Construction Site","Seemühle Mine",[23],[23],[5426,5427,81,5428],"Hinduja [15]","Lee [19] (Switch-SLAM)","Zhang [14]",[5415,5430,5431],"hinduja2019degeneracy","zhang2016degeneracy",{"slug":5433,"sourceId":5415,"sourceLabel":5416,"sourceYear":2267,"table":325,"note":5434,"datasets":5435,"metrics":5436,"families":5437,"methods":5438,"methodIds":5439,"rows":2882,"failures":30},"hatleskog2024probdegen-table-ii","Seemühle mine, ANYmal C with VLP-16 (360 deg FOV) and ROVIO prior; mean (SD) APE and RPE vs ground truth from the X-ICP procedure, computed with evo",[5422],[329,2411,330],[25,332],[5426,5427,81,5428],[5415,5430,5431],{"slug":5441,"sourceId":5415,"sourceLabel":5416,"sourceYear":2267,"table":5442,"note":5443,"datasets":5444,"metrics":5445,"families":5446,"methods":5447,"methodIds":5448,"rows":63,"failures":30},"hatleskog2024probdegen-text-sec-iv-d","Text Sec. IV-D","RelyOn tank, translational end-position error in height",[5420],[1551],[1553],[5426,5427],[5430],{"slug":5450,"sourceId":5415,"sourceLabel":5416,"sourceYear":2267,"table":5451,"note":5452,"datasets":5453,"metrics":5454,"families":5455,"methods":5456,"methodIds":5458,"rows":274,"failures":30},"hatleskog2024probdegen-text-sec-iv-e","Text Sec. IV-E","Fyllingsdalen tunnel, approximate values stated in text",[5419],[23],[23],[5426,81,5457],"Zhang [14] (tuned lambda_min = 123)",[5415,5430,5431],{"slug":5460,"sourceId":5415,"sourceLabel":5416,"sourceYear":2267,"table":5461,"note":5462,"datasets":5463,"metrics":5464,"families":5465,"methods":5466,"methodIds":5467,"rows":274,"failures":30},"hatleskog2024probdegen-text-sec-iv-f","Text Sec. IV-F","Runtime of adapted LOAM scan-matching step in exp. 2, incl. k-d tree build, correspondence search and plane estimation",[5422],[23,38],[40,23],[81],[5415],{"slug":5469,"sourceId":5470,"sourceLabel":5471,"sourceYear":306,"table":69,"note":5472,"datasets":5473,"metrics":5475,"families":5476,"methods":5477,"methodIds":5479,"rows":415,"failures":30},"hawley2022tunnelleakage-table-1","hawley2022tunnelleakage","Hawley & Gräbe, 2022","Standard deviation of point residuals to a best-fit plane per coloured MDF target (static Hovermap scans in lab); device characterization, no competin…",[5474],"laboratory coloured targets",[1830],[78],[5478],"Emesent Hovermap (static lab scan)",[],{"slug":5481,"sourceId":5470,"sourceLabel":5471,"sourceYear":306,"table":108,"note":5482,"datasets":5483,"metrics":5485,"families":5486,"methods":5487,"methodIds":5489,"rows":2420,"failures":30},"hawley2022tunnelleakage-table-2","Extracted water leakage areas in the 50 m DB tunnel section (chainage 396 to 446 m); no ground truth, visual check only",[5484],"own field scan, South African rapid rail tunnel (DB section)",[23],[23],[5488],"intensity-based leakage extraction workflow (Maptek PointStudio)",[5470],{"slug":5491,"sourceId":2029,"sourceLabel":5492,"sourceYear":4828,"table":91,"note":5493,"datasets":5494,"metrics":5495,"families":5496,"methods":5497,"methodIds":5504,"rows":2252,"failures":154},"m2dp2016-table-i","He et al., 2016","Recall at 100% precision, raw clouds, nearest-neighbour matching, loop if GT distance \u003C 10 m, +\u002F-50 frames excluded (+\u002F-5 Freiburg)",[2020,4976,1997],[23],[23],[5498,5499,2025,5500,5501,5502,5503],"ESF","GIST","SHOT","Spin Image","VFH","Z-projection",[2029],{"slug":5506,"sourceId":2029,"sourceLabel":5492,"sourceYear":4828,"table":325,"note":5507,"datasets":5508,"metrics":5509,"families":5510,"methods":5511,"methodIds":5512,"rows":224,"failures":30},"m2dp2016-table-ii","Per-cloud time on KITTI00, raw clouds; M2DP Matlab and C, Z-projection Matlab, others PCL C++",[1997],[38],[40],[5498,2025,5500,5501,5502,5503],[2029],{"slug":5514,"sourceId":2029,"sourceLabel":5492,"sourceYear":4828,"table":279,"note":5515,"datasets":5516,"metrics":5517,"families":5518,"methods":5519,"methodIds":5520,"rows":608,"failures":30},"m2dp2016-table-iii","Recall at 100% precision on KITTI06 after grid downsampling with grid size res x k (res = mean nearest-neighbour distance in first frame)",[1997],[23],[23],[5498,2025,5500,5501,5502,5503],[2029],{"slug":5522,"sourceId":4054,"sourceLabel":5523,"sourceYear":374,"table":69,"note":5524,"datasets":5525,"metrics":5527,"families":5528,"methods":5529,"methodIds":5531,"rows":52,"failures":30},"pointlio2023-table-1","He et al., 2023a","Robot car returns to its start; drift is end-to-start distance (true start-end gap below 10 cm); 10 Hz LiDAR packages; strong chassis vibration",[5526],"Point-LIO own sequences (Livox Avia sensor suite)",[1551],[1553],[1437,4052,5530],"Point-LIO-input (ablation: point-wise update with IMU as input)",[321,4054],{"slug":5533,"sourceId":4054,"sourceLabel":5523,"sourceYear":374,"table":108,"note":5534,"datasets":5535,"metrics":5536,"families":5537,"methods":5538,"methodIds":5540,"rows":120,"failures":63},"pointlio2023-table-2","Odo sequence on rotating platform, 100 Hz packages; FAST-LIO2 frames split in two (200 Hz); average odometry output frequency",[5526],[148,23],[40,23],[1437,4052,5539],"Point-LIO-input (ablation)",[321,4054],{"slug":5542,"sourceId":4054,"sourceLabel":5523,"sourceYear":374,"table":17,"note":5543,"datasets":5544,"metrics":5545,"families":5546,"methods":5547,"methodIds":5548,"rows":654,"failures":154},"pointlio2023-table-3","Spinning experiment started at different initial yaw rates; IMU range 35 rad\u002Fs; rotation RMSE against Vicon",[5526],[23],[23],[4052],[4054],{"slug":5550,"sourceId":4054,"sourceLabel":5523,"sourceYear":374,"table":33,"note":5551,"datasets":5552,"metrics":5553,"families":5554,"methods":5555,"methodIds":5556,"rows":102,"failures":356},"pointlio2023-table-4","Average total time per scan (ms); Park, Square, Corridor at 10 Hz, Odo, Satu-1, Satu-2 at 100 Hz; dash marks sequences where the LIO fails",[5526],[38],[40],[1437,4052,5539],[321,4054],{"slug":5558,"sourceId":4054,"sourceLabel":5523,"sourceYear":374,"table":244,"note":5559,"datasets":5560,"metrics":5564,"families":5565,"methods":5566,"methodIds":5568,"rows":3819,"failures":274},"pointlio2023-table-5","RMSE of translation (m) on public sequences selected by FAST-LIO2; baseline values taken from the FAST-LIO2 paper on the same computer; one parameter…",[5561,5562,5563],"liosam","ulhk","utbm",[568],[25],[1437,5567,4769,334,4052],"LILI-OM",[321,337,4783,338,4054],{"slug":5570,"sourceId":4054,"sourceLabel":5523,"sourceYear":374,"table":1072,"note":5571,"datasets":5572,"metrics":5574,"families":5575,"methods":5576,"methodIds":5577,"rows":1339,"failures":641},"pointlio2023-table-6","End-to-end drift (m) on sequences that start and end at the same place; lili uses Livox Horizon, ulhk Velodyne HDL-32E, liosam VLP-16; LILI-OM tuned p…",[5573,5561,5562],"lili",[1551],[1553],[1437,5567,4769,334,4052],[321,337,4783,338,4054],{"slug":5579,"sourceId":4054,"sourceLabel":5523,"sourceYear":374,"table":621,"note":5580,"datasets":5581,"metrics":5583,"families":5584,"methods":5585,"methodIds":5594,"rows":618,"failures":30},"pointlio2023-table-7","Average time per LiDAR scan over the 12 public sequences; Point-LIO and FAST-LIO2 report total time, LILI-OM, LIO-SAM and LINS report odometry and map…",[5582],"12 public sequences (utbm, ulhk, liosam, lili)",[38],[40],[5586,5587,5588,5589,5590,5591,5592,5593],"FAST-LIO2 (Total)","LILI-OM (Map.)","LILI-OM (Odo.)","LINS (Map.)","LINS (Odo.)","LIO-SAM (Map.)","LIO-SAM (Odo.)","Point-LIO (Total)",[321,337,4783,338,4054],{"slug":5596,"sourceId":4054,"sourceLabel":5523,"sourceYear":374,"table":5597,"note":5598,"datasets":5599,"metrics":5600,"families":5601,"methods":5602,"methodIds":5603,"rows":356,"failures":30},"pointlio2023-text-sec-5-5","Text Sec. 5.5","IMU saturated after the initial stage; errors against Vicon ground truth",[5526],[568,23],[25,23],[4052],[4054],{"slug":5605,"sourceId":5606,"sourceLabel":5607,"sourceYear":374,"table":91,"note":5608,"datasets":5609,"metrics":5611,"families":5612,"methods":5613,"methodIds":5616,"rows":224,"failures":30},"he2023ikfom-table-i","he2023ikfom","He et al., 2023b","Odometry drift (%) of the FAST-LIO based LiDAR-inertial system implemented with IKFoM (with online extrinsics) versus the hand-derived IESEKF of FAST-…",[5610],"own datasets (trial 01 by the authors; trial 02 from the FAST-LIO paper)",[1551],[1553],[5614,5615],"Hand-derived [14] (FAST-LIO)","IKFoM-based",[1476,5606],{"slug":5618,"sourceId":5606,"sourceLabel":5607,"sourceYear":374,"table":325,"note":5619,"datasets":5620,"metrics":5621,"families":5622,"methods":5623,"methodIds":5624,"rows":224,"failures":30},"he2023ikfom-table-ii","Average running time of one complete iteration of LiDAR-inertial navigation, IKFoM-based (six more states for extrinsics) versus hand-derived FAST-LIO…",[5610],[38],[40],[5614,5615],[1476,5606],{"slug":5626,"sourceId":5606,"sourceLabel":5607,"sourceYear":374,"table":279,"note":5627,"datasets":5628,"metrics":5630,"families":5631,"methods":5632,"methodIds":5634,"rows":356,"failures":30},"he2023ikfom-table-iii","Average running time of one complete state-estimation iteration of LINS with its hand-derived IESEKF replaced by IKFoM versus the original LINS, on LI…",[5629],"LIO-SAM open sequences",[38],[40],[5633,5615],"Hand-derived [13] (LINS)",[5606,4783],{"slug":5636,"sourceId":5637,"sourceLabel":5638,"sourceYear":306,"table":279,"note":5639,"datasets":5640,"metrics":5642,"families":5643,"methods":5644,"methodIds":5658,"rows":2551,"failures":30},"helmberger2022hilti-table-iii","helmberger2022hilti","Helmberger et al., 2022","Hilti SLAM Challenge 2021 results for the 13 non-anonymous of 27 teams; evaluated on the half of the sequences whose ground truth was withheld; score…",[5641],"Hilti SLAM Challenge Dataset (2021)",[568,23],[25,23],[5645,5646,5647,5648,5649,5650,5651,5652,5653,5654,5655,5656,5657],"Bosch Research: closed-source; graph optimization, Manhattan world; sensors LiDAR + imu","C.F Rubio et.al: based on [34] (LOAM); optimization based; sensors LiDAR + imu","CMU Doom: closed-source; sliding window + loop closure; sensors camera + LiDAR + imu","ETH Zürich: Maplab [32]; tightly coupled, graph based; sensors camera + LiDAR + imu","GeoSLAM: closed-source; sliding window, loop closure, BA; sensors LiDAR + imu","IVISO: closed-source; graph based approach, no loop closing; sensors camera + imu","Megvii3D: based on [28] (variant of FAST-LIO2 per Sec. V); IEFK based [sic]; sensors LiDAR + imu","NPM3D Team, MINES ParisTech: CT-ICP [33]; scan-to-map, no loop closure, no ba; sensors LiDAR","Nanyang Technological University: VIRAL [31]; sliding window, BA; sensors camera + LiDAR + imu","Oxford Robotics Institute: VILENS [30]; tightly coupled, based on factor graphs; sensors camera + LiDAR + imu","Spectacular AI: HybVIO [35]; MSCKF [36] based, no global ba; sensors camera + imu","UC San Diego: closed-source; scan-to-map, imu for undistortion; sensors LiDAR + imu","Vision & Robotics GmbH: based on [29] (MC2SLAM); tightly coupled MHE, loop closure, BA; sensors LiDAR + imu",[596,798],{"slug":5660,"sourceId":5661,"sourceLabel":5662,"sourceYear":1111,"table":69,"note":5663,"datasets":5664,"metrics":5666,"families":5667,"methods":5668,"methodIds":5671,"rows":63,"failures":30},"rgbdmapping2012-table-1","rgbdmapping2012","Henry et al., 2012","Intel-Day marker sequence; mean number of RANSAC inliers per frame for Euclidean-error versus re-projection-error RANSAC (components of RGB-D ICP eval…",[5665],"authors' Intel Labs Seattle marker sequences",[23],[23],[5669,5670],"EE-RANSAC","RE-RANSAC",[],{"slug":5673,"sourceId":5661,"sourceLabel":5662,"sourceYear":1111,"table":108,"note":5674,"datasets":5675,"metrics":5676,"families":5677,"methods":5678,"methodIds":5682,"rows":578,"failures":30},"rgbdmapping2012-table-2","Sequential frame-to-frame alignment on the Intel loop: mean error (m) between measured and estimated distances of 16 consecutive marker pairs (3 to 5.…",[5665],[23],[23],[5669,5679,5670,5680,5681],"ICP (dense point-to-plane component alone)","RGB-D ICP","Two-Stage RGB-D ICP",[],{"slug":5684,"sourceId":5661,"sourceLabel":5662,"sourceYear":1111,"table":17,"note":5685,"datasets":5686,"metrics":5687,"families":5688,"methods":5689,"methodIds":5690,"rows":578,"failures":30},"rgbdmapping2012-table-3","Timing of the sequential alignment techniques on the same marker sequences: mean seconds per frame with 95% confidence intervals; compute hardware not…",[5665],[38],[40],[5669,5679,5670,5680,5681],[],{"slug":5692,"sourceId":5661,"sourceLabel":5662,"sourceYear":1111,"table":3822,"note":5693,"datasets":5694,"metrics":5696,"families":5697,"methods":5698,"methodIds":5701,"rows":274,"failures":30},"rgbdmapping2012-text-fig-8-caption","Global optimisation time for the map of Fig. 5 (challenging Intel Labs sequence): final optimisation run (Fig. 8 caption) and SBA time amortised per f…",[5695],"authors' Intel Labs Seattle sequence (Fig. 5)",[23,38],[40,23],[5699,5700],"SBA with ICP point pairs","TORO pose-graph optimisation",[],{"slug":5703,"sourceId":5661,"sourceLabel":5662,"sourceYear":1111,"table":5342,"note":5704,"datasets":5705,"metrics":5707,"families":5708,"methods":5709,"methodIds":5712,"rows":416,"failures":30},"rgbdmapping2012-text-sec-4-4","Size of the surfel representation compared with merging raw point clouds (values stated in the text; counts are approximate as written)",[5706],"authors' Intel Labs Seattle data",[23,38],[40,23],[5710,5711],"merged point clouds","surfel map",[],{"slug":5714,"sourceId":2116,"sourceLabel":5715,"sourceYear":4828,"table":91,"note":5716,"datasets":5717,"metrics":5719,"families":5720,"methods":5721,"methodIds":5722,"rows":578,"failures":30},"cartographer2016-table-i","Hess et al., 2016","Revo LDS 5 cm floor plan: five straight-line lengths measured in the map with a drawing tool vs laser tape",[5718],"own Revo LDS capture",[1819],[1821],[4759],[2116],{"slug":5724,"sourceId":2116,"sourceLabel":5715,"sourceYear":4828,"table":325,"note":5725,"datasets":5726,"metrics":5728,"families":5729,"methods":5730,"methodIds":5732,"rows":301,"failures":30},"cartographer2016-table-ii","Radish benchmarks, relative-pose error against manually verified relations (metric of Kuemmerle et al. [21]); mean with std; GM values quoted from [21…",[5727],"Radish",[2411,330,23],[23,332],[4759,5731],"GM (Graph Mapping, quoted from [21])",[2116],{"slug":5734,"sourceId":2116,"sourceLabel":5715,"sourceYear":4828,"table":279,"note":5735,"datasets":5736,"metrics":5737,"families":5738,"methods":5739,"methodIds":5741,"rows":29,"failures":30},"cartographer2016-table-iii","Radish benchmarks, relative-pose error, mean with std; Graph FLIRT values quoted from Tipaldi et al. [9]",[5727],[2411,330],[332],[4759,5740],"Graph FLIRT (quoted from [9])",[2116],{"slug":5743,"sourceId":2116,"sourceLabel":5715,"sourceYear":4828,"table":731,"note":5744,"datasets":5745,"metrics":5746,"families":5747,"methods":5748,"methodIds":5749,"rows":224,"failures":30},"cartographer2016-table-iv","Loop closure constraints added (true and false positives) and precision; true positives are constraints not violated by more than 20 cm or 1 deg after…",[5727],[23],[23],[4759],[2116],{"slug":5751,"sourceId":2116,"sourceLabel":5715,"sourceYear":4828,"table":818,"note":5752,"datasets":5753,"metrics":5754,"families":5755,"methods":5756,"methodIds":5757,"rows":120,"failures":30},"cartographer2016-table-v","Wall clock time to process each whole data set, parameters not tuned for CPU performance",[5727],[23],[23],[4759],[2116],{"slug":5759,"sourceId":2116,"sourceLabel":5715,"sourceYear":4828,"table":5760,"note":5761,"datasets":5762,"metrics":5764,"families":5765,"methods":5766,"methodIds":5767,"rows":416,"failures":30},"cartographer2016-text-sec-vi-a","Text Sec. VI.A","Deutsches Museum backpack data: 1,913 s of sensor data, 2,253 m trajectory (from the computed solution); loop-closure graph 11,456 nodes and 35,300 ed…",[5763],"Deutsches Museum (own backpack data)",[219,23],[40,23],[4759],[2116],{"slug":5769,"sourceId":5430,"sourceLabel":5770,"sourceYear":16,"table":91,"note":5771,"datasets":5772,"metrics":5775,"families":5776,"methods":5777,"methodIds":5781,"rows":120,"failures":30},"hinduja2019degeneracy-table-i","Hinduja et al., 2019","Simulated datasets with added odometry noise; RMSE against simulated ground truth, quantity and unit not stated",[5773,5774],"simulated pilings","simulated propeller",[23],[23],[5778,5779,5780],"Degeneracy-aware","Odometry","PTP-OP (point-to-plane ICP with odometry prior, full loop closure factor)",[5430],{"slug":5783,"sourceId":5430,"sourceLabel":5770,"sourceYear":16,"table":1278,"note":5784,"datasets":5785,"metrics":5786,"families":5787,"methods":5788,"methodIds":5791,"rows":356,"failures":30},"hinduja2019degeneracy-text-sec-iv-c","Number of loop closures accepted with identical parameters",[5773,5774],[23],[23],[5789,5790],"PTP-OP","proposed degeneracy-aware algorithm",[5430],{"slug":5793,"sourceId":5794,"sourceLabel":5795,"sourceYear":1032,"table":325,"note":5796,"datasets":5797,"metrics":5799,"families":5800,"methods":5801,"methodIds":5804,"rows":29,"failures":30},"hong2010vicp-table-ii","hong2010vicp","Hong et al., 2010","Office experiments with cart-carried URG-04LX; drift error on return to the starting point (no ground truth)",[5798],"own office experiments",[1551],[1553],[5802,5803],"Original ICP","VICP",[5794],{"slug":5806,"sourceId":5807,"sourceLabel":5808,"sourceYear":2267,"table":325,"note":5809,"datasets":5810,"metrics":5812,"families":5813,"methods":5814,"methodIds":5823,"rows":1790,"failures":30},"livgaussmap2024-table-ii","livgaussmap2024","Hong et al., 2024","Novel-view synthesis on interpolated and extrapolated views on a real-world dataset (dataset and sequence not named for this table); asterisk methods…",[5811],"not stated (real-world dataset)",[148,23],[40,23],[5815,5816,5817,5818,5819,5820,5821,5822],"3D-GS [1]","3D-GS* [1]","DS-NeRF* [24]","F2-NeRF [25]","M-NeRF360 [16]","Our method","Plenoxel [18]","Point-NeRF* [2]",[5824,5807],"kerbl2023_3dgs",{"slug":5826,"sourceId":5807,"sourceLabel":5808,"sourceYear":2267,"table":279,"note":5827,"datasets":5828,"metrics":5833,"families":5834,"methods":5835,"methodIds":5840,"rows":415,"failures":30},"livgaussmap2024-table-iii","Ablation of map structure optimization: Case I = 3D-GS baseline; Case II = LiDAR-initialized Gaussians without visual structure optimization; Case III…",[5829,5830,5831,5832],"FAST-LIVO dataset","all five sequences","self-collected (Our Device I)","self-collected (Our Device II)",[23],[23],[5836,5837,5838,5839],"Case I (3D-GS baseline)","Case II (LiDAR initialization only)","Case III (+ photometric position optimization)","Case IV (full method)",[5824,5807],{"slug":5842,"sourceId":5807,"sourceLabel":5808,"sourceYear":2267,"table":731,"note":5843,"datasets":5844,"metrics":5846,"families":5847,"methods":5848,"methodIds":5849,"rows":224,"failures":30},"livgaussmap2024-table-iv","Structure accuracy of the Gaussian map against the ground-truth point cloud on FusionPortable (sequence HKUST_indoor per Table I); CD and EMD units no…",[5845],"FusionPortable",[2343,74,23],[78,23],[5836,5837,5838,5839],[5824,5807],{"slug":5851,"sourceId":5852,"sourceLabel":5853,"sourceYear":562,"table":91,"note":5854,"datasets":5855,"metrics":5859,"families":5860,"methods":5861,"methodIds":5868,"rows":2262,"failures":274},"gslivo2025-table-i","gslivo2025","Hong et al., 2025","Rendering comparison (T-RO Table I; arXiv v1 Table II without M2Mapping); 15,000 iterations per method; indoor root voxel 0.03 m, outdoor 1.0 m, 2 lev…",[5856,5857,5858],"FAST-LIVO2 dataset","MARS-LVIG","proprietary (MoCap)",[23],[23],[5862,5863,5864,5865,5866,5867,3177],"3D-GS","GS-LIVO (Ours)","LetsGo","M2Mapping","MonoGS","S3GS",[5852,5824,3183,3186],{"slug":5870,"sourceId":5852,"sourceLabel":5853,"sourceYear":562,"table":325,"note":5871,"datasets":5872,"metrics":5873,"families":5874,"methods":5875,"methodIds":5877,"rows":2262,"failures":30},"gslivo2025-table-ii","LIV-based SLAM comparison (T-RO Table II; arXiv v1 Table III without Radcliffe01); image 640x480; octree 0.06 m (indoor) or 0.5 m (outdoor), 2 layers;…",[5857,72,5858],[568,38],[25,40],[5876,5863,2380,2381],"FAST-LIVO [7]",[5852,2386,2387],{"slug":5879,"sourceId":5852,"sourceLabel":5853,"sourceYear":562,"table":279,"note":5880,"datasets":5881,"metrics":5882,"families":5883,"methods":5884,"methodIds":5886,"rows":2252,"failures":52},"gslivo2025-table-iii","Gaussian-based SLAM comparison (T-RO Table III; arXiv v1 Table IV without Radcliffe01); MonoGS* uses LiDAR-projected depth, MonoGS is monocular; x = f…",[5857,72,5858],[568,219,38],[25,40],[5863,5866,5885,3177],"MonoGS*",[5852,3183,3186],{"slug":5888,"sourceId":5852,"sourceLabel":5853,"sourceYear":562,"table":5889,"note":5890,"datasets":5891,"metrics":5892,"families":5893,"methods":5894,"methodIds":5895,"rows":154,"failures":154},"gslivo2025-text-sec-iii-b2","Text Sec.III-B2","Outdoor RMSE stated in the text for Fig. 6(b); R3LIVE 1.465 m and LVI-SAM 4.665 m quoted with it equal the means of their MARS-LVIG rows in the table",[5857],[568],[25],[5863],[5852],{"slug":5897,"sourceId":5852,"sourceLabel":5853,"sourceYear":562,"table":5898,"note":5899,"datasets":5900,"metrics":5901,"families":5902,"methods":5903,"methodIds":5904,"rows":356,"failures":30},"gslivo2025-text-sec-iii-d","Text Sec.III-D","Embedded test on Jetson Orin NX 16 GB: root voxel 0.5 m, 2 layers, 256x216 images, window of 20,000 Gaussians",[2070],[23,38],[40,23],[5863],[5852],{"slug":5906,"sourceId":3527,"sourceLabel":5907,"sourceYear":1234,"table":69,"note":5908,"datasets":5909,"metrics":5913,"families":5914,"methods":5915,"methodIds":5917,"rows":120,"failures":30},"hornung2013octomap-table-1","Hornung et al., 2013","Percentage of cells whose maximum-likelihood state (free or occupied) matches the evaluated scan; Accuracy uses all scans to build and evaluate, Cross…",[5910,5911,5912],"FR-079 corridor","Freiburg campus","New College (Epoch C)",[23],[23],[5916],"OctoMap",[3527],{"slug":5919,"sourceId":3527,"sourceLabel":5907,"sourceYear":1234,"table":108,"note":5920,"datasets":5921,"metrics":5923,"families":5924,"methods":5925,"methodIds":5932,"rows":2119,"failures":30},"hornung2013octomap-table-2","Memory on a 32-bit architecture: full 3D grid (minimal bounding box, one float per cell) versus OctoMap without compression, pruned, and maximum-likel…",[5910,5911,5912,5922],"TUM RGB-D freiburg1_360",[219,23],[40,23],[5926,5927,5928,5929,5930,5931],"OctoMap file, full probabilistic","OctoMap file, lossy maximum-likelihood","OctoMap, maximum-likelihood compression","OctoMap, no compression","OctoMap, pruned","full 3D occupancy grid",[3527],{"slug":5934,"sourceId":3527,"sourceLabel":5907,"sourceYear":1234,"table":5935,"note":5936,"datasets":5937,"metrics":5938,"families":5939,"methods":5940,"methodIds":5941,"rows":154,"failures":154},"hornung2013octomap-text-sec-5-5-2","Text Sec. 5.5.2","Time to traverse all leaf nodes of an existing map with iterators at full resolution (depth cutoff 0); map has 1,087,014 occupied and 3,377,882 free l…",[5911],[23],[23],[5916],[3527],{"slug":5943,"sourceId":5944,"sourceLabel":5945,"sourceYear":374,"table":108,"note":5946,"datasets":5947,"metrics":5949,"families":5950,"methods":5951,"methodIds":5960,"rows":29,"failures":30},"hsieh2023slamarbim-table-2","hsieh2023slamarbim","Hsieh et al., 2023","Same scene and movement range, closed path A to B to A; offset between virtual and physical cylinder centres after ARKit relocalization at different a…",[5948],"own on-site test",[23],[23],[5952,5953,5954,5955,5956,5957,5958,5959],"ARKit SLAM at average movement rate 0.22 m\u002Fs","ARKit SLAM at average movement rate 0.25 m\u002Fs","ARKit SLAM at average movement rate 0.29 m\u002Fs","ARKit SLAM at average movement rate 0.33 m\u002Fs","ARKit SLAM at average movement rate 0.40 m\u002Fs","ARKit SLAM at average movement rate 0.50 m\u002Fs","ARKit SLAM at average movement rate 0.67 m\u002Fs","ARKit SLAM at average movement rate 1.00 m\u002Fs",[],{"slug":5962,"sourceId":5963,"sourceLabel":5964,"sourceYear":374,"table":33,"note":5965,"datasets":5966,"metrics":5968,"families":5969,"methods":5970,"methodIds":5972,"rows":654,"failures":30},"hu2023robotassisted-table-4","hu2023robotassisted","Hu et al., 2023","Semantic segmentation of the 5G Centre point cloud by ResPointNet++ trained on 6 own scenes",[5967],"own NUS 5G Centre scan",[23],[23],[5971],"ResPointNet++ on proposed robot-assisted scan",[5963],{"slug":5974,"sourceId":5963,"sourceLabel":5964,"sourceYear":374,"table":244,"note":5975,"datasets":5976,"metrics":5977,"families":5978,"methods":5979,"methodIds":5982,"rows":578,"failures":30},"hu2023robotassisted-table-5","Proposed legged robot with enhanced DWA and Dot3D SLAM vs comparative scenario (UGV, RTAB-Map, no motion integration); completeness on 13 mm raster, m…",[5967],[76,23],[78,23],[5980,5981],"Comparative scenario (UGV + RTAB-Map)","Proposed approach",[5963,241],{"slug":5984,"sourceId":5963,"sourceLabel":5964,"sourceYear":374,"table":3048,"note":5985,"datasets":5986,"metrics":5987,"families":5988,"methods":5989,"methodIds":5992,"rows":63,"failures":63},"hu2023robotassisted-text-sec-4-1","SLAM mapping outcome during navigation between scan positions with conventional vs enhanced DWA",[5967],[1933],[1935],[5990,5991],"conventional DWA","enhanced DWA",[5963],{"slug":5994,"sourceId":5963,"sourceLabel":5964,"sourceYear":374,"table":3060,"note":5995,"datasets":5996,"metrics":5997,"families":5998,"methods":5999,"methodIds":6000,"rows":63,"failures":30},"hu2023robotassisted-text-sec-4-2","Minimum point cloud densities stated in text for elements not in Table 5; GSA requirement 0.00188 \u002Fmm2",[5967],[23],[23],[5981],[5963],{"slug":6002,"sourceId":6003,"sourceLabel":6004,"sourceYear":562,"table":818,"note":6005,"datasets":6006,"metrics":6008,"families":6009,"methods":6010,"methodIds":6013,"rows":6014,"failures":30},"hu2025mapeval-table-v","hu2025mapeval","Hu et al., 2025","Map metrics and ATE for FAST-LIO2 and PALoc maps against TLS or high-precision ground-truth maps (FusionPortable MCR room sequences and MS-dataset par…",[5845,6007],"MS-dataset (authors, self-collected)",[329,2343,75,76,23],[25,78,23],[6011,6012],"FAST-LIO2 (FL2) [2]","PALoc [10] (loop closure and prior-map constraints)",[321],84,{"slug":6016,"sourceId":6003,"sourceLabel":6004,"sourceYear":562,"table":827,"note":6017,"datasets":6018,"metrics":6019,"families":6020,"methods":6021,"methodIds":6022,"rows":415,"failures":30},"hu2025mapeval-table-vi","Map metrics for FAST-LIO2 and PALoc maps in larger scenes (FusionPortable corridor, canteen, escalator, building; Newer College math easy and parkland…",[5845,3253],[2343,75,76,23],[78,23],[6011,6012],[321],{"slug":6024,"sourceId":6025,"sourceLabel":6026,"sourceYear":345,"table":1235,"note":6027,"datasets":6028,"metrics":6030,"families":6031,"methods":6032,"methodIds":6034,"rows":356,"failures":30},"fovis2017-fig-4","fovis2017","Huang et al., 2017","Metric box printed in Fig. 4: autonomous position hold controlled by visual odometry, position measured by motion capture",[6029],"authors' position-hold flight",[23],[23],[6033],"vehicle controlled using visual odometry (fused with the IMU in an EKF, PID position control; Fig. 4, Sec. 3.3)",[6025],{"slug":6036,"sourceId":6025,"sourceLabel":6026,"sourceYear":345,"table":69,"note":6037,"datasets":6038,"metrics":6040,"families":6041,"methods":6042,"methodIds":6061,"rows":2607,"failures":30},"fovis2017-table-1","Ablation on a challenging MAV motion-capture dataset (motion blur, feature-poor images); each row changes one component of the authors' configuration;…",[6039],"authors' MAV motion-capture dataset",[23,38],[40,23],[6043,6044,6045,6046,6047,6048,6049,6050,6051,6052,6053,6054,6055,6056,6057,6058,6059,6060],"Adaptive FAST threshold: Fixed threshold (10)","Feature grid\u002Fbucketing: No grid","Feature window size: 11","Feature window size: 3","Feature window size: 5","Feature window size: 7","Gaussian pyramid levels: 1","Gaussian pyramid levels: 2","Gaussian pyramid levels: 4","Initial rotation estimate: None","Inlier detection: Preemptive RANSAC","Inlier detection: RANSAC","Our approach (greedy max-clique, initial rotation, 3 pyramid levels, bidirectional ESM, 9 x 9 window, subpixel refinement, adaptive FAST threshold, grid bucketing)","Reprojection error minimization: Absolute orientation only","Reprojection error minimization: Bidir. Gauss-Newton","Reprojection error minimization: Unidir. ESM","Reprojection error minimization: Unidir. Gauss-Newton","Subpixel feature refinement: No refinement",[6025],{"slug":6063,"sourceId":6025,"sourceLabel":6026,"sourceYear":345,"table":3087,"note":6064,"datasets":6065,"metrics":6066,"families":6067,"methods":6068,"methodIds":6071,"rows":63,"failures":30},"fovis2017-text-sec-3-2","Offboard mapping and loop-closure timings stated in the text (laptop, model not reported)",[2070],[23,38],[40,23],[6069,6070],"RGB-D Mapping back end (TORO pose graph)","RGB-D Mapping back end (offboard occupancy voxel map, 10 cm)",[],{"slug":6073,"sourceId":6025,"sourceLabel":6026,"sourceYear":345,"table":3048,"note":6074,"datasets":6075,"metrics":6077,"families":6078,"methods":6079,"methodIds":6081,"rows":618,"failures":30},"fovis2017-text-sec-4-1","Per-stage timing of the chosen configuration stated in the Timing paragraph (laptop given as 2.6 GHz there), plus the approximate onboard time per fra…",[6039,6076],"authors' observations in feature-rich environments",[23,38],[40,23],[6080],"our approach (visual odometry of this chapter; the name 'fovis' is not used in the text)",[6025],{"slug":6083,"sourceId":6084,"sourceLabel":6085,"sourceYear":2267,"table":69,"note":6086,"datasets":6087,"metrics":6089,"families":6090,"methods":6091,"methodIds":6099,"rows":6101,"failures":356},"huang2024-2dgs-table-1","huang2024_2dgs","Huang et al., 2024a","Chamfer distance per DTU scan (15 scans) and mean; meshes of 3DGS and 2DGS by TSDF fusion of rendered depth; unit not stated in the paper; images down…",[6088],"DTU",[2343,23],[78,23],[6092,6093,6094,6095,6096,6097,6098],"2DGS-15k (Ours)","2DGS-30k (Ours)","3DGS (Kerbl et al., 2023)","NeRF (Mildenhall et al., 2021)","NeuS (Wang et al., 2021)","SuGaR (Guédon and Lepetit, 2023)","VolSDF (Yariv et al., 2021)",[6084,5824,6100],"nerf2020",119,{"slug":6103,"sourceId":6084,"sourceLabel":6085,"sourceYear":2267,"table":108,"note":6104,"datasets":6105,"metrics":6107,"families":6108,"methods":6109,"methodIds":6115,"rows":1069,"failures":356},"huang2024-2dgs-table-2","F1 score per Tanks and Temples scene and mean; distance threshold not stated in the paper (TnT benchmark protocol)",[6106],"Tanks and Temples",[74,23],[78,23],[6110,6111,6112,6113,81,6114],"3DGS","Geo-Neus","NeuS","Neurlangelo (as written; Neuralangelo)","SuGaR",[6084,5824],{"slug":6117,"sourceId":6118,"sourceLabel":6119,"sourceYear":2267,"table":91,"note":6120,"datasets":6121,"metrics":6123,"families":6124,"methods":6125,"methodIds":6129,"rows":120,"failures":30},"loglio2024-table-i","loglio2024","Huang et al., 2024b","Mean running time of normal estimation for a single scan; Ring FALS includes projection, box-filtering and smoothing; PCL least squares with k-d tree,…",[6122,1636],"M2DGR",[38],[40],[6126,6127,6128],"PCL OMP 10 threads","PCL single thread","Ring FALS (total)",[6118,6130],"rusu2011pcl",{"slug":6132,"sourceId":6118,"sourceLabel":6119,"sourceYear":2267,"table":325,"note":6133,"datasets":6134,"metrics":6135,"families":6136,"methods":6137,"methodIds":6138,"rows":3766,"failures":30},"loglio2024-table-ii","M2DGR; translation RMSE of ATE; loop closure disabled; map and scan downsampling 0.4 m; first and last 100 s of street07 and street10 discarded (RTK i…",[6122],[568],[25],[1437,334,4051],[321,338,6118],{"slug":6140,"sourceId":6118,"sourceLabel":6119,"sourceYear":2267,"table":279,"note":6141,"datasets":6142,"metrics":6143,"families":6144,"methods":6145,"methodIds":6146,"rows":6147,"failures":578},"loglio2024-table-iii","NTU VIRAL (horizontal OS1-16, VN100); translation RMSE of ATE; loop closure disabled; map and scan downsampling 0.5 m; x = failed; LOG-C ablation colu…",[1636],[568],[25],[1437,334,4051],[321,338,6118],57,{"slug":6149,"sourceId":6118,"sourceLabel":6119,"sourceYear":2267,"table":731,"note":6150,"datasets":6151,"metrics":6153,"families":6154,"methods":6155,"methodIds":6156,"rows":608,"failures":30},"loglio2024-table-iv","Average processing time per scan for each sequence group",[6152],"M2DGR and NTU VIRAL",[38],[40],[1437,4051],[321,6118],{"slug":6158,"sourceId":5182,"sourceLabel":6159,"sourceYear":2267,"table":69,"note":6160,"datasets":6161,"metrics":6162,"families":6163,"methods":6164,"methodIds":6177,"rows":6179,"failures":63},"photoslam2024-table-1","Huang et al., 2024c","Replica, average of 5 runs per sequence; all baselines run with official code on the desktop; '-' means rendering not supported or tracking failed; re…",[3223],[568,148,219,23],[25,40,23],[6165,6166,6167,6168,6169,6170,6171,6172,6173,81,6174,6175,6176],"BundleFusion [6]","Co-SLAM [36]","DROID-SLAM [34]","ESLAM [16]","Go-SLAM [44]","Nice-SLAM [46]","Nice-SLAM* [46] (depth supervision disabled)","ORB-SLAM3 [2]","Orbeez-SLAM [4]","Ours (Jetson)","Ours (Laptop)","Point-SLAM [27]",[2897,3181,6178,3182,3184,763,5182,3185],"droidslam2021",80,{"slug":6181,"sourceId":5182,"sourceLabel":6159,"sourceYear":2267,"table":108,"note":6182,"datasets":6183,"metrics":6184,"families":6185,"methods":6186,"methodIds":6187,"rows":3547,"failures":30},"photoslam2024-table-2","TUM RGB-D, ATE RMSE in cm, average of 5 runs; rendering metrics not extracted",[2872],[568],[25],[6166,6167,6168,6169,6170,6172,81,6174,6175],[3181,6178,3182,3184,763,5182],{"slug":6189,"sourceId":5182,"sourceLabel":6159,"sourceYear":2267,"table":17,"note":6190,"datasets":6191,"metrics":6192,"families":6193,"methods":6194,"methodIds":6195,"rows":1042,"failures":30},"photoslam2024-table-3","EuRoC MAV stereo input, ATE RMSE in cm; rendering metrics not extracted",[743],[568],[25],[6167,6172,81,6174,6175],[6178,763,5182],{"slug":6197,"sourceId":6198,"sourceLabel":6199,"sourceYear":538,"table":454,"note":6200,"datasets":6201,"metrics":6205,"families":6206,"methods":6207,"methodIds":6212,"rows":654,"failures":654},"hahnel2003-gridfastslam-text-sec-iv","hahnel2003_gridfastslam","Hähnel et al., 2003a","Intel Research Lab (28 m x 28 m, 491 m traveled); map judged globally consistent and built in real time",[6202,6203,6204],"B21r simulator, Wean Hall","Intel Research Lab (Pioneer 2, SICK LMS)","University of Washington Sieg Hall",[23],[23],[6208,6209,6210,6211],"particle filter strategy of Thrun et al. [20], [19] (single map)","proposed RBPF (offline)","proposed RBPF with scan-matching-corrected odometry","standard Rao-Blackwellized particle filter",[6198,6213],"thrun2000_3dmapping",{"slug":6215,"sourceId":6216,"sourceLabel":6217,"sourceYear":538,"table":108,"note":6218,"datasets":6219,"metrics":6223,"families":6224,"methods":6225,"methodIds":6228,"rows":608,"failures":30},"hahnel2003-compact3d-table-2","hahnel2003_compact3d","Hähnel et al., 2003b","Statistics of the planar simplification for three data sets; times given as min:s in the paper and converted to seconds here; reduction ratio given as…",[6220,6221,6222],"CMU Wean Hall corridor (10 m traveled, SICK PLS)","UW Sieg Hall corridor","University of Freiburg campus buildings (40 m x 60 m, robot Herbert)",[23],[23],[6226,6227],"input data \u002F raw mesh","planar approximation and polygon merging (proposed)",[6216],{"slug":6230,"sourceId":6231,"sourceLabel":6232,"sourceYear":16,"table":6233,"note":6234,"datasets":6235,"metrics":6237,"families":6238,"methods":6239,"methodIds":6241,"rows":120,"failures":30},"ibrahim2019bimugv-text-experimental-setup-and-results","ibrahim2019bimugv","Ibrahim et al., 2019","Text Experimental Setup and Results","Experiment 2 on a building floor under indoor partitioning; density = neighbours per unit area at each point; accuracy = distance of each point to clo…",[6236],"own site data (experiment 2)",[75,23],[78,23],[6240],"BIM-driven UGV pipeline with Hector SLAM and vertical 2D LiDAR",[6231],{"slug":6243,"sourceId":6244,"sourceLabel":6245,"sourceYear":374,"table":108,"note":6246,"datasets":6247,"metrics":6249,"families":6250,"methods":6251,"methodIds":6253,"rows":2882,"failures":63},"ibrahimkhil2023masonryslam-table-2","ibrahimkhil2023masonryslam","Ibrahimkhil et al., 2023","Percent error of scan-based progress percentage vs manually calculated actual progress per wall and scan round; accuracy = 100 minus total average",[6248],"own site data, Quakers Hill granny flat",[23],[23],[6252],"SLAM scan + Hausdorff filtering + as-built BIM quantities",[6244],{"slug":6255,"sourceId":6256,"sourceLabel":6257,"sourceYear":374,"table":325,"note":6258,"datasets":6259,"metrics":6261,"families":6262,"methods":6263,"methodIds":6267,"rows":1042,"failures":274},"loner2023-table-ii","loner2023","Isaacson et al., 2023","RMS APE (m), median of 5 runs per method; trajectories aligned following Zhang and Scaramuzza [30] with evo (alignment type not stated); x = failed; '…",[6260,3253],"Fusion Portable",[568],[25],[6264,6265,6266,2197,3174],"LONER","LONER w.\u002F L_CLONeR","LONER w.\u002F L_URF",[395,6256,3184],{"slug":6269,"sourceId":6256,"sourceLabel":6257,"sourceYear":374,"table":279,"note":6270,"datasets":6271,"metrics":6272,"families":6273,"methods":6274,"methodIds":6276,"rows":6179,"failures":429},"loner2023-table-iii","Map accuracy and completion (m, mean nearest-point distances) and precision and recall at a 0.1 m threshold; meshes from each method sampled to point…",[6260,3253],[75,76,23],[78,23],[6264,6265,6266,3174,6275],"SHINE (ground-truth poses)",[6256,3184,3263],{"slug":6278,"sourceId":6256,"sourceLabel":6257,"sourceYear":374,"table":6279,"note":6280,"datasets":6281,"metrics":6282,"families":6283,"methods":6284,"methodIds":6285,"rows":356,"failures":30},"loner2023-text-sec-iv-e","Text Sec.IV-E","Runtime of LONER in real-time configuration (scans decimated to 5 Hz, one KeyFrame every 3 s, 50 iterations per KeyFrame).",[2070],[148,23,38],[40,23],[6264],[6256],{"slug":6287,"sourceId":6288,"sourceLabel":6289,"sourceYear":562,"table":17,"note":6290,"datasets":6291,"metrics":6293,"families":6294,"methods":6295,"methodIds":6301,"rows":1042,"failures":30},"jeon2025-nerf-construction-table-3","jeon2025_nerf_construction","Jeon et al., 2025","NeRF (Instant-NGP in Nerfstudio) rendering quality averaged over several randomly selected frames, and training time for 100k iterations, per capture…",[6292],"Authors' construction-site videos (Miryang-si, Korea)",[23],[23],[6296,6297,6298,6299,6300],"Instant-NGP NeRF within the proposed pipeline (1440 images)","Instant-NGP NeRF within the proposed pipeline (1903 images)","Instant-NGP NeRF within the proposed pipeline (576 images)","Instant-NGP NeRF within the proposed pipeline (853 images)","Instant-NGP NeRF within the proposed pipeline (879 images)",[6288],{"slug":6303,"sourceId":6288,"sourceLabel":6289,"sourceYear":562,"table":33,"note":6304,"datasets":6305,"metrics":6307,"families":6308,"methods":6309,"methodIds":6311,"rows":598,"failures":30},"jeon2025-nerf-construction-table-4","Scene_1 (concrete pouring completed, smartphone video): per-element IoU against BIM-generated masks, absolute error AE = |IoU - actual progress (100%)…",[6306],"Authors' construction-site videos",[1819,23],[23,1821],[6310],"Proposed NeRF-BIM pipeline (point-prompted SAM on NeRF render vs BIM mask)",[6288],{"slug":6313,"sourceId":6288,"sourceLabel":6289,"sourceYear":562,"table":244,"note":6314,"datasets":6315,"metrics":6316,"families":6317,"methods":6318,"methodIds":6319,"rows":618,"failures":30},"jeon2025-nerf-construction-table-5","Scene_1 per-class mean absolute error of segmentation (mAE) and mean wRMSE (mwRMSE); abstract's '1% to 2.2%' corresponds to these mwRMSE values",[6306],[1819,23],[23,1821],[6310],[6288],{"slug":6321,"sourceId":6288,"sourceLabel":6289,"sourceYear":562,"table":1072,"note":6322,"datasets":6323,"metrics":6324,"families":6325,"methods":6326,"methodIds":6327,"rows":29,"failures":30},"jeon2025-nerf-construction-table-6","Scene_2 (formwork installation ongoing, smartphone video): segmentation IoU vs manually labelled as-built masks and progress-tracking IoU vs BIM-gener…",[6306],[23],[23],[6310],[6288],{"slug":6329,"sourceId":6288,"sourceLabel":6289,"sourceYear":562,"table":621,"note":6330,"datasets":6331,"metrics":6332,"families":6333,"methods":6334,"methodIds":6335,"rows":120,"failures":30},"jeon2025-nerf-construction-table-7","Scene_2 per-class mean absolute errors for segmentation (mAE_seg) and progress tracking (mAE_trk)",[6306],[23],[23],[6310],[6288],{"slug":6337,"sourceId":6338,"sourceLabel":6339,"sourceYear":306,"table":731,"note":6340,"datasets":6341,"metrics":6342,"families":6343,"methods":6344,"methodIds":6347,"rows":6348,"failures":274},"jiao2022fusionportable-table-iv","jiao2022fusionportable","Jiao et al., 2022","Mean ATE of open-source SLAM systems on FusionPortable sequences; x = failed to finish; VINS-Fusion run with loop closure (LC); ESVO omitted because i…",[5845],[22],[25],[4330,1437,6345,334,6346],"LIO-Mapping","VINS-Fusion (LC)",[392,321,2117,338,1513],85,{"slug":6350,"sourceId":6338,"sourceLabel":6339,"sourceYear":306,"table":6351,"note":6352,"datasets":6353,"metrics":6354,"families":6355,"methods":6356,"methodIds":6357,"rows":63,"failures":30},"jiao2022fusionportable-text-sec-v","Text Sec.V","Mapping accuracy: mean point-to-point error of the algorithm map w.r.t. the Leica BLK360 ground-truth map; pairing follows 'respectively' in Sec. V an…",[5845],[75],[78],[4330,334],[392,338],{"slug":6359,"sourceId":3182,"sourceLabel":6360,"sourceYear":374,"table":2425,"note":6361,"datasets":6362,"metrics":6363,"families":6364,"methods":6365,"methodIds":6367,"rows":224,"failures":30},"eslam2023-supp-table-1","Johari et al., 2023","Robustness to input depth resolution on Replica room0; full-resolution depth (1\u002F1 D)",[3223],[568,75,76],[25,78],[6366,3174],"ESLAM (ours)",[3182,3184],{"slug":6369,"sourceId":3182,"sourceLabel":6360,"sourceYear":374,"table":2451,"note":6370,"datasets":6371,"metrics":6372,"families":6373,"methods":6374,"methodIds":6377,"rows":608,"failures":30},"eslam2023-supp-table-4","Per-scene Replica breakdown, ATE RMSE mean of five runs (other metrics not transcribed)",[3223],[568],[25],[6375,3174,6376],"ESLAM (Ours)","iMAP*",[3182,6378,3184],"imap2021",{"slug":6380,"sourceId":3182,"sourceLabel":6360,"sourceYear":374,"table":6381,"note":6382,"datasets":6383,"metrics":6384,"families":6385,"methods":6386,"methodIds":6390,"rows":1042,"failures":30},"eslam2023-supp-table-5","Supp. Table 5","Accuracy versus frame processing time with more optimization iterations, Replica averages",[3223],[568,75,76,38],[25,40,78],[6387,6388,6389,3174,6376],"ESLAM (ours), Iter_m 15, Iter_t 8","ESLAM x10 (ours), Iter_m 150, Iter_t 80","ESLAM x2 (ours), Iter_m 30, Iter_t 16",[3182,6378,3184],{"slug":6392,"sourceId":3182,"sourceLabel":6360,"sourceYear":374,"table":69,"note":6393,"datasets":6394,"metrics":6395,"families":6396,"methods":6397,"methodIds":6398,"rows":102,"failures":30},"eslam2023-table-1","Replica, average of five runs over eight scenes; meshes from a 1 cm TSDF volume with frustum and occlusion culling for all methods",[3223],[568,22,75,76,23],[25,78,23],[6366,3174,6376],[3182,6378,3184],{"slug":6400,"sourceId":3182,"sourceLabel":6360,"sourceYear":374,"table":108,"note":6401,"datasets":6402,"metrics":6403,"families":6404,"methods":6405,"methodIds":6406,"rows":2252,"failures":30},"eslam2023-table-2","ScanNet localization, average of five runs per scene (std omitted)",[2963],[568,22],[25],[6366,3174,6376],[3182,6378,3184],{"slug":6408,"sourceId":3182,"sourceLabel":6360,"sourceYear":374,"table":17,"note":6409,"datasets":6410,"metrics":6411,"families":6412,"methods":6413,"methodIds":6414,"rows":52,"failures":30},"eslam2023-table-3","TUM RGB-D localization; no ground-truth meshes, so no reconstruction metric",[2872],[568],[25],[6366,3174,6376],[3182,6378,3184],{"slug":6416,"sourceId":3182,"sourceLabel":6360,"sourceYear":374,"table":33,"note":6417,"datasets":6418,"metrics":6419,"families":6420,"methods":6421,"methodIds":6422,"rows":224,"failures":30},"eslam2023-table-4","Average frame processing time and parameter count; model size growth O(L^2) for ESLAM versus O(L^3) for NICE-SLAM",[3223,2963],[23,38],[40,23],[6366,3174,6376],[3182,6378,3184],{"slug":6424,"sourceId":6425,"sourceLabel":6426,"sourceYear":374,"table":325,"note":6427,"datasets":6428,"metrics":6430,"families":6431,"methods":6432,"methodIds":6436,"rows":3819,"failures":154},"malio2023-table-ii","malio2023","Jung et al., 2023","Hilti SLAM Dataset 2021; ATEt from the dataset's evaluator; FAST-LIO2 run on each single LiDAR (Fast-H Livox, Fast-O Ouster); M-LOAM with Livox points…",[6429],"Hilti SLAM Dataset 2021",[329],[25],[6433,6434,6435,4775,81],"Fast-H (FAST-LIO2 with Livox Horizon)","Fast-O (FAST-LIO2 with OS0-64)","LOCUS 2.0",[321,6425],{"slug":6438,"sourceId":6425,"sourceLabel":6426,"sourceYear":374,"table":279,"note":6439,"datasets":6440,"metrics":6442,"families":6443,"methods":6444,"methodIds":6446,"rows":1069,"failures":30},"malio2023-table-iii","UrbanNav (HDL-32E central, VLP-16 and LS-16C inclined, 400 Hz IMU); RMSE of ATE and RTE via evo",[6441],"UrbanNav",[568,2411,330,23],[25,23,332],[6445,6435,4775,81],"Fast-LIO2 (central LiDAR only)",[321,6425],{"slug":6448,"sourceId":6425,"sourceLabel":6426,"sourceYear":374,"table":731,"note":6449,"datasets":6450,"metrics":6452,"families":6453,"methods":6454,"methodIds":6455,"rows":1069,"failures":30},"malio2023-table-iv","Authors' city dataset (OS2-128, Livox Avia, Livox Tele, 100 Hz IMU, PTP time reference); INS ground truth; RMSE of ATE and RTE via evo",[6451],"MA-LIO city dataset (City01-03)",[568,2411,330,23],[25,23,332],[6445,6435,4775,81],[321,6425],{"slug":6457,"sourceId":6425,"sourceLabel":6426,"sourceYear":374,"table":818,"note":6458,"datasets":6459,"metrics":6460,"families":6461,"methods":6462,"methodIds":6468,"rows":598,"failures":30},"malio2023-table-v","Component ablation with ATEt: RAW (IMU discrete model, equal point weights), CNT (B-spline interpolation), F-UNC (single state covariance as in M-LOAM…",[6451,6441],[568],[25],[6463,6464,6465,6466,6467],"MA-LIO CNT","MA-LIO F-UNC","MA-LIO FULL","MA-LIO RAW","MA-LIO UNC",[6425],{"slug":6470,"sourceId":6425,"sourceLabel":6426,"sourceYear":374,"table":827,"note":6471,"datasets":6472,"metrics":6473,"families":6474,"methods":6475,"methodIds":6477,"rows":120,"failures":30},"malio2023-table-vi","Average processing time per scan versus number of LiDARs (0.4 m downsampling); totals only stored",[6451,6441],[38],[40],[6476],"MA-LIO",[6425],{"slug":6479,"sourceId":6480,"sourceLabel":6481,"sourceYear":898,"table":91,"note":6482,"datasets":6483,"metrics":6485,"families":6486,"methods":6487,"methodIds":6492,"rows":224,"failures":30},"kaess2008isam-table-i","kaess2008isam","Kaess et al., 2008","Simulated loop with 500 poses and 240 landmarks, significant measurement noise, 3% of measurements replaced by random ones; times include factor updat…",[6484],"simulated loop (500 poses, 240 landmarks)",[23,38],[40,23],[6488,6489,6490,6491],"ML conservative","ML exact, efficient","ML exact, full","NN",[6480],{"slug":6494,"sourceId":6480,"sourceLabel":6481,"sourceYear":898,"table":2158,"note":6495,"datasets":6496,"metrics":6498,"families":6499,"methods":6500,"methodIds":6502,"rows":154,"failures":30},"kaess2008isam-text-sec-iii-c","Simulated linear exploration task; back-substitution time to recover the full solution after 10000 steps",[6497],"simulated linear exploration",[23],[23],[6501],"iSAM",[6480],{"slug":6504,"sourceId":6480,"sourceLabel":6481,"sourceYear":898,"table":481,"note":6505,"datasets":6506,"metrics":6508,"families":6509,"methods":6510,"methodIds":6511,"rows":120,"failures":30},"kaess2008isam-text-sec-vi-a","Sydney Victoria Park: 6969 of 7247 frames retained, 3640 landmark measurements from a tree detector, 4 km, 26 min recording; unknown correspondences u…",[6507],"Victoria Park",[23,38],[40,23],[6501],[6480],{"slug":6513,"sourceId":6480,"sourceLabel":6481,"sourceYear":898,"table":6514,"note":6515,"datasets":6516,"metrics":6518,"families":6519,"methods":6520,"methodIds":6525,"rows":356,"failures":30},"kaess2008isam-text-sec-vi-b-manhattan","Text Sec.VI-B Manhattan","Simulated Manhattan world of Olson et al. (3500 poses, 5598 constraints), known correspondences; normalized chi-square of the solution (lower is bette…",[6517],"Manhattan world (Olson et al.)",[23],[23],[6521,6522,6523,6524],"Olson et al. [21]","full nonlinear optimization until convergence","iSAM (incremental solution)","iSAM after one extra relinearization and back-substitution",[6480],{"slug":6527,"sourceId":6480,"sourceLabel":6481,"sourceYear":898,"table":6528,"note":6529,"datasets":6530,"metrics":6532,"families":6533,"methods":6534,"methodIds":6535,"rows":52,"failures":30},"kaess2008isam-text-sec-vi-b-timing","Text Sec.VI-B timing","Pose-only iSAM with known data association, full solution after each step; 3500 poses, 5598 constraints; reordering and relinearization every 100 step…",[6531,4978,6517],"Intel dataset",[23,38],[40,23],[6501],[6480],{"slug":6537,"sourceId":6480,"sourceLabel":6481,"sourceYear":898,"table":6538,"note":6539,"datasets":6540,"metrics":6541,"families":6542,"methods":6543,"methodIds":6544,"rows":356,"failures":30},"kaess2008isam-text-sparsity","Text sparsity","Average number of non-zero entries per column of the final square-root factor R (sparsity of the factor)",[6531,4978,6517,6507],[23],[23],[6501],[6480],{"slug":6546,"sourceId":6547,"sourceLabel":6548,"sourceYear":1111,"table":69,"note":6549,"datasets":6550,"metrics":6559,"families":6560,"methods":6561,"methodIds":6566,"rows":6567,"failures":120},"kaess2012isam2-table-1","kaess2012isam2","Kaess et al., 2012","Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average,…",[6551,6552,6553,6554,6555,6556,6557,6507,6558],"City20000","Intel","Killian Court","Manhattan","Sphere2500","Torus10000","Trees10000","W10000",[23,38],[40,23],[6562,6563,6564,6565],"HOG-Man","SPA","iSAM1","iSAM2",[6480,6547],126,{"slug":6569,"sourceId":6570,"sourceLabel":6571,"sourceYear":306,"table":6572,"note":6573,"datasets":6574,"metrics":6576,"families":6577,"methods":6578,"methodIds":6580,"rows":63,"failures":30},"kayhani2022tagvio-text-sec-6-1","kayhani2022tagvio","Kayhani et al., 2022","Text Sec.6.1","Simulation, planar trajectory with a tag-blind zone (experiment 3), BIM-enabled Parrot-Sphinx and Gazebo environment, six 0.165 m 36h11 AprilTags, cam…",[6575],"BIM-enabled simulation (Parrot-Sphinx + Gazebo)",[568],[25],[6579],"proposed tag-based on-manifold EKF",[6570],{"slug":6582,"sourceId":6570,"sourceLabel":6571,"sourceYear":306,"table":2984,"note":6583,"datasets":6584,"metrics":6586,"families":6587,"methods":6588,"methodIds":6589,"rows":63,"failures":63},"kayhani2022tagvio-text-sec-6-2","Laboratory, 3D circular trajectory of radius 1 m (experiment 5), Vicon ground truth, camera-to-tag distance 2.2 to 4.2 m; value labelled in the text a…",[6585],"laboratory flight arena with Vicon",[568],[25],[6579],[6570],{"slug":6591,"sourceId":6592,"sourceLabel":6593,"sourceYear":1234,"table":91,"note":6594,"datasets":6595,"metrics":6598,"families":6599,"methods":6600,"methodIds":6606,"rows":415,"failures":63},"kazhdan2013screened-table-i","kazhdan2013screened","Kazhdan & Hoppe, 2013","Wall-clock time and memory for Neptune and David at depths 8 to 11; screening weight alpha = 4, Neumann boundaries, samples-per-node 1; bracketed valu…",[6596,6597],"David (Stanford 3D Scanning Repository)","Neptune (Aim@Shape)",[219,23],[40,23],[6601,6602,6603,6604,6605],"Poisson","SSD","Screened","Wavelet","new solver without screening (alpha = 0)",[6607,6592],"kazhdan2006poisson",{"slug":6609,"sourceId":6592,"sourceLabel":6593,"sourceYear":1234,"table":6610,"note":6611,"datasets":6612,"metrics":6614,"families":6615,"methods":6616,"methodIds":6620,"rows":416,"failures":30},"kazhdan2013screened-text-sec-1","Text Sec. 1","Subset of 11.4M points from the David scan, octree depth 10 (effective 1024^3), timings without parallelization; unscreened timing uses the new implem…",[6613],"David (Digital Michelangelo, 11.4M-point subset)",[23],[23],[6617,6618,6619],"original Poisson reconstruction implementation","screened Poisson","traditional Poisson (new implementation, screening weight 0)",[6607,6592],{"slug":6622,"sourceId":6607,"sourceLabel":6623,"sourceYear":2781,"table":69,"note":6624,"datasets":6625,"metrics":6627,"families":6628,"methods":6629,"methodIds":6630,"rows":224,"failures":30},"kazhdan2006poisson-table-1","Kazhdan et al., 2006","Dragon model reconstructed at octree depths 7 to 10; kernel depth 6 for density estimation; hardware not reported",[6626],"Stanford dragon",[219,23],[40,23],[6601],[6607],{"slug":6632,"sourceId":6607,"sourceLabel":6623,"sourceYear":2781,"table":108,"note":6633,"datasets":6634,"metrics":6636,"families":6637,"methods":6638,"methodIds":6646,"rows":608,"failures":30},"kazhdan2006poisson-table-2","Stanford Bunny raw data (362,000 points from ten range images), processed to fit each algorithm's input format; Poisson reconstructed at octree depth…",[6635],"Stanford Bunny",[219,23],[40,23],[6639,6640,6641,6642,6601,6643,6644,6645],"FFT","FastRBF","Hoppe et al 1992","MPU","Power Crust","Robust Cocone","VRIP",[2826,6607],{"slug":6648,"sourceId":6607,"sourceLabel":6623,"sourceYear":2781,"table":3618,"note":6649,"datasets":6650,"metrics":6652,"families":6653,"methods":6654,"methodIds":6655,"rows":274,"failures":30},"kazhdan2006poisson-text-sec-5-3","Head of Michelangelo's David at depth 11 from 215,613,477 samples",[6651],"David (non-rigidly aligned scans)",[219,23],[40,23],[6601],[6607],{"slug":6657,"sourceId":3186,"sourceLabel":6658,"sourceYear":2267,"table":69,"note":6659,"datasets":6660,"metrics":6664,"families":6665,"methods":6666,"methodIds":6669,"rows":6670,"failures":120},"splatam2024-table-1","Keetha et al., 2024","Online camera-pose estimation, ATE RMSE [cm]; ScanNet++ baselines run by the authors; Point-SLAM and ORB-SLAM3 fail to track because of large displace…",[3223,6661,6662,6663],"ScanNet (original)","ScanNet++","TUM-RGBD",[568],[25],[6667,3168,5233,2909,3174,6668,892,3175,3177,3179],"DROID-SLAM","ORB-SLAM2",[6178,2915,3182,2448,3184,1511,763,3185,3186],133,{"slug":6672,"sourceId":3186,"sourceLabel":6658,"sourceYear":2267,"table":17,"note":6673,"datasets":6674,"metrics":6675,"families":6676,"methods":6677,"methodIds":6678,"rows":120,"failures":30},"splatam2024-table-3","Rendered depth versus ground-truth depth on ScanNet++ held-out novel views and training views; novel views placed via their GT pose relative to the fi…",[6662],[23],[23],[3177],[3186],{"slug":6680,"sourceId":3186,"sourceLabel":6658,"sourceYear":2267,"table":1072,"note":6681,"datasets":6682,"metrics":6683,"families":6684,"methods":6685,"methodIds":6687,"rows":224,"failures":30},"splatam2024-table-6","Runtime on Replica room0; SplaTAM uses 40 tracking and 60 mapping iterations per frame, SplaTAM-S 10 and 15; per-iteration times not extracted",[3223],[568,38],[25,40],[3174,3175,3177,6686],"SplaTAM-S",[3184,3185,3186],{"slug":6689,"sourceId":6690,"sourceLabel":6691,"sourceYear":374,"table":108,"note":6692,"datasets":6693,"metrics":6695,"families":6696,"methods":6697,"methodIds":6699,"rows":340,"failures":30},"keitaanniemi2023drift-table-2","keitaanniemi2023drift","Keitaanniemi et al., 2023","Per-section ICP alignment RMSE of the SLAM cloud to the TLS reference before post-processing; large sections 52-119 s (Sections 1-8) and small section…",[6694],"Aalto University campus building (own data)",[23],[23],[6698],"GeoSLAM ZEB-REVO (GeoSLAM Hub default)",[],{"slug":6701,"sourceId":6690,"sourceLabel":6691,"sourceYear":374,"table":17,"note":6702,"datasets":6703,"metrics":6704,"families":6705,"methods":6706,"methodIds":6711,"rows":29,"failures":30},"keitaanniemi2023drift-table-3","Discontinuity analysis: M3C2 distances in overlaps between consecutive post-processed sections, averaged over sections; three planes per overlap",[6694],[3919],[78],[6707,6708,6709,6710],"Non-rigid transformation, large sections (Terrascan fit to TLS patches)","Non-rigid transformation, small sections (Terrascan fit to TLS patches)","Rigid transformation, large sections (Terrascan fit to TLS patches)","Rigid transformation, small sections (Terrascan fit to TLS patches)",[6690],{"slug":6713,"sourceId":6690,"sourceLabel":6691,"sourceYear":374,"table":6714,"note":6715,"datasets":6716,"metrics":6717,"families":6718,"methods":6719,"methodIds":6721,"rows":154,"failures":30},"keitaanniemi2023drift-text-sec-5-1","Text Sec.5.1","Drift of the raw SLAM cloud after anchoring the first 99 s to TLS by ICP (RMSE 0.9 cm); M3C2 mean distance on horizontal planes",[6694],[3919],[78],[6720],"GeoSLAM ZEB-REVO before post-processing",[],{"slug":6723,"sourceId":6690,"sourceLabel":6691,"sourceYear":374,"table":6724,"note":6725,"datasets":6726,"metrics":6727,"families":6728,"methods":6729,"methodIds":6730,"rows":356,"failures":30},"keitaanniemi2023drift-text-sec-5-1-1","Text Sec.5.1.1","Approximate drift increase attributed by authors to environment features along the walking path (circa values)",[6694],[23],[23],[6720],[],{"slug":6732,"sourceId":6690,"sourceLabel":6691,"sourceYear":374,"table":2391,"note":6733,"datasets":6734,"metrics":6735,"families":6736,"methods":6737,"methodIds":6740,"rows":63,"failures":30},"keitaanniemi2023drift-text-sec-5-2","Average M3C2 mean distance of horizontal planes after post-processing with large sections, best configuration",[6694],[3919,23],[78,23],[6738,6739],"Sectional post-processing","Sectional post-processing, large sections",[6690],{"slug":6742,"sourceId":6690,"sourceLabel":6691,"sourceYear":374,"table":6743,"note":6744,"datasets":6745,"metrics":6746,"families":6747,"methods":6748,"methodIds":6750,"rows":154,"failures":30},"keitaanniemi2023drift-text-sec-6","Text Sec.6","Residual drift after sectional post-processing against TLS patches (not an independent check: same TLS data used as control)",[6694],[3919],[78],[6749],"Sectional post-processing (best case)",[6690],{"slug":6752,"sourceId":6753,"sourceLabel":6754,"sourceYear":1234,"table":69,"note":6755,"datasets":6756,"metrics":6764,"families":6765,"methods":6766,"methodIds":6768,"rows":1339,"failures":30},"keller2013pointfusion-table-1","keller2013pointfusion","Keller et al., 2013","Average per-frame timings of ICP, dynamic segmentation and fusion; frames input\u002Fprocessed and fps input\u002Fprocessed; input 640x480 except PMD 200x200. I…",[6757,6758,6759,6760,6761,6762,6763],"Ballgame","Flowerpot (Nguyen et al.)","Large Office","Moving Person","PMD","Sim (synthetic)","Teapot (Nguyen et al.)",[148,23,38],[40,23],[6767],"point-based fusion",[6753],{"slug":6770,"sourceId":6753,"sourceLabel":6754,"sourceYear":1234,"table":6771,"note":6772,"datasets":6773,"metrics":6774,"families":6775,"methods":6776,"methodIds":6779,"rows":416,"failures":30},"keller2013pointfusion-text-sec-7","Text Sec. 7","Synthetic Sim scene with ground-truth camera transformations and geometry; errors are means over model points or frames",[6759,6762],[22,75,219,23],[25,40,78,23],[6767,6777,6778],"point-based fusion with ICP pose estimation","point-based fusion with ground-truth camera poses",[6753],{"slug":6781,"sourceId":6782,"sourceLabel":6783,"sourceYear":2267,"table":17,"note":6784,"datasets":6785,"metrics":6787,"families":6788,"methods":6789,"methodIds":6792,"rows":52,"failures":154},"kelly2024blk2go-table-3","kelly2024blk2go","Kelly et al., 2024","Mobile drift test: RMSE of inter-target distances vs Trimble SX10 per collection condition; transformation-free distance comparison",[6786],"Washington Hall, USMA (own data)",[1819],[1821],[6790,6791],"Leica BLK2GO, lidar-only SLAM (cameras covered)","Leica BLK2GO, visual + lidar SLAM (Both)",[],{"slug":6794,"sourceId":6782,"sourceLabel":6783,"sourceYear":2267,"table":3048,"note":6795,"datasets":6796,"metrics":6797,"families":6798,"methods":6799,"methodIds":6801,"rows":63,"failures":30},"kelly2024blk2go-text-sec-4-1","Stationary tripod test, eight consecutive 5-min collections; BLK2GO range to target vs Trimble SX10-derived distance",[6786],[1819],[1821],[6800],"Leica BLK2GO (stationary)",[],{"slug":6803,"sourceId":5824,"sourceLabel":6804,"sourceYear":374,"table":69,"note":6805,"datasets":6806,"metrics":6810,"families":6811,"methods":6812,"methodIds":6819,"rows":6820,"failures":30},"kerbl2023-3dgs-table-1","Kerbl et al., 2023","Novel-view synthesis on held-out views (every 8th photo), average per dataset, with training time, rendering FPS and model memory; all on an A6000 GPU…",[6807,6808,6809],"Deep Blending","Mip-NeRF360","Tanks&Temples",[148,219,23],[40,23],[6813,6814,6815,6816,6817,6818],"INGP-Base","INGP-Big","M-NeRF360","Ours-30K","Ours-7K","Plenoxels",[5824],108,{"slug":6822,"sourceId":6823,"sourceLabel":6824,"sourceYear":1234,"table":91,"note":6825,"datasets":6826,"metrics":6827,"families":6828,"methods":6829,"methodIds":6833,"rows":102,"failures":30},"dvoslam2013-table-i","dvoslam2013","Kerl et al., 2013","TUM RGB-D sequences grouped by scene content (structure and texture present or not) and camera distance; RMSE of translational drift (RPE) in m\u002Fs for…",[2872],[330],[332],[6830,6831,6832],"Depth-only dense odometry","RGB+Depth dense odometry","RGB-only dense odometry",[],{"slug":6835,"sourceId":6823,"sourceLabel":6824,"sourceYear":1234,"table":325,"note":6836,"datasets":6837,"metrics":6838,"families":6839,"methods":6840,"methodIds":6844,"rows":1069,"failures":154},"dvoslam2013-table-ii","All TUM RGB-D freiburg1 sequences; RMSE of translational drift (RPE) in m\u002Fs for frame-to-frame, frame-to-keyframe and frame-to-keyframe with pose-grap…",[2872],[330],[332],[6841,6842,6843],"RGB+D (frame-to-frame)","RGB+D+KF (frame-to-keyframe)","RGB+D+KF+Opt (DVO-SLAM)",[],{"slug":6846,"sourceId":6823,"sourceLabel":6824,"sourceYear":1234,"table":279,"note":6847,"datasets":6848,"metrics":6849,"families":6850,"methods":6851,"methodIds":6856,"rows":2262,"failures":30},"dvoslam2013-table-iii","RMSE of absolute trajectory error (m) on TUM RGB-D sequences versus RGB-D SLAM (Engelhard, Endres et al.), MRSMap and the PCL KinectFusion implementat…",[2872],[568],[25],[6852,6853,6854,6855],"KinFu (PCL KinectFusion) [5]","MRSMap [11]","Ours (DVO-SLAM)","RGB-D SLAM [2], [31]",[4750],{"slug":6858,"sourceId":6823,"sourceLabel":6824,"sourceYear":1234,"table":6351,"note":6859,"datasets":6860,"metrics":6861,"families":6862,"methods":6863,"methodIds":6866,"rows":356,"failures":30},"dvoslam2013-text-sec-v","Average ATE RMSE over the freiburg1 sequences excluding fr1\u002Ffloor",[2872],[568,23,38],[25,40,23],[6864,6865,6841,6843],"DVO-SLAM map update","DVO-SLAM tracking",[],{"slug":6868,"sourceId":6869,"sourceLabel":6870,"sourceYear":698,"table":69,"note":6871,"datasets":6872,"metrics":6874,"families":6875,"methods":6876,"methodIds":6878,"rows":120,"failures":30},"khoshelham2021isprsindoorresults-table-1","khoshelham2021isprsindoorresults","Khoshelham et al., 2021","Accuracy of the manually built reference models: median distance between benchmark points and the nearest visible reference surface, distances above a…",[6873],"ISPRS benchmark on indoor modelling",[23],[23],[6877],"manually reconstructed Revit reference model",[],{"slug":6880,"sourceId":6869,"sourceLabel":6870,"sourceYear":698,"table":17,"note":6881,"datasets":6882,"metrics":6883,"families":6884,"methods":6885,"methodIds":6895,"rows":849,"failures":30},"khoshelham2021isprsindoorresults-table-3","Wall-element evaluation of submitted automatic indoor models for TUB1: completeness and correctness at 10 cm buffer, accuracy (median distance) at 10…",[6873],[75,76,23],[78,23],[6886,6887,6888,6889,6890,6891,6892,6893,6894],"Ahmed et al.","Bassier & Vergauwen","Lim et al.","Maset et al.","Ochmann et al.","Previtali et al.","Su et al.","Tran & Khoshelham","Tran et al.",[],{"slug":6897,"sourceId":6869,"sourceLabel":6870,"sourceYear":698,"table":33,"note":6898,"datasets":6899,"metrics":6900,"families":6901,"methods":6902,"methodIds":6905,"rows":849,"failures":30},"khoshelham2021isprsindoorresults-table-4","Wall-element evaluation of submitted automatic indoor models for TUB2: completeness and correctness at 10 cm buffer, accuracy (median distance) at 10…",[6873],[75,76,23],[78,23],[6886,6903,6887,6904,6888,6889,6890,6892,6894],"Ai","Cui et al.",[],{"slug":6907,"sourceId":6869,"sourceLabel":6870,"sourceYear":698,"table":244,"note":6908,"datasets":6909,"metrics":6910,"families":6911,"methods":6912,"methodIds":6913,"rows":1315,"failures":30},"khoshelham2021isprsindoorresults-table-5","Wall-element evaluation of submitted automatic indoor models for Fire Brigade: completeness and correctness at 10 cm buffer, accuracy (median distance…",[6873],[75,76,23],[78,23],[6903,6887,6888,6889,6890,6893,6894],[],{"slug":6915,"sourceId":6869,"sourceLabel":6870,"sourceYear":698,"table":1072,"note":6916,"datasets":6917,"metrics":6918,"families":6919,"methods":6920,"methodIds":6921,"rows":1315,"failures":30},"khoshelham2021isprsindoorresults-table-6","Wall-element evaluation of submitted automatic indoor models for UVigo: completeness and correctness at 10 cm buffer, accuracy (median distance) at 10…",[6873],[75,76,23],[78,23],[6903,6887,6888,6889,6890,6893,6894],[],{"slug":6923,"sourceId":6869,"sourceLabel":6870,"sourceYear":698,"table":621,"note":6924,"datasets":6925,"metrics":6926,"families":6927,"methods":6928,"methodIds":6929,"rows":1315,"failures":30},"khoshelham2021isprsindoorresults-table-7","Wall-element evaluation of submitted automatic indoor models for UoM: completeness and correctness at 10 cm buffer, accuracy (median distance) at 10 c…",[6873],[75,76,23],[78,23],[6887,6888,6889,6890,6892,6893,6894],[],{"slug":6931,"sourceId":6869,"sourceLabel":6870,"sourceYear":698,"table":633,"note":6932,"datasets":6933,"metrics":6934,"families":6935,"methods":6936,"methodIds":6937,"rows":120,"failures":30},"khoshelham2021isprsindoorresults-table-8","Wall-element evaluation of submitted automatic indoor models for Grainger Museum: completeness and correctness at 10 cm buffer, accuracy (median dista…",[6873],[75,76,23],[78,23],[6886,6887],[],{"slug":6939,"sourceId":6940,"sourceLabel":6941,"sourceYear":107,"table":325,"note":6942,"datasets":6943,"metrics":6944,"families":6945,"methods":6946,"methodIds":6949,"rows":578,"failures":30},"scancontext2018-table-ii","scancontext2018","Kim & Kim, 2018","Average time on KITTI 00; 0.6 m3 grid downsampling for all methods (Sec. IV-D); scan context creation includes optional root-shift augmentation; imple…",[1997],[38],[40],[5498,2025,6947,6948,5503],"Scan context-10","Scan context-50",[2029,6940],{"slug":6951,"sourceId":6940,"sourceLabel":6941,"sourceYear":107,"table":1221,"note":6952,"datasets":6953,"metrics":6954,"families":6955,"methods":6956,"methodIds":6958,"rows":154,"failures":30},"scancontext2018-text-sec-iv-d","Single scan context creation without augmentation",[1997],[38],[40],[6957],"Scan context",[6940],{"slug":6960,"sourceId":6961,"sourceLabel":6962,"sourceYear":306,"table":91,"note":6963,"datasets":6964,"metrics":6965,"families":6966,"methods":6967,"methodIds":6970,"rows":340,"failures":30},"ltmapper2022-table-i","ltmapper2022","Kim & Kim, 2022","Change composition check on MulRan KAIST: KAIST 01 restored from KAIST 04 by chaining delta maps (01 to 02 and 02 to 04) compared with the real KAIST…",[671],[2343,23],[78,23],[6968,6969],"Neg. Pair (01 vs 04)","Pos. Pair (01 vs Restored 01)",[6961],{"slug":6972,"sourceId":6961,"sourceLabel":6962,"sourceYear":306,"table":325,"note":6973,"datasets":6974,"metrics":6975,"families":6976,"methods":6977,"methodIds":6982,"rows":416,"failures":30},"ltmapper2022-table-ii","Efficiency of LT-map delta-map chaining versus saving whole snapshots for the Fig. 10 scene (KAIST 04 vs 01); hardware not reported",[671],[219,23],[40,23],[6978,6979,6980,6981],"Baseline (saving whole snapshot)","Baseline, w\u002F HD removal","Baseline, w\u002Fo HD removal","Ours (LT-map, delta map chaining)",[6961],{"slug":6984,"sourceId":6985,"sourceLabel":6986,"sourceYear":107,"table":108,"note":6987,"datasets":6988,"metrics":6990,"families":6991,"methods":6992,"methodIds":6995,"rows":598,"failures":30},"kim2018construction-table-2","kim2018construction","Kim et al., 2018a","Testbed #1 construction site on the Georgia Tech campus, six scan positions; RMSE after final alignment for every scan pair, ICP (point-to-point LM-IC…",[6989],"authors' Testbed #1",[23],[23],[6993,6994],"ICP (point-to-point, Levenberg-Marquardt)","P-M (plane matching)",[6985],{"slug":6997,"sourceId":6985,"sourceLabel":6986,"sourceYear":107,"table":17,"note":6998,"datasets":6999,"metrics":7000,"families":7001,"methods":7002,"methodIds":7006,"rows":608,"failures":63},"kim2018construction-table-3","Testbed #1 construction site; scans #1, #4, #6 chosen because ICP fails or is worst there; deviation from reference axes and RMSE per registration sta…",[6989],[23],[23],[7003,7004,7005],"final alignment (plane matching with three planes and a corner point)","initial alignment (SURF + RANSAC + Kabsch)","original scan before registration",[6985],{"slug":7008,"sourceId":6985,"sourceLabel":6986,"sourceYear":107,"table":33,"note":7009,"datasets":7010,"metrics":7012,"families":7013,"methods":7014,"methodIds":7015,"rows":608,"failures":63},"kim2018construction-table-4","Testbed #2 near a target building, three scan positions, all pairs below 89% overlap so plane matching is used; deviation angles and RMSE per stage",[7011],"authors' Testbed #2",[23],[23],[7003,7004,7005],[6985],{"slug":7017,"sourceId":6985,"sourceLabel":6986,"sourceYear":107,"table":244,"note":7018,"datasets":7019,"metrics":7021,"families":7022,"methods":7023,"methodIds":7024,"rows":224,"failures":63},"kim2018construction-table-5","Testbed #3 indoor environment, two scan positions; initial alignment by visual features, final alignment by planes and a corner point",[7020],"authors' Testbed #3",[23],[23],[7003,7004,7005],[6985],{"slug":7026,"sourceId":6985,"sourceLabel":6986,"sourceYear":107,"table":1072,"note":7027,"datasets":7028,"metrics":7030,"families":7031,"methods":7032,"methodIds":7034,"rows":224,"failures":30},"kim2018construction-table-6","final result of automatic registration per testbed (Testbed #1 uses only scans #1, #4, #6)",[7029],"authors' testbeds",[23],[23],[7033],"proposed framework (final result)",[6985],{"slug":7036,"sourceId":7037,"sourceLabel":7038,"sourceYear":107,"table":17,"note":7039,"datasets":7040,"metrics":7042,"families":7043,"methods":7044,"methodIds":7046,"rows":102,"failures":30},"kim2018slamdriven-table-3","kim2018slamdriven","Kim et al., 2018b","Indoor building floor, six static scans registered with SLAM transforms; NN RMSE and axis deviation angle vs the higher scan ID taken as ground truth",[7041],"GRoMI indoor testbed (one building floor)",[3417,23],[78,23],[7045],"SLAM-driven registration (proposed)",[7037],{"slug":7048,"sourceId":7037,"sourceLabel":7038,"sourceYear":107,"table":33,"note":7049,"datasets":7050,"metrics":7051,"families":7052,"methods":7053,"methodIds":7055,"rows":641,"failures":120},"kim2018slamdriven-table-4","Same indoor scans registered with the authors' earlier image-feature plus plane-matching method [52]; RMSE and deviation angles as in Table 3",[7041],[3417,23],[78,23],[7054],"Feature matching registration [52]",[],{"slug":7057,"sourceId":7037,"sourceLabel":7038,"sourceYear":107,"table":244,"note":7058,"datasets":7059,"metrics":7061,"families":7062,"methods":7063,"methodIds":7064,"rows":3819,"failures":30},"kim2018slamdriven-table-5","Outdoor testbed among buildings, scans 12-20 m apart registered with SLAM transforms; SLAM inter-scan distance vs measured actual distance (method not…",[7060],"GRoMI outdoor testbed",[3417,23],[78,23],[7045],[7037],{"slug":7066,"sourceId":7037,"sourceLabel":7038,"sourceYear":107,"table":1072,"note":7067,"datasets":7068,"metrics":7069,"families":7070,"methods":7071,"methodIds":7073,"rows":29,"failures":30},"kim2018slamdriven-table-6","Outdoor scans registered with feature point plus plane matching by Kim et al. [53]; NN RMSE and deviation angles vs higher scan ID",[7060],[3417,23],[78,23],[7072],"Feature and planar matching (Kim et al. [53])",[6985],{"slug":7075,"sourceId":7076,"sourceLabel":7077,"sourceYear":16,"table":17,"note":7078,"datasets":7079,"metrics":7081,"families":7082,"methods":7083,"methodIds":7085,"rows":608,"failures":30},"kim2019uavassisted-table-3","kim2019uavassisted","Kim et al., 2019","Scan-planning simulation on the UAV-derived voxel map; lack of completeness (LoC) = % of voxels not visible from any selected location (lower is bette…",[7080],"Georgia Tech test site UAV map",[76,23],[78,23],[7084],"UAV-assisted scan planning (ray tracing + greedy cover)",[7076],{"slug":7087,"sourceId":7076,"sourceLabel":7077,"sourceYear":16,"table":33,"note":7088,"datasets":7089,"metrics":7091,"families":7092,"methods":7093,"methodIds":7097,"rows":120,"failures":30},"kim2019uavassisted-table-4","Time required to build a registered point cloud from six scans, GRoMI with UAV prior vs commercial TLS workflow",[7090],"Georgia Tech earthquake experiment structure site (own data)",[23],[23],[7094,7095,7096],"Commercial laser scanner (TLS)","GRoMI","GRoMI (UAV map 9 min + point cloud generation 19 min + scan planning 4 min)",[7076],{"slug":7099,"sourceId":7076,"sourceLabel":7077,"sourceYear":16,"table":952,"note":7100,"datasets":7101,"metrics":7102,"families":7103,"methods":7104,"methodIds":7106,"rows":154,"failures":30},"kim2019uavassisted-text-sec-5","Registered GRoMI point cloud compared with commercial TLS cloud taken as ground truth; comparison and alignment method not reported",[7090],[75],[78],[7105],"GRoMI, Hector SLAM coarse + ICP fine registration (proposed)",[7076],{"slug":7108,"sourceId":7109,"sourceLabel":7110,"sourceYear":306,"table":108,"note":7111,"datasets":7112,"metrics":7114,"families":7115,"methods":7116,"methodIds":7125,"rows":618,"failures":30},"kim2022scaffoldrobotdog-table-2","kim2022scaffoldrobotdog","Kim et al., 2022a","RandLA-Net pre-trained on Semantic3D, fine-tuned with different re-trained layer sets; mIoU on validation data",[7113],"Validation data (Yonsei University)",[23],[23],[7117,7118,7119,7120,7121,7122,7123,7124],"Re-trained: all decoders","Re-trained: all decoders and all fully connected layers","Re-trained: all encoders","Re-trained: encoder 5, MLP, decoder 1","Re-trained: encoders 1-5, MLP, decoders 1-5","Re-trained: encoders 2-5, MLP, decoders 1-4","Re-trained: encoders 3-5, MLP, decoders 1-3 (selected)","Re-trained: encoders 4-5, MLP, decoders 1-2",[7109],{"slug":7127,"sourceId":7109,"sourceLabel":7110,"sourceYear":306,"table":17,"note":7128,"datasets":7129,"metrics":7131,"families":7132,"methods":7133,"methodIds":7135,"rows":120,"failures":30},"kim2022scaffoldrobotdog-table-3","Point-wise segmentation of the LIO-SAM point cloud of the unseen test site",[7130],"Dataset 3 (Chungang University)",[23],[23],[7134],"Robot dog + LIO-SAM + fine-tuned RandLA-Net (proposed)",[7109],{"slug":7137,"sourceId":7109,"sourceLabel":7110,"sourceYear":306,"table":33,"note":7138,"datasets":7139,"metrics":7141,"families":7142,"methods":7143,"methodIds":7144,"rows":120,"failures":30},"kim2022scaffoldrobotdog-table-4","Ablation: same test data after removing points of a mobile scaffold not covered by training data",[7140],"Dataset 3 (Chungang University), mobile scaffold removed",[23],[23],[7134],[7109],{"slug":7146,"sourceId":7147,"sourceLabel":7148,"sourceYear":306,"table":731,"note":7149,"datasets":7150,"metrics":7151,"families":7152,"methods":7153,"methodIds":7154,"rows":29,"failures":30},"scancontextpp2022-table-iv","scancontextpp2022","Kim et al., 2022b","ATE (mean \u002F max) of LeGO-LOAM odometry vs Scan Context integrated SC-LeGO-LOAM (iSAM2 pose graph)",[671],[329,22],[25],[2197,4780],[395,7147],{"slug":7156,"sourceId":7147,"sourceLabel":7148,"sourceYear":306,"table":803,"note":7157,"datasets":7158,"metrics":7159,"families":7160,"methods":7161,"methodIds":7164,"rows":2882,"failures":30},"scancontextpp2022-table-ix","AUC with respect to correctness threshold (baseline 8 m)",[469,671],[23],[23],[7162,7163],"Cart Context (CC)","Polar Context (PC)",[7147],{"slug":7166,"sourceId":7147,"sourceLabel":7148,"sourceYear":306,"table":818,"note":7167,"datasets":7168,"metrics":7169,"families":7170,"methods":7171,"methodIds":7176,"rows":641,"failures":30},"scancontextpp2022-table-v","Time cost per query in ms; ours and M2DP measured in Matlab, SegMatch copied from its paper, PointNetVLAD on GPU",[2070],[38],[40],[2025,7172,7173,7174,7175],"Ours (A-PC)","Ours (PC)","PointNetVLAD","SegMatch",[2029,7177,7147],"pointnetvlad2018",{"slug":7179,"sourceId":7147,"sourceLabel":7148,"sourceYear":306,"table":852,"note":7180,"datasets":7181,"metrics":7182,"families":7183,"methods":7184,"methodIds":7189,"rows":29,"failures":30},"scancontextpp2022-table-viii","AUC of precision-recall: retrieval key (k-d tree, k = 1) vs brute-force full descriptor search; 8 m correctness threshold",[469,671],[23],[23],[7185,7186,7187,7188],"Cart Context (CC), full descriptor","Cart Context (CC), retrieval key","Polar Context (PC), full descriptor","Polar Context (PC), retrieval key",[7147],{"slug":7191,"sourceId":7147,"sourceLabel":7148,"sourceYear":306,"table":7192,"note":7193,"datasets":7194,"metrics":7196,"families":7197,"methods":7198,"methodIds":7201,"rows":63,"failures":30},"scancontextpp2022-text-sec-vii-e","Text Sec.VII-E","Semi-metric 1-DoF localization at max F1 score vs ground truth",[7195],"NAVER LABS",[23],[23],[7199,7200],"A-CC","A-PC",[7147],{"slug":7203,"sourceId":7147,"sourceLabel":7148,"sourceYear":306,"table":7204,"note":7205,"datasets":7206,"metrics":7207,"families":7208,"methods":7209,"methodIds":7210,"rows":63,"failures":30},"scancontextpp2022-text-sec-vii-g","Text Sec.VII-G","Mean total per-query execution time of PC module, periodic k-d tree rebuild every 10 s included",[469,7195],[38],[40],[7173],[7147],{"slug":7212,"sourceId":3866,"sourceLabel":7213,"sourceYear":1694,"table":69,"note":7214,"datasets":7215,"metrics":7217,"families":7218,"methods":7219,"methodIds":7221,"rows":416,"failures":30},"ptam2007-table-1","Klein & Murray, 2007","Tracking time for a typical frame broken down by step, map size M=4000 (Sec. 7.1, Table 1)",[7216],"own live video (Sec. 7.1)",[38],[40],[7220],"PTAM (proposed system)",[3866],{"slug":7223,"sourceId":3866,"sourceLabel":7213,"sourceYear":1694,"table":108,"note":7224,"datasets":7225,"metrics":7227,"families":7228,"methods":7229,"methodIds":7230,"rows":120,"failures":30},"ptam2007-table-2","Mean bundle adjustment time by map size (keyframes); timings vary with map size and scene structure",[7226],"typical timings from live operation (no specific sequence named; Sec. 7.2)",[23],[23],[7220],[3866],{"slug":7232,"sourceId":3866,"sourceLabel":7213,"sourceYear":1694,"table":7233,"note":7234,"datasets":7235,"metrics":7237,"families":7238,"methods":7239,"methodIds":7240,"rows":154,"failures":30},"ptam2007-text-sec-7-2","Text Sec. 7.2","Largest map produced; beyond the small-workspace design goal, keyframe and point addition impaired but tracking at frame rate",[7236],"own live video",[23],[23],[7220],[3866],{"slug":7242,"sourceId":3866,"sourceLabel":7213,"sourceYear":1694,"table":7243,"note":7244,"datasets":7245,"metrics":7247,"families":7248,"methods":7249,"methodIds":7252,"rows":416,"failures":30},"ptam2007-text-sec-7-3","Text Sec. 7.3","Synthetic rendered scene (two textured walls at right angles), 600 frames, camera travels 18.2 m; trajectories aligned to ground truth by 6-DOF rigid…",[7246],"synthetic sequence (own)",[23,38],[40,23],[7250,7251],"EKF-SLAM (SceneLib-based implementation with JCBB)","Proposed method (PTAM)",[3866],{"slug":7254,"sourceId":5265,"sourceLabel":7255,"sourceYear":681,"table":7256,"note":7257,"datasets":7258,"metrics":7260,"families":7261,"methods":7262,"methodIds":7271,"rows":2882,"failures":30},"chisel2015-fig-9e","Klingensmith et al., 2015","Fig. 9e","Time to fuse a single depth scan on the 'Room' (apartment, Fig. 1b) dataset for each fusion mode; mean and standard deviation printed as 'mean +- std'",[7259],"authors' Room (apartment) dataset",[38],[40],[7263,7264,7265,7266,7267,7268,7269,7270],"CHISEL: Projection mapping, colour, carving","CHISEL: Projection mapping, colour, no carving","CHISEL: Projection mapping, no colour, carving","CHISEL: Projection mapping, no colour, no carving","CHISEL: Raycast, colour, carving","CHISEL: Raycast, colour, no carving","CHISEL: Raycast, no colour, carving","CHISEL: Raycast, no colour, no carving",[5265],{"slug":7273,"sourceId":5265,"sourceLabel":7255,"sourceYear":681,"table":91,"note":7274,"datasets":7275,"metrics":7277,"families":7278,"methods":7279,"methodIds":7281,"rows":356,"failures":30},"chisel2015-table-i","Voxel statistics for the Freiburg 5 m depth-frustum reconstruction; share of the bounding box per voxel class (culled voxels are not stored)",[7276],"Freiburg (TUM) RGB-D benchmark",[23],[23],[7280],"CHISEL spatially hashed TSDF",[5265],{"slug":7283,"sourceId":5265,"sourceLabel":7255,"sourceYear":681,"table":325,"note":7284,"datasets":7285,"metrics":7286,"families":7287,"methods":7288,"methodIds":7291,"rows":618,"failures":30},"chisel2015-table-ii","Per-frame mesh generation and TSDF update (colorization, space carving, projection mapping) on the 'Room' dataset; platform not stated (update time eq…",[7259],[38],[40],[7289,7290],"16^3 Spatial Hashing (CHISEL)","256^3 Fixed Grid (baseline)",[5265],{"slug":7293,"sourceId":5265,"sourceLabel":7255,"sourceYear":681,"table":7294,"note":7295,"datasets":7296,"metrics":7298,"families":7299,"methods":7300,"methodIds":7302,"rows":154,"failures":30},"chisel2015-text-sec-iii-j","Text Sec.III-J","Approximate drift at the end of a roughly 175 m office corridor scanned handheld, with visual odometry and dense alignment only (before offline bundle…",[7297],"authors' office corridor",[1551],[1553],[7301],"CHISEL (VIO + scan-to-model ICP, no loop closure)",[5265],{"slug":7304,"sourceId":5265,"sourceLabel":7255,"sourceYear":681,"table":1221,"note":7305,"datasets":7306,"metrics":7308,"families":7309,"methods":7310,"methodIds":7316,"rows":120,"failures":30},"chisel2015-text-sec-iv-d","Memory usage stated in the text; values qualified in the text as 'nearly' or 'around' are approximate; FG = fixed grid tightly fitting the explored vo…",[7276,7307],"authors' Dragon dataset",[219],[40],[7311,7312,7313,7314,7315],"FG baseline (text: nearly 2GB)","FG baseline (text: nearly 300MB)","SH (CHISEL)","SH (CHISEL) (text: around 700MB)","SH (CHISEL) (text: only 50MB)",[5265],{"slug":7318,"sourceId":7319,"sourceLabel":7320,"sourceYear":345,"table":108,"note":7321,"datasets":7322,"metrics":7323,"families":7324,"methods":7325,"methodIds":7341,"rows":7342,"failures":30},"knapitsch2017tnt-table-2","knapitsch2017tnt","Knapitsch et al., 2017","F-score of 15 SfM+MVS pipelines at scene-specific threshold tau after Sim(3) alignment (Umeyama then scaled ICP), voxel resampling at tau\u002F2 and croppi…",[6106],[74,23],[78,23],[7326,7327,7328,7329,7330,7331,7332,7333,7334,7335,7336,7337,7338,7339,7340],"Bundler + PMVS","COLMAP","MVE","MVE + SMVS","OpenMVG + MVE","OpenMVG + OpenMVS","OpenMVG + PMVS","OpenMVG + SMVS","OpenMVG-G + OpenMVS","Pix4D","Theia-G + OpenMVS","Theia-I + OpenMVS","VisualSfM + CMPMVS","VisualSfM + OpenMVS","VisualSfM + PMVS",[],150,{"slug":7344,"sourceId":2336,"sourceLabel":7345,"sourceYear":4540,"table":4401,"note":7346,"datasets":7347,"metrics":7348,"families":7349,"methods":7350,"methodIds":7352,"rows":154,"failures":30},"hector2011-text-sec-v","Kohlbrecher et al., 2011","Navigation filter update rate; SLAM pose fused asynchronously",[3980],[148],[40],[7351],"navigation filter (EKF)",[2336],{"slug":7354,"sourceId":2336,"sourceLabel":7345,"sourceYear":4540,"table":3393,"note":7355,"datasets":7356,"metrics":7358,"families":7359,"methods":7360,"methodIds":7362,"rows":63,"failures":154},"hector2011-text-sec-vi-c","Handheld embedded mapping system; logged sensor data replayed to the SLAM system on the Atom Z530 CPU",[7357],"RoboCup 2011 Rescue Arena and Dagstuhl new building logs",[113,23],[40,23],[7361],"Hector SLAM",[2336],{"slug":7364,"sourceId":394,"sourceLabel":7365,"sourceYear":16,"table":69,"note":7366,"datasets":7367,"metrics":7369,"families":7370,"methods":7371,"methodIds":7374,"rows":416,"failures":63},"koide2019-hdlgraphslam-table-1","Koide et al., 2019","Offline map building time for a 45 min, about 2400 m indoor sequence; LeGO-LOAM not listed because it only runs in real time",[7368],"Own indoor sequence",[23],[23],[7372,7373],"BLAM","Ours (graph SLAM)",[394],{"slug":7376,"sourceId":394,"sourceLabel":7365,"sourceYear":16,"table":108,"note":7377,"datasets":7378,"metrics":7380,"families":7381,"methods":7382,"methodIds":7385,"rows":224,"failures":274},"koide2019-hdlgraphslam-table-2","Corridor localization; error is the difference between the predicted initial guess and the NDT-corrected pose (no ground truth); time is per frame",[7379],"Own corridor sequences",[23,38],[40,23],[7383,7384],"With prediction (UKF angular-velocity prediction + NDT)","Without prediction (previous NDT result as initial guess)",[394],{"slug":7387,"sourceId":394,"sourceLabel":7365,"sourceYear":16,"table":17,"note":7388,"datasets":7389,"metrics":7391,"families":7392,"methods":7393,"methodIds":7398,"rows":224,"failures":30},"koide2019-hdlgraphslam-table-3","People detection on 102 frames with two persons walking side by side; ablation of split-merge clustering and human classifier",[7390],"Own people-detection sequence",[23],[23],[7394,7395,7396,7397],"With split-merge, with classifier","With split-merge, without classifier","Without split-merge, with classifier","Without split-merge, without classifier",[394],{"slug":7400,"sourceId":394,"sourceLabel":7365,"sourceYear":16,"table":33,"note":7401,"datasets":7402,"metrics":7404,"families":7405,"methods":7406,"methodIds":7408,"rows":120,"failures":30},"koide2019-hdlgraphslam-table-4","Position difference between the portable system and OpenPTrack (nine Kinect v2) while an observer followed a walking subject in a corridor; min column…",[7403],"Own corridor comparison",[23],[23],[7407],"Proposed portable system vs OpenPTrack",[394],{"slug":7410,"sourceId":7411,"sourceLabel":7412,"sourceYear":698,"table":91,"note":7413,"datasets":7414,"metrics":7416,"families":7417,"methods":7418,"methodIds":7431,"rows":907,"failures":30},"interactiveslam2021-table-i","interactiveslam2021","Koide et al., 2021a","PASCO Mobile Measurement System outdoor dataset (about 200 m x 400 m, 20 min); ground truth is the 3D LiDAR position tracked by static total stations…",[7415],"PASCO Mobile Measurement System outdoor dataset (released via SMRT-AIST)",[22,330],[25,332],[4330,7419,7420,7421,7422,7423,7424,7425,7426,7427,7428,7429,7430],"LOAM [1]","LeGO-LOAM [2] (with loop closure)","LeGO-LOAM [2] (without loop closure)","Proposed: Base (LOAM)","Proposed: Edge refinement (with loop closure)","Proposed: Loop closing (with loop closure)","Proposed: Plane constraints (with loop closure)","SuMa [7] (with loop closure)","SuMa [7] (without loop closure)","ethzasl_icp_mapping [33]","hdl_graph_slam [4] (with loop closure)","hdl_graph_slam [4] (without loop closure)",[392,7411,394,395,2118,7432,432],"pomerleau2014_icpmapper",{"slug":7434,"sourceId":7411,"sourceLabel":7412,"sourceYear":698,"table":7435,"note":7436,"datasets":7437,"metrics":7439,"families":7440,"methods":7441,"methodIds":7444,"rows":63,"failures":30},"interactiveslam2021-text-sec-iii-c","Text Sec. III.C","Example correction sequence (Fig. 3): chi-square distance of the pose graph before pose-constraint refinement",[7438],"example dataset of Fig. 3",[23],[23],[7442,7443],"after pose constraint refinement","before pose constraint refinement",[7411],{"slug":7446,"sourceId":7447,"sourceLabel":7448,"sourceYear":698,"table":91,"note":7449,"datasets":7450,"metrics":7452,"families":7453,"methods":7454,"methodIds":7459,"rows":578,"failures":30},"koide2021vgicp-table-i","koide2021vgicp","Koide et al., 2021b","simulated Velodyne HDL-32e sequence from the authors' ray-casting simulator; consecutive-frame scan-matching odometry; ATE as RMS of position and rota…",[7451],"authors' simulated LiDAR sequence",[568],[25],[7455,7456,522,7457,7458],"GICP(PCL)","GICP(ours)","NDT(4.0m)","VGICP(1.0m)",[478,7447,3292],{"slug":7461,"sourceId":7447,"sourceLabel":7448,"sourceYear":698,"table":325,"note":7462,"datasets":7463,"metrics":7465,"families":7466,"methods":7467,"methodIds":7476,"rows":7477,"failures":30},"koide2021vgicp-table-ii","eight real Velodyne HDL-32e sequences of about 120 m, about 15,000 points per frame; consecutive-frame registration; error of the last frame against a…",[7464],"authors' HDL-32e sequences",[1551,148],[40,1553],[7468,7469,522,7470,7471,7472,7473,7474,7475],"GICP (PCL)","GICP (ours)","NDT (0.5m)","NDT (1.0m)","NDT (2.0m)","NDT (4.0m)","VGICP (0.5m)","VGICP (1.0m)",[478,7447,3292],29,{"slug":7479,"sourceId":7447,"sourceLabel":7448,"sourceYear":698,"table":2225,"note":7480,"datasets":7481,"metrics":7482,"families":7483,"methods":7484,"methodIds":7490,"rows":120,"failures":30},"koide2021vgicp-text-sec-iv-a","average processing time per scan on the simulated sequence, as stated in the Sec. IV-A text (Fig. 4 is a plot and was not read off); the text does not…",[7451],[38],[40],[7468,7485,7486,7487,7488,7489],"GICP (ours, multi-thread)","GICP (ours, single-thread)","VGICP (GPU)","VGICP (multi-thread)","VGICP (single-thread)",[7447,3292],{"slug":7492,"sourceId":7493,"sourceLabel":7494,"sourceYear":2267,"table":91,"note":7495,"datasets":7496,"metrics":7498,"families":7499,"methods":7500,"methodIds":7504,"rows":641,"failures":274},"glim2024-table-i","glim2024","Koide et al., 2024","Simulated corridor 40 m wide with LiDAR range limited to 15 m so range data fully degenerate mid-corridor; five IMU noise levels; ATE via evo; loop cl…",[7497],"simulation (Velodyne VLP-16 model, OpenVINS IMU synthesis)",[329],[25],[7501,7502,7503],"FAST-LIO2 [5]","GLIM","LIO-SAM [10]",[321,7493,338],{"slug":7506,"sourceId":7493,"sourceLabel":7494,"sourceYear":2267,"table":325,"note":7507,"datasets":7508,"metrics":7510,"families":7511,"methods":7512,"methodIds":7514,"rows":608,"failures":30},"glim2024-table-ii","Eight real sequences (path 2.2-4.8 m) with a Livox Avia moved between two pillars while facing a flat wall; ground truth from AprilTag bundle adjustme…",[7509],"authors' flat-wall degeneration sequences",[329],[25],[7501,7502,7513],"VoxelMap [61]",[321,7493,7515],"yuan2022voxelmap",{"slug":7517,"sourceId":7493,"sourceLabel":7494,"sourceYear":2267,"table":279,"note":7518,"datasets":7519,"metrics":7521,"families":7522,"methods":7523,"methodIds":7524,"rows":340,"failures":30},"glim2024-table-iii","Cross-sensor test with one parameter set; reference trajectories from alignment to a FARO Focus environment point cloud; RTE sub-trajectory length 2 m…",[7520],"authors' cross-sensor sequences",[329,330],[25,332],[7502],[7493],{"slug":7526,"sourceId":7493,"sourceLabel":7494,"sourceYear":2267,"table":818,"note":7527,"datasets":7528,"metrics":7530,"families":7531,"methods":7532,"methodIds":7541,"rows":339,"failures":30},"glim2024-table-v","Multi-Camera Newer College (Ouster OS0-128, Alphasense Core); translational ATE; unlabeled rows are the no-loop-closure variant of the method printed…",[7529],"Multi-Camera Newer College",[329],[25],[7533,7534,7535,7501,7536,7537,7538,7539,7540],"CLINS [11] (with loop closure)","CLINS [11] (without loop closure; unlabeled row above CLINS)","DLO [14]","GLIM (odometry, without loop closure; unlabeled row above GLIM)","GLIM (with loop closure)","LINS [3]","LIO-SAM [10] (with loop closure)","LIO-SAM [10] (without loop closure; unlabeled row above LIO-SAM)",[2088,321,7493,4783,338],{"slug":7543,"sourceId":7493,"sourceLabel":7494,"sourceYear":2267,"table":838,"note":7544,"datasets":7545,"metrics":7546,"families":7547,"methods":7548,"methodIds":7549,"rows":356,"failures":30},"glim2024-table-vii","Processing time on the park sequence (longest in Newer College); submaps created about every 2 s; maximum global optimization time about 250 ms (text)",[7529],[23,38],[40,23],[7502],[7493],{"slug":7551,"sourceId":7493,"sourceLabel":7494,"sourceYear":2267,"table":7552,"note":7553,"datasets":7554,"metrics":7555,"families":7556,"methods":7557,"methodIds":7566,"rows":2420,"failures":30},"glim2024-table-x","Table X","NTU VIRAL UAV dataset, Average column only (per-sequence eee\u002Fnya\u002Fsbs values omitted); caption says Relative Trajectory Error but Sec. VI-D describes t…",[1636],[329],[25],[7558,7559,7560,7561,7562,7537,7503,7563,7564,7565,7513],"BALM [39]","FAST-LIO2 [25]","FAST-LIVO [67]","GLIM (LiDAR-IMU)","GLIM (with camera)","MLOAM [65] †","SLICT [68]","VIRAL-SLAM [66] †",[7567,321,815,7493,338,7515],"balm2021",{"slug":7569,"sourceId":7570,"sourceLabel":7571,"sourceYear":2267,"table":7572,"note":7573,"datasets":7574,"metrics":7576,"families":7577,"methods":7578,"methodIds":7586,"rows":429,"failures":30},"koide2024smallgicp-text-benchmark-md-accuracy","koide2024smallgicp","Koide, 2024","Text BENCHMARK.md Accuracy","repository documentation linked from the paper (BENCHMARK.md, master branch, fetched 2026-09-25), not peer-reviewed text; odometry benchmark on KITTI…",[7575],"KITTI odometry sequence 00",[329,23],[25,23],[7579,7580,7581,7582,7583,7584,7585],"fast_gicp","fast_vgicp","pcl_gicp","small_gicp","small_gicp (omp)","small_gicp (tbb)","small_vgicp",[7447,7570,3292],{"slug":7588,"sourceId":7570,"sourceLabel":7571,"sourceYear":2267,"table":7589,"note":7590,"datasets":7591,"metrics":7592,"families":7593,"methods":7594,"methodIds":7596,"rows":154,"failures":30},"koide2024smallgicp-text-benchmark-md-odometry-estimation","Text BENCHMARK.md Odometry estimation","repository documentation linked from the paper (master branch, fetched 2026-09-25); not peer-reviewed text",[7575],[23],[23],[7595],"small_gicp::GICP (single-thread) vs fast_gicp::GICP",[7570],{"slug":7598,"sourceId":7570,"sourceLabel":7571,"sourceYear":2267,"table":7599,"note":7600,"datasets":7601,"metrics":7603,"families":7604,"methods":7605,"methodIds":7610,"rows":356,"failures":30},"koide2024smallgicp-text-benchmark-results","Text Benchmark results","speed ratios stated in the JOSS paper; details deferred to BENCHMARK.md (KITTI sequence 00); benchmark machine not stated",[7602],"KITTI odometry sequence 00 (per BENCHMARK.md)",[23],[23],[7606,7607,7608,7609],"small_gicp::GICP (single-thread) vs pcl::GICP","small_gicp::KdTree multi-threaded construction vs nanoflann","small_gicp::voxelgrid_sampling (6 threads) vs pcl::VoxelGrid","small_gicp::voxelgrid_sampling (single-thread) vs pcl::VoxelGrid",[7570],{"slug":7612,"sourceId":7570,"sourceLabel":7571,"sourceYear":2267,"table":7613,"note":7614,"datasets":7615,"metrics":7617,"families":7618,"methods":7619,"methodIds":7621,"rows":154,"failures":30},"koide2024smallgicp-text-statement-of-need","Text Statement of need","single-thread speed gain claimed in the Statement of need of the JOSS paper; no dataset, baseline library or machine named for this figure",[7616],"not stated in the Statement of need (BENCHMARK.md benchmarks use KITTI 00)",[23],[23],[7620],"small_gicp pipeline (single-thread) vs existing libraries",[7570],{"slug":7623,"sourceId":7624,"sourceLabel":7625,"sourceYear":1032,"table":7626,"note":7627,"datasets":7628,"metrics":7633,"families":7634,"methods":7635,"methodIds":7640,"rows":120,"failures":356},"karto-spa2010-text-sec-i-vi-vii","karto_spa2010","Konolige et al., 2010","Text Sec. I, VI, VII","Full nonlinear optimization of the MIT corridor graph from odometry initialization",[7629,7630,7631,7632],"63 Karto-generated real-world graphs","Karto-generated pose graph of the MIT corridor log","a graph with 800 nodes and 1600 constraints (origin not stated in the paper)","synthetic grid dataset (500 m x 500 m, 100 km trajectory)",[23,38],[40,23],[6563,7636,7637,7638,7639],"SPA (incremental)","SPA (spanning-tree initialization)","dense Cholesky solver","the other approaches (the compared optimizers)",[7624],{"slug":7642,"sourceId":7643,"sourceLabel":7644,"sourceYear":681,"table":69,"note":7645,"datasets":7646,"metrics":7649,"families":7650,"methods":7651,"methodIds":7657,"rows":598,"failures":30},"infinitam2015-table-1","infinitam2015","Kähler et al., 2015","Average computation time per frame over the teddy sequence (Kinect for XBOX 360, 640x480 colour and disparity, no IMU) for three visualisation strateg…",[7647,7648],"authors' couch sequence","authors' teddy sequence",[38],[40],[7652,7653,7654,7655,7656],"InfiniTAM (forward projection)","InfiniTAM (full raycast every frame)","InfiniTAM (no visualisation)","KinectFusion implementation [14]","Voxel hashing implementation [16]",[7643,4750,2916],{"slug":7659,"sourceId":7643,"sourceLabel":7644,"sourceYear":681,"table":17,"note":7660,"datasets":7661,"metrics":7662,"families":7663,"methods":7664,"methodIds":7668,"rows":224,"failures":30},"infinitam2015-table-3","ICL-NUIM tracking accuracy; metric not labelled in the table, values match the ATE RMSE (m) of handa2014iclnuim; 'Handa - best' is the best benchmark-…",[2870],[568],[25],[7665,7666,7667],"Handa - best (best value reported in the ICL-NUIM benchmark paper)","ITM-ARC (approximate raycast)","ITM-Full (full raycast)",[7643],{"slug":7670,"sourceId":7643,"sourceLabel":7644,"sourceYear":681,"table":33,"note":7660,"datasets":7671,"metrics":7672,"families":7673,"methods":7674,"methodIds":7675,"rows":224,"failures":30},"infinitam2015-table-4",[2870],[568],[25],[7665,7666,7667],[7643],{"slug":7677,"sourceId":7643,"sourceLabel":7644,"sourceYear":681,"table":244,"note":7678,"datasets":7679,"metrics":7680,"families":7681,"methods":7682,"methodIds":7685,"rows":2882,"failures":30},"infinitam2015-table-5","Reconstruction error of the living room models against the ICL-NUIM ground-truth surface (m); statistics as labelled in the table",[2870],[75],[78],[7683,7684],"Handa best (best value reported in the ICL-NUIM benchmark paper)","ITM (InfiniTAM)",[7643],{"slug":7687,"sourceId":7643,"sourceLabel":7644,"sourceYear":681,"table":7688,"note":7689,"datasets":7690,"metrics":7692,"families":7693,"methods":7694,"methodIds":7697,"rows":120,"failures":30},"infinitam2015-text-sec-7-4","Text Sec.7.4","Tablet fixed to a swivel chair, one full rotation; rotation error at the end of the rotation per axis",[7691],"authors' swivel-chair test",[23],[23],[7695,7696],"ICP tracker","IMU-based tracker (InfiniTAM)",[7643],{"slug":7699,"sourceId":7700,"sourceLabel":7701,"sourceYear":1157,"table":69,"note":7702,"datasets":7703,"metrics":7708,"families":7709,"methods":7710,"methodIds":7714,"rows":322,"failures":30},"kuemmerle2009measuring-table-1","kuemmerle2009measuring","Kümmerle et al., 2009","Translational error of the relative-relation metric (Eq. 4) averaged over all manually verified relations for three mapping approaches; abs values in…",[7704,7705,7706,6552,7707,4978],"Aces","Freiburg Hospital","Freiburg bldg 79","MIT CSAIL",[330],[332],[7711,7712,7713],"Graph Mapping","RBPF (50 part.)","Scan Matching",[4972],{"slug":7716,"sourceId":7700,"sourceLabel":7701,"sourceYear":1157,"table":108,"note":7717,"datasets":7718,"metrics":7719,"families":7720,"methods":7721,"methodIds":7722,"rows":322,"failures":30},"kuemmerle2009measuring-table-2","Rotational error of the relative-relation metric (Eq. 4) averaged over all manually verified relations for three mapping approaches; abs values in deg…",[7704,7705,7706,6552,7707,4978],[2411],[332],[7711,7712,7713],[4972],{"slug":7724,"sourceId":7725,"sourceLabel":7726,"sourceYear":4540,"table":325,"note":7727,"datasets":7728,"metrics":7738,"families":7739,"methods":7740,"methodIds":7744,"rows":598,"failures":30},"kummerle2011g2o-table-ii","kummerle2011g2o","Kümmerle et al., 2011","Time to solve the linear system within g2o with different linear solvers (CHOLMOD and CSparse sparse Cholesky; PCG with block-Jacobi preconditioner, t…",[7729,7730,6552,7731,7732,7733,7734,7735,7736,7737],"Garage","Grid5000","MIT","Manhattan3500","New College","Scale Drift","Sphere","Venice","Victoria",[23],[23],[7741,7742,7743],"g2o with CHOLMOD","g2o with CSparse","g2o with PCG",[7725],{"slug":7746,"sourceId":7725,"sourceLabel":7726,"sourceYear":4540,"table":279,"note":7747,"datasets":7748,"metrics":7749,"families":7750,"methods":7751,"methodIds":7754,"rows":29,"failures":30},"kummerle2011g2o-table-iii","Average time per iteration of g2o with the direct solution versus the Schur-complement decomposition (build, solve and total) for landmark SLAM and bu…",[7730,7733,7736,7737],[23],[23],[7752,7753],"g2o Schur decomposition","g2o direct solution",[7725],{"slug":7756,"sourceId":7725,"sourceLabel":7726,"sourceYear":4540,"table":7757,"note":7758,"datasets":7759,"metrics":7760,"families":7761,"methods":7762,"methodIds":7765,"rows":63,"failures":30},"kummerle2011g2o-text-sec-v-a","Text Sec.V-A","Time for one iteration on the Garage 3D pose graph; the authors report no accuracy loss with numeric Jacobians",[7729],[23],[23],[7763,7764],"g2o with analytic Jacobians","g2o with numerically evaluated Jacobians",[7725],{"slug":7767,"sourceId":7768,"sourceLabel":7769,"sourceYear":2267,"table":7770,"note":7771,"datasets":7772,"metrics":7774,"families":7775,"methods":7776,"methodIds":7779,"rows":120,"failures":30},"kremen2024hovermap750m-tab-1","kremen2024hovermap750m","Křemen et al., 2024","Tab. 1","RMSE of transforming each Hovermap cloud onto six GCP spheres: congruence (RMSET1) vs similarity (RMSETS) transformation, averaged over five runs per…",[7773],"Josef mine main gallery (own data)",[23],[23],[7777,7778],"Emesent Hovermap ST-X, 1P","Emesent Hovermap ST-X, 2P",[],{"slug":7781,"sourceId":7768,"sourceLabel":7769,"sourceYear":2267,"table":7782,"note":7783,"datasets":7784,"metrics":7785,"families":7786,"methods":7787,"methodIds":7792,"rows":451,"failures":30},"kremen2024hovermap750m-tab-2","Tab. 2","RMSE over 5 runs of ICP-derived systematic shift per profile after GCP-sphere transformation; profiles 0 and 6 hold the GCPs",[7773],[3417,75],[78],[7788,7789,7790,7791],"Emesent Hovermap ST-X, 1P, S=1 (congruence)","Emesent Hovermap ST-X, 1P, S≠1 (similarity)","Emesent Hovermap ST-X, 2P, S=1 (congruence)","Emesent Hovermap ST-X, 2P, S≠1 (similarity)",[],{"slug":7794,"sourceId":7768,"sourceLabel":7769,"sourceYear":2267,"table":7795,"note":7796,"datasets":7797,"metrics":7798,"families":7799,"methods":7800,"methodIds":7801,"rows":154,"failures":30},"kremen2024hovermap750m-text-conclusion","Text Conclusion","Largest transverse deviation observed for single-pass runs (path 4, profile 3 in Tab. A.1 reads 430.1 mm)",[7773],[75],[78],[7777],[],{"slug":7803,"sourceId":7804,"sourceLabel":7805,"sourceYear":562,"table":7782,"note":7806,"datasets":7807,"metrics":7809,"families":7810,"methods":7811,"methodIds":7816,"rows":224,"failures":30},"kremen2025earthworks-tab-2","kremen2025earthworks","Křemen et al., 2025","RMSD of each SLAM cloud to the P40 reference (cloud-to-local-TIN, k=15), two passes per georeferencing mode; 'Mean' is the quadratic mean of both pass…",[7808],"D7 motorway soil deposit near Slaný (own data)",[3417],[78],[7812,7813,7814,7815],"Hovermap ST-X, 8 reflective-target GCPs (GCP)","Hovermap ST-X, GCP, MLS-smoothed (r 0.1 m, order 2)","Hovermap ST-X, GNSS-RTK trajectory (RTK)","Hovermap ST-X, RTK, MLS-smoothed (r 0.1 m, order 2)",[],{"slug":7818,"sourceId":7804,"sourceLabel":7805,"sourceYear":562,"table":7819,"note":7820,"datasets":7821,"metrics":7822,"families":7823,"methods":7824,"methodIds":7833,"rows":1069,"failures":30},"kremen2025earthworks-tab-3","Tab. 3","Axis components (X, Y) of the cloud-to-TIN distances vs P40: RMSD, mean M and std s per cloud",[7808],[3417],[78],[7825,7826,7827,7828,7829,7830,7831,7832],"GCP_1","GCP_1_smooth","GCP_2","GCP_2_smooth","RTK_1","RTK_1_smooth","RTK_2","RTK_2_smooth",[],{"slug":7835,"sourceId":7804,"sourceLabel":7805,"sourceYear":562,"table":181,"note":7836,"datasets":7837,"metrics":7838,"families":7839,"methods":7840,"methodIds":7844,"rows":120,"failures":30},"kremen2025earthworks-text-discussion","Mean vertical deviation of the heap top and of the slopes (sides) evaluated separately against P40",[7808],[3417],[78],[7841,7842,7843],"Hovermap ST-X, GCP_1 and GCP_2","Hovermap ST-X, RTK_1","Hovermap ST-X, RTK_2",[],{"slug":7846,"sourceId":241,"sourceLabel":7847,"sourceYear":16,"table":633,"note":7848,"datasets":7849,"metrics":7851,"families":7852,"methods":7853,"methodIds":7878,"rows":7879,"failures":578},"rtabmap2019-table-8","Labbé & Michaud, 2019","Online results on MIT Stata Center PR2 bags 2012-01-25; ATE computed at every frame on the map graph with loop closures; ATEend = error at end of run,…",[7850],"MIT Stata Center (PR2)",[568,23,38],[25,40,23],[7854,7855,7856,7857,7858,7859,7860,7861,7862,7863,7864,7865,7866,7867,7868,7869,7870,7871,7872,7873,7874,7875,7876,7877],"RTAB-Map with DVO odometry (RGB-D camera)","RTAB-Map with F2F odometry (RGB-D camera)","RTAB-Map with F2F odometry (Stereo camera)","RTAB-Map with F2M odometry (RGB-D camera)","RTAB-Map with F2M odometry (Stereo camera)","RTAB-Map with Fovis odometry (RGB-D camera)","RTAB-Map with Fovis odometry (Stereo camera)","RTAB-Map with ORB2-RTAB odometry (RGB-D camera)","RTAB-Map with ORB2-RTAB odometry (Stereo camera)","RTAB-Map with S2M odometry (Long-range lidar)","RTAB-Map with S2M odometry (Short-range lidar)","RTAB-Map with S2S odometry (Long-range lidar)","RTAB-Map with S2S odometry (Short-range lidar)","RTAB-Map with Viso2 odometry (Stereo camera)","RTAB-Map with WheelIMU odometry (Long-range lidar)","RTAB-Map with WheelIMU odometry (RGB-D camera)","RTAB-Map with WheelIMU odometry (Short-range lidar)","RTAB-Map with WheelIMU odometry (Stereo camera)","RTAB-Map with WheelIMUrefined odometry (Long-range lidar)","RTAB-Map with WheelIMUrefined odometry (Short-range lidar)","RTAB-Map with WheelIMU→S2M odometry (Long-range lidar)","RTAB-Map with WheelIMU→S2M odometry (Short-range lidar)","RTAB-Map with WheelIMU→S2S odometry (Long-range lidar)","RTAB-Map with WheelIMU→S2S odometry (Short-range lidar)",[241],114,{"slug":7881,"sourceId":241,"sourceLabel":7847,"sourceYear":16,"table":644,"note":7882,"datasets":7883,"metrics":7884,"families":7885,"methods":7886,"methodIds":7897,"rows":2262,"failures":30},"rtabmap2019-table-9","MIT Stata Center 2012-01-25 sequences; RTAB-Map WheelIMU→S2M versus other ROS 2D lidar SLAM run with default parameters; Cartographer, GMapping and Ka…",[7850],[568,23],[25,23],[7887,7888,7889,7890,7891,7892,7893,7894,7895,7896],"Cartographer (WheelIMU) [Long-range lidar]","Cartographer (WheelIMU) [Short-range lidar]","GMapping (WheelIMU) [Long-range lidar]","GMapping (WheelIMU) [Short-range lidar]","Hector SLAM (no odometry) [Long-range lidar]","Hector SLAM (no odometry) [Short-range lidar]","Karto SLAM (WheelIMU) [Long-range lidar]","Karto SLAM (WheelIMU) [Short-range lidar]","RTAB-Map (WheelIMU→S2M) [Long-range lidar]","RTAB-Map (WheelIMU→S2M) [Short-range lidar]",[2116,4972,2336,241],{"slug":7899,"sourceId":241,"sourceLabel":7847,"sourceYear":16,"table":3942,"note":7900,"datasets":7901,"metrics":7902,"families":7903,"methods":7904,"methodIds":7907,"rows":63,"failures":30},"rtabmap2019-text-sec-4-4","RAM use on MIT Stata Center; ORB-SLAM2 used as odometry does not free removed features",[7850],[219],[40],[7905,7906],"ORB2-RTAB","other RTAB-Map visual odometry configurations",[241],{"slug":7909,"sourceId":241,"sourceLabel":7847,"sourceYear":16,"table":7910,"note":7911,"datasets":7912,"metrics":7913,"families":7914,"methods":7915,"methodIds":7918,"rows":274,"failures":30},"rtabmap2019-text-sec-5-1","Text Sec. 5.1","Two MIT Stata Center sequences played back to back (two sessions merged), WheelIMU→S2M with short-range lidar, 2 Hz update, working memory limited to…",[7850],[568,23],[25,23],[7916,7917],"RTAB-Map WheelIMU→S2M with memory management","RTAB-Map WheelIMU→S2M without memory management",[241],{"slug":7920,"sourceId":7921,"sourceLabel":7922,"sourceYear":16,"table":4401,"note":7923,"datasets":7924,"metrics":7926,"families":7927,"methods":7928,"methodIds":7930,"rows":154,"failures":154},"laconte2019lidarbias-text-sec-v","laconte2019lidarbias","Laconte et al., 2019","Bench characterization; magnitude of range bias at high incidence angle stated in text (Sec. V: more than 20 cm below 10 m for the LMS151 data of Fig.…",[7925],"own test bench (Laval metrology room)",[23],[23],[7929],"raw LMS151 range measurements without correction",[],{"slug":7932,"sourceId":5320,"sourceLabel":7933,"sourceYear":1234,"table":7934,"note":7935,"datasets":7936,"metrics":7938,"families":7939,"methods":7940,"methodIds":7943,"rows":63,"failures":63},"lague2013m3c2-text-app-a","Lague et al., 2013","Text App.A","ICP registration tried instead of or in addition to targets; errors checked on targets not used in registration",[7937],"Rangitikei river TLS surveys",[23],[23],[7941,7942],"ICP registration on manually selected dense elements","ICP registration on the whole scene",[],{"slug":7945,"sourceId":5320,"sourceLabel":7933,"sourceYear":1234,"table":7946,"note":7947,"datasets":7948,"metrics":7950,"families":7951,"methods":7952,"methodIds":7954,"rows":63,"failures":30},"lague2013m3c2-text-app-b","Text App.B","Per-point difference between bootstrap and parametric LOD95% (Eq. 1, n > 4); identical when averaged over all points",[7949],"Rangitikei river TLS sample surfaces",[23],[23],[7953],"M3C2 bootstrap LOD95% vs parametric LOD95%",[5320],{"slug":7956,"sourceId":5320,"sourceLabel":7933,"sourceYear":1234,"table":7957,"note":7958,"datasets":7959,"metrics":7961,"families":7962,"methods":7963,"methodIds":7966,"rows":654,"failures":30},"lague2013m3c2-text-sec-3-4","Text Sec.3.4","Synthetic horizontal planes, Gaussian noise 1 mm std, 100,000 points, point spacing dx = 1 mm, vertical shifts 1 to 100 mm; M3C2 with D = 50dx and d =…",[7960],"synthetic point clouds",[3417,23],[78,23],[7964,7965,5348],"C2C (closest point)","C2M (cloud-to-mesh)",[4801,5320],{"slug":7968,"sourceId":5320,"sourceLabel":7933,"sourceYear":1234,"table":7969,"note":7970,"datasets":7971,"metrics":7973,"families":7974,"methods":7975,"methodIds":7978,"rows":618,"failures":30},"lague2013m3c2-text-sec-4-3","Text Sec.4.3","Target-based TLS registration (Leica HDS targets, Leica Cyclone 7.2), all stations of 5 epochs registered at once; statistics over 231 target position…",[7972],"Rangitikei river TLS surveys (2009 to 2011)",[23],[23],[7976,7977],"manufacturer specification","target-based registration",[],{"slug":7980,"sourceId":5320,"sourceLabel":7933,"sourceYear":1234,"table":6714,"note":7981,"datasets":7982,"metrics":7984,"families":7985,"methods":7986,"methodIds":7988,"rows":154,"failures":30},"lague2013m3c2-text-sec-5-1","Automatic normal-scale selection (most planar scale over 0.5 to 15 m in 0.5 m steps) on a cliff subset centred on rockfall debris, core points at 10 c…",[7983],"Rangitikei river TLS",[23],[23],[7987],"M3C2 (automatic most-planar normal scale)",[5320],{"slug":7990,"sourceId":5320,"sourceLabel":7933,"sourceYear":1234,"table":7991,"note":7992,"datasets":7993,"metrics":7994,"families":7995,"methods":7996,"methodIds":7997,"rows":52,"failures":30},"lague2013m3c2-text-sec-5-2-3","Text Sec.5.2.3","Parametric mean LOD95% from Eq. 1 at projection scale d = 0.5 m with co-registration error set to zero (Fig. 9 caption); values stated in text",[7949],[3919],[78],[5348],[5320],{"slug":7999,"sourceId":5320,"sourceLabel":7933,"sourceYear":1234,"table":1143,"note":8000,"datasets":8001,"metrics":8002,"families":8003,"methods":8004,"methodIds":8005,"rows":120,"failures":30},"lague2013m3c2-text-sec-5-3","Full-scene comparison of February 2009 and February 2011 surveys: 1.6 M core points (2009 subsampled at 10 cm), fixed normal scale D = 15 m (cliff) an…",[7937],[3919,23],[78,23],[5348],[5320],{"slug":8007,"sourceId":1275,"sourceLabel":8008,"sourceYear":16,"table":325,"note":8009,"datasets":8010,"metrics":8012,"families":8013,"methods":8014,"methodIds":8018,"rows":1315,"failures":30},"landry2019cello3d-table-ii","Landry et al., 2019","Average KL divergence between sampled and predicted ICP covariance per test group; trained on the named other group",[8011],"Challenging data sets (ETH)",[23],[23],[8015,8016,8017],"Baseline (mean training covariance)","Censi","Ours (CELLO-3D)",[1263,1275],{"slug":8020,"sourceId":1275,"sourceLabel":8008,"sourceYear":16,"table":279,"note":8021,"datasets":8022,"metrics":8023,"families":8024,"methods":8025,"methodIds":8027,"rows":1339,"failures":30},"landry2019cello3d-table-iii","ICP odometry over whole sequence, initial guesses sampled with a = 0.05; final pose error and Mahalanobis distance DM against ground truth; DM average…",[8011],[1551,23],[1553,23],[1271,8026],"CELLO-3D (ICP odometry)",[1275],{"slug":8029,"sourceId":2384,"sourceLabel":8030,"sourceYear":374,"table":325,"note":8031,"datasets":8032,"metrics":8034,"families":8035,"methods":8036,"methodIds":8045,"rows":415,"failures":340},"cocolic2023-table-ii","Lang et al., 2023","LiDAR-inertial only (cameras excluded) comparison of uniform B-splines with x control points per 0.1 s (uni-x) against the adaptive non-uniform placem…",[8033],"self-collected mocap sequences",[329],[25],[8037,8038,8039,8040,8041,8042,8043,8044],"non-uni (LIO, adaptive non-uniform B-spline)","uni-1 (LIO, uniform B-spline)","uni-16 (LIO, uniform B-spline)","uni-2 (LIO, uniform B-spline)","uni-3 (LIO, uniform B-spline)","uni-4 (LIO, uniform B-spline)","uni-5 (LIO, uniform B-spline)","uni-8 (LIO, uniform B-spline)",[2384],{"slug":8047,"sourceId":2384,"sourceLabel":8030,"sourceYear":374,"table":279,"note":8048,"datasets":8049,"metrics":8051,"families":8052,"methods":8053,"methodIds":8055,"rows":2119,"failures":120},"cocolic2023-table-iii","Start-to-end drift (translation m \u002F rotation deg) on degenerate Livox Avia sequences without ground truth; rig returns to start; loop closure disabled…",[5829,8050],"R3LIVE dataset",[1551],[1553],[8054,2379,1437,811,2381,1606],"CLIC",[2384,321,815,2387,251],{"slug":8057,"sourceId":2384,"sourceLabel":8030,"sourceYear":374,"table":731,"note":8058,"datasets":8059,"metrics":8060,"families":8061,"methods":8062,"methodIds":8063,"rows":102,"failures":154},"cocolic2023-table-iv","RMSE of APE on UrbanNav (HDL-32E, left camera of stereo pair, Xsens MTi-10, human-driven vehicle); R3LIVE not run because it supports only solid-state…",[6441],[329],[25],[8054,2379,1437,811,2380,1606],[2384,321,815,2386,251],{"slug":8065,"sourceId":2384,"sourceLabel":8030,"sourceYear":374,"table":818,"note":8066,"datasets":8067,"metrics":8068,"families":8069,"methods":8070,"methodIds":8071,"rows":52,"failures":154},"cocolic2023-table-v","Average time per module on UrbanNav Medium (785 s); Coco-LIC visual association includes global LiDAR map update",[6441],[38],[40],[8054,2379,1437],[2384,321],{"slug":8073,"sourceId":2384,"sourceLabel":8030,"sourceYear":374,"table":5268,"note":8074,"datasets":8075,"metrics":8076,"families":8077,"methods":8078,"methodIds":8079,"rows":154,"failures":30},"cocolic2023-text-sec-iv-b2","Total processing time for the whole Medium sequence",[6441],[23],[23],[2379],[2384],{"slug":8081,"sourceId":8082,"sourceLabel":8083,"sourceYear":562,"table":91,"note":8084,"datasets":8085,"metrics":8088,"families":8089,"methods":8090,"methodIds":8096,"rows":2119,"failures":30},"gaussianlic2025-table-i","gaussianlic2025","Lang et al., 2025","Rendering quality; compared methods mapped with ground-truth poses (MCD) or Gaussian-LIC estimated poses (FAST-LIVO, R3LIVE); FAST-LIVO and R3LIVE use…",[811,8086,8087,2381],"FAST-LIVO, R3LIVE and MCD","MCD",[23],[23],[8091,8092,8093,8094,8095],"Gaussian-LIC (novel view)","Gaussian-LIC (train view)","MonoGS (train view)","NeRF-SLAM (train view)","SplaTAM with LiDAR pseudo RGB-D (train view)",[8082,3183,3186],{"slug":8098,"sourceId":8082,"sourceLabel":8083,"sourceYear":562,"table":325,"note":8099,"datasets":8100,"metrics":8101,"families":8102,"methods":8103,"methodIds":8108,"rows":608,"failures":63},"gaussianlic2025-table-ii","Runtime on sequence f0 (105 s of data) with each method's own estimated poses; real time means finishing within the data duration",[811],[23],[23],[8104,8105,8106,5866,8107,3177],"COLMAP + 3DGS (offline)","Gaussian-LIC","Gaussian-LIC w\u002Fo acceleration","NeRF-SLAM",[8082,3183,3186],{"slug":8110,"sourceId":8082,"sourceLabel":8083,"sourceYear":562,"table":279,"note":8111,"datasets":8112,"metrics":8113,"families":8114,"methods":8115,"methodIds":8120,"rows":356,"failures":30},"gaussianlic2025-table-iii","Ablation on sequence f0",[811],[23],[23],[8116,8117,8118,8119],"Gaussian-LIC full","Gaussian-LIC w\u002Fo exposure modelling","Gaussian-LIC w\u002Fo sky modelling","Gaussian-LIC w\u002Fo visual SFM points",[8082],{"slug":8122,"sourceId":8123,"sourceLabel":8124,"sourceYear":107,"table":91,"note":8125,"datasets":8126,"metrics":8127,"families":8128,"methods":8129,"methodIds":8132,"rows":618,"failures":30},"legentil2018lidarimucalib-table-i","legentil2018lidarimucalib","Le Gentil et al., 2018","Simulation with exact known IMU poses and noise-free measurements; mean calibration error over 10 Monte Carlo runs; normal motion 0.2 to 0.53 Hz sines…",[4430],[23],[23],[8130,8131],"(i) with upsampled preintegrated measurements","(ii) without upsampled preintegrated measurements",[8123],{"slug":8134,"sourceId":8123,"sourceLabel":8124,"sourceYear":107,"table":8135,"note":8136,"datasets":8137,"metrics":8138,"families":8139,"methods":8140,"methodIds":8143,"rows":356,"failures":30},"legentil2018lidarimucalib-text-sec-iv-a3","Text Sec. IV-A3","IMU pose sensitivity; IMU poses and velocities fixed; 100 points per plane per scan; realistic range and inertial noise",[4430],[23],[23],[8141,8142],"proposed framework with fixed ground-truth IMU states","proposed framework with fixed perturbed IMU states",[8123],{"slug":8145,"sourceId":8123,"sourceLabel":8124,"sourceYear":107,"table":1928,"note":8146,"datasets":8147,"metrics":8149,"families":8150,"methods":8151,"methodIds":8153,"rows":63,"failures":30},"legentil2018lidarimucalib-text-sec-iv-b","Real 60 s handheld sequence at a room corner (mean 0.7 m\u002Fs and 26 deg\u002Fs); difference between the proposed calibration (150 points per plane per scan)…",[8148],"own handheld room-corner sequence",[23],[23],[8152],"proposed lidar-IMU calibration",[8123],{"slug":8155,"sourceId":8156,"sourceLabel":8157,"sourceYear":213,"table":91,"note":8158,"datasets":8159,"metrics":8161,"families":8162,"methods":8163,"methodIds":8166,"rows":608,"failures":30},"legentil2020gpm-table-i","legentil2020gpm","Le Gentil et al., 2020","Simulated random sinusoidal trajectories, integration interval 1 to 5 s, 100 trials; average relative error with respect to travelled linear or angula…",[8160],"simulated IMU trajectories",[23],[23],[1743,8164,8165],"PM (on-manifold preintegration [14])","UPM (upsampled preintegration [17])",[3756,8123,8156],{"slug":8168,"sourceId":8156,"sourceLabel":8157,"sourceYear":213,"table":325,"note":8169,"datasets":8170,"metrics":8171,"families":8172,"methods":8173,"methodIds":8174,"rows":1069,"failures":30},"legentil2020gpm-table-ii","Simulated fast trajectories, 100 trials, fixed query rates of 1 to 20 Hz; average absolute error of the preintegrated measurement (the paper calls it…",[8160],[23],[23],[1743,8164,8165],[3756,8123,8156],{"slug":8176,"sourceId":8156,"sourceLabel":8157,"sourceYear":213,"table":279,"note":8177,"datasets":8178,"metrics":8179,"families":8180,"methods":8181,"methodIds":8182,"rows":6179,"failures":30},"legentil2020gpm-table-iii","Average computation time over 50 trials for different integration interval lengths; IMU 100 Hz, UPM upsampled to 1 kHz; for UPM and GPM the hyper-para…",[8160],[23],[23],[1743,8164,8165],[3756,8123,8156],{"slug":8184,"sourceId":8156,"sourceLabel":8157,"sourceYear":213,"table":8185,"note":8186,"datasets":8187,"metrics":8189,"families":8190,"methods":8191,"methodIds":8194,"rows":63,"failures":30},"legentil2020gpm-text-sec-v-b-real","Text Sec.V-B real","Real hand-held run (Velodyne VLP-16 and MTi3 Xsens IMU) in the UTS lab, 6.2 m trajectory, maximum estimated speed 1.7 m\u002Fs; about 150k points on a manu…",[8188],"UTS lab hand-held sequence",[1830],[78],[8192,8193],"IN2LAAMA with GPM","IN2LAAMA with PM",[8156],{"slug":8196,"sourceId":8156,"sourceLabel":8157,"sourceYear":213,"table":8197,"note":8198,"datasets":8199,"metrics":8201,"families":8202,"methods":8203,"methodIds":8204,"rows":63,"failures":154},"legentil2020gpm-text-sec-v-b-sim","Text Sec.V-B sim","IN2LAAMA lidar-inertial odometry on simulated data, nine runs, trajectories of 95.6 m on average, loop closure deactivated; failure means final error…",[8200],"IN2LAAMA simulated lidar-inertial data",[1551],[1553],[8192,8193],[8156],{"slug":8206,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":91,"note":8209,"datasets":8210,"metrics":8212,"families":8213,"methods":8214,"methodIds":8218,"rows":322,"failures":30},"in2laama2021-table-i","in2laama2021","Le Gentil et al., 2021","Simulated odometry set-up, 50-run Monte Carlo, loop closure off; trajectories average 288.7 m at 4.85 m\u002Fs (max 7.35 m\u002Fs); errors on successful runs on…",[8211],"IN2LAAMA simulation (virtual room with 7 planes, VLP-16 and MTi-3 models)",[568,2411,330,1551,23],[25,1553,23,332],[8215,8216,8217],"IN2LAAMA","[10] (A-LOAM implementation of LOAM)","[5] IN2LAMA (no IMU factors)",[392,8207],{"slug":8220,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":325,"note":8221,"datasets":8222,"metrics":8224,"families":8225,"methods":8226,"methodIds":8229,"rows":618,"failures":30},"in2laama2021-table-ii","50 simulated closed trajectories (mean 210 m, 3.53 m\u002Fs, 8.16 deg\u002Fs); IN2LAAMA with and without loop closure; mean with plus-minus spread",[8223],"IN2LAAMA simulation",[568,1551,23],[25,1553,23],[8227,8228],"IN2LAAMA (With loop closure)","IN2LAAMA (Without loop closure)",[8207],{"slug":8231,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":279,"note":8232,"datasets":8233,"metrics":8234,"families":8235,"methods":8236,"methodIds":8239,"rows":120,"failures":30},"in2laama2021-table-iii","Robustness to IMU sensitivity mismatch (IMU data multiplied by 1.01, 1.03, 1.05); RMSE position error over 50 runs, number of failures in parentheses;…",[8223],[568],[25],[8237,8238],"IN2LAAMA with Cauchy loss","IN2LAAMA without Cauchy loss",[8207],{"slug":8241,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":731,"note":8242,"datasets":8243,"metrics":8244,"families":8245,"methods":8246,"methodIds":8249,"rows":356,"failures":30},"in2laama2021-table-iv","IN2LAAMA versus a constant-velocity variant; 50 runs, 95.2 m Fast trajectories",[8223],[568,23],[25,23],[8247,8248],"Constant vel. (IN2LAAMA variant with constant angular and linear velocities)","IN2LAAMA (no motion model)",[8207],{"slug":8251,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":818,"note":8252,"datasets":8253,"metrics":8254,"families":8255,"methods":8256,"methodIds":8258,"rows":618,"failures":30},"in2laama2021-table-v","Simulated LiDAR-IMU extrinsic calibration accuracy for four initial-guess error levels; 50 runs of 19.6 s Fast trajectories; mean with plus-minus spre…",[8223],[23],[23],[8257],"IN2LAAMA calibration",[8207],{"slug":8260,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":827,"note":8261,"datasets":8262,"metrics":8264,"families":8265,"methods":8266,"methodIds":8267,"rows":224,"failures":30},"in2laama2021-table-vi","Real data without ground truth: RMS point-to-plane distance between map points on manually segmented planes and the PCA-fitted plane; staircase uses o…",[8263],"IN2LAAMA UTS datasets",[1830],[78],[8215,8216,8217],[392,8207],{"slug":8269,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":838,"note":8270,"datasets":8271,"metrics":8273,"families":8274,"methods":8275,"methodIds":8276,"rows":52,"failures":30},"in2laama2021-table-vii","Memory consumption and execution time of the offline batch optimisation on real data; Ne frames between optimisations; outdoor uses HDL-32 (about four…",[8272],"IN2LAAMA UTS datasets and MC2SLAM dataset",[219,23],[40,23],[8215],[8207],{"slug":8278,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":8279,"note":8280,"datasets":8281,"metrics":8282,"families":8283,"methods":8284,"methodIds":8286,"rows":63,"failures":30},"in2laama2021-text-sec-vii-c","Text Sec.VII-C","Front-end comparison: full IN2LAAMA versus a hybrid using IN2LAAMA back-end with the LOAM front-end; 50-run Monte Carlo, 95.2 m, 4.86 m\u002Fs, 125 deg\u002Fs",[8223],[568],[25],[8285,8215],"Hybrid (IN2LAAMA back-end + LOAM front-end)",[8207],{"slug":8288,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":8289,"note":8290,"datasets":8291,"metrics":8293,"families":8294,"methods":8295,"methodIds":8297,"rows":356,"failures":30},"in2laama2021-text-sec-vii-e-2","Text Sec.VII-E-2","MC2SLAM campus drive (HDL-32 on a car), 409 m in 85.4 s; drift measured at loop closure because no ground truth exists",[8292],"MC2SLAM dataset",[1551,23],[1553,23],[8296,8216],"IN2LAAMA (loop closure disabled for drift)",[392,8207],{"slug":8299,"sourceId":8207,"sourceLabel":8208,"sourceYear":698,"table":8300,"note":8301,"datasets":8302,"metrics":8303,"families":8304,"methods":8305,"methodIds":8308,"rows":63,"failures":30},"in2laama2021-text-sec-vii-f","Text Sec.VII-F","Mapping an aggressive sequence (RMS 0.24 m\u002Fs, 81.3 deg\u002Fs) with extrinsics from two calibration pipelines; average RMS point-to-plane distance over six…",[8263],[1830],[78],[8306,8307],"Chained calibration (Kalibr IMU-camera + camera-lidar via Realsense D435)","IN2LAAMA autocalibration",[8207],{"slug":8310,"sourceId":8311,"sourceLabel":8312,"sourceYear":2267,"table":33,"note":8313,"datasets":8314,"metrics":8316,"families":8317,"methods":8318,"methodIds":8320,"rows":598,"failures":63},"lee2024lidarodom-survey-table-4","lee2024lidarodom_survey","Lee et al., 2024b","Survey benchmark, ATE (m) defined as RMSE (Eq. 1); EVO for ConSLAM and HeLiPR, dataset tool for NTU VIRAL; one sequence per dataset; alignment and har…",[8315,5072,1636],"ConSLAM",[568],[25],[1436,2111,1437,317,571,334,461,2197,4052,8319],"VoxelMap",[1392,2088,304,321,577,395,338,2118,4054,7515],{"slug":8322,"sourceId":8311,"sourceLabel":8312,"sourceYear":2267,"table":910,"note":8323,"datasets":8324,"metrics":8325,"families":8326,"methods":8327,"methodIds":8329,"rows":154,"failures":154},"lee2024lidarodom-survey-text-sec-7-1","Two multi-LiDAR odometry studies on NTU VIRAL (two 16-channel LiDARs), as reported by the survey",[1636],[38],[40],[8328],"multi-LiDAR odometry of [98] and [100]",[],{"slug":8331,"sourceId":8332,"sourceLabel":8333,"sourceYear":562,"table":91,"note":8334,"datasets":8335,"metrics":8336,"families":8337,"methods":8338,"methodIds":8344,"rows":224,"failures":154},"genzicp2025-table-i","genzicp2025","Lee et al., 2025a","Newer College; relative translational error in % (KITTI metric); baseline values taken from their papers when available, otherwise tuned by the author…",[3253],[438],[441],[8339,8340,1370,8341,8342,8343],"CT-ICP [6] (SLAM)","F-LOAM [36]","MAD-ICP [8]","MULLS [35] (SLAM)","Ours (GenZ-ICP)",[596,393,8332,577,4148,597],{"slug":8346,"sourceId":8332,"sourceLabel":8333,"sourceYear":562,"table":325,"note":8347,"datasets":8348,"metrics":8349,"families":8350,"methods":8351,"methodIds":8354,"rows":608,"failures":154},"genzicp2025-table-ii","MulRan urban driving; relative translational error in % (KITTI metric)",[671],[438],[441],[8340,1370,8341,8352,8343,8353],"MULLS [35]","SuMa [37]",[393,8332,577,4148,597,432],{"slug":8356,"sourceId":8332,"sourceLabel":8333,"sourceYear":562,"table":279,"note":8357,"datasets":8358,"metrics":8359,"families":8360,"methods":8361,"methodIds":8366,"rows":224,"failures":30},"genzicp2025-table-iii","KITTI odometry training sequences 00-10; relative translational error in %",[469],[438],[441],[8339,8340,8362,8363,1370,8341,8352,8342,8343,8353,8364,8365],"Generalized-ICP [9]","IMLS-SLAM [22]","SuMa++ [37] (SLAM)","VGICP [13]",[596,393,8332,577,7447,4148,597,3292,432,1968],{"slug":8368,"sourceId":8332,"sourceLabel":8333,"sourceYear":562,"table":731,"note":8369,"datasets":8370,"metrics":8372,"families":8373,"methods":8374,"methodIds":8378,"rows":120,"failures":30},"genzicp2025-table-iv","HILTI-Oxford Exp07 Long Corridor; HILTI SLAM Challenge score from APE at millimetre-level reference points (counts per error band omitted)",[8371],"HILTI-Oxford 2022",[23],[23],[8375,7535,1370,8343,8376,8377],"CT-ICP [6]","X-ICP [24]","Zhang et al. [18]",[596,2088,8332,577,8379,5431],"tuna2024xicp",{"slug":8381,"sourceId":8332,"sourceLabel":8333,"sourceYear":562,"table":818,"note":8382,"datasets":8383,"metrics":8385,"families":8386,"methods":8387,"methodIds":8388,"rows":608,"failures":30},"genzicp2025-table-v","Ground-Challenge corridors; translation APE and RPE via EVO; only RMSE columns kept (mean, max, std omitted); Zhang et al. and X-ICP reimplemented by…",[8384],"Ground-Challenge",[568,330],[25,332],[8375,7535,1370,8343,8376,8377],[596,2088,8332,577,8379,5431],{"slug":8390,"sourceId":8332,"sourceLabel":8333,"sourceYear":562,"table":827,"note":8391,"datasets":8392,"metrics":8394,"families":8395,"methods":8396,"methodIds":8399,"rows":29,"failures":63},"genzicp2025-table-vi","SubT-MRS Long_Corridor (ICCV 2023 SLAM Challenge); translation APE and RPE via EVO; only RMSE columns kept; point-to-point and point-to-plane ICP run…",[8393],"SubT-MRS",[568,330],[25,332],[8375,7535,1370,8343,8397,8398,8376,8377],"Point-to-plane ICP [4]","Point-to-point ICP [3]",[478,596,2088,8332,577,8379,5431],{"slug":8401,"sourceId":8402,"sourceLabel":8403,"sourceYear":562,"table":33,"note":8404,"datasets":8405,"metrics":8407,"families":8408,"methods":8409,"methodIds":8416,"rows":102,"failures":30},"mins2025-table-4","mins2025","Lee et al., 2025b","Simulation: pose RMSE (deg \u002F m) and total computation time (s) of fixed-rate 30 Hz cloning versus dynamic cloning with threshold coefficients 0.01 to…",[8406],"MINS simulation",[568,23],[25,23],[8410,8411,8412,8413,8414,8415],"MINS (dynamic, coefficient 0.01 cloning)","MINS (dynamic, coefficient 0.1 cloning)","MINS (dynamic, coefficient 1 cloning)","MINS (dynamic, coefficient 10 cloning)","MINS (dynamic, coefficient 100 cloning)","MINS (fixed 30 Hz cloning)",[8402],{"slug":8418,"sourceId":8402,"sourceLabel":8403,"sourceYear":562,"table":244,"note":8419,"datasets":8420,"metrics":8421,"families":8422,"methods":8423,"methodIds":8432,"rows":608,"failures":30},"mins2025-table-5","Simulation with different sensor combinations (I IMU, C camera, G GNSS, W wheel, L LiDAR); orientation and position RMSE (mean over 10 runs, +- std in…",[8406],[568,23],[25,23],[8424,8425,8426,8427,8428,8429,8430,8431],"MINS(I,C)","MINS(I,C,G)","MINS(I,C,G,W,L)","MINS(I,C,L)","MINS(I,C,W)","MINS(I,G)","MINS(I,L)","MINS(I,W)",[8402],{"slug":8434,"sourceId":8402,"sourceLabel":8403,"sourceYear":562,"table":621,"note":8435,"datasets":8436,"metrics":8438,"families":8439,"methods":8440,"methodIds":8449,"rows":1991,"failures":800},"mins2025-table-7","UD Husky dataset (Clearpath Husky; indoor structured I1-I2 with OptiTrack GT, outdoor O1-O2 and unstructured T1-T4 with Emlid RTK GT); average (5 runs…",[8437],"UD Husky dataset",[22],[25],[1437,8441,8442,8443,8424,8444,8428,8430,8445,892,8446,8447,8448],"GNSS","LIW-OAM","Lvio-Fusion","MINS(I,C,L,W,G)","MINS(I,L,W)","VINS-Fusion(G)","VINS-Fusion(L)","VINS-Fusion(V)",[321,8402,763,1513],{"slug":8451,"sourceId":8402,"sourceLabel":8403,"sourceYear":562,"table":8452,"note":8453,"datasets":8454,"metrics":8456,"families":8457,"methods":8458,"methodIds":8459,"rows":224,"failures":30},"mins2025-table-8-total-column","Table 8 (Total column)","KAIST Urban 38 timing (ms), average time per function call; total of frontend (I, C, G, W, L) and backend (map, Opt.) times; ThinkPad P17 with Intel i…",[8455],"KAIST Urban",[38],[40],[1437,8442,8443,8424,8444,8428,8430,8445,892,8446,8447,8448],[321,8402,763,1513],{"slug":8461,"sourceId":8462,"sourceLabel":8463,"sourceYear":2267,"table":108,"note":8464,"datasets":8465,"metrics":8467,"families":8468,"methods":8469,"methodIds":8481,"rows":8483,"failures":30},"mast3r2024-table-2","mast3r2024","Leroy et al., 2024","Map-free relocalization test set (VoR table, which adds FAR, RoMa and Mickey compared with arXiv v1). VCRE = virtual correspondence reprojection error…",[8466],"Map-free relocalization",[23,1933],[23,1935],[8470,8471,8472,8473,8474,8475,8476,8477,8478,8479,8480],"DUSt3R [106] (DPT depth)","FAR [75] (auto)","LoFTR [87] (KBR depth)","MASt3R (DPT depth)","MASt3R (auto, own metric depth)","MASt3R (direct reg., PnP on pointmap)","Mickey [8] (auto)","RPR [5] (DPT depth)","RoMa [29] (DPT depth)","SIFT [54] (DPT depth)","SP+SG [78] (DPT depth)",[8482,8462],"dust3r2024",77,{"slug":8485,"sourceId":8462,"sourceLabel":8463,"sourceYear":2267,"table":8486,"note":8487,"datasets":8488,"metrics":8489,"families":8490,"methods":8491,"methodIds":8507,"rows":3547,"failures":30},"mast3r2024-table-3-right","Table 3 right","DTU dense MVS (mm): accuracy, completeness and overall Chamfer (average of the two) with the benchmark's evaluation code; MASt3R and DUSt3R zero-shot;…",[6088],[2343,75,76],[78],[8492,8493,8494,8495,8496,8497,8498,8499,8500,8501,8502,8503,8504,8505,8506],"CER-MVS [57] (d)","CIDER [111] (d)","CVP-MVSNet [113] (d)","Camp [14] (c)","CasMVSNet [36] (d)","DUSt3R [106] (e)","Furu [32] (c)","GeoMVSNet [122] (d)","Gipuma [33] (c)","MASt3R (e)","MVSNet [114] (d)","PatchmatchNet [103] (d)","Tola [95] (c)","TransMVSNet [22] (d)","UCS-Net [18] (d)",[8482,8462],{"slug":8509,"sourceId":8462,"sourceLabel":8463,"sourceYear":2267,"table":8510,"note":8511,"datasets":8512,"metrics":8513,"families":8514,"methods":8515,"methodIds":8517,"rows":274,"failures":30},"mast3r2024-table-5-right-arxiv-v1-app-c","Table 5 right (arXiv v1 App. C)","DTU MVS (mm) with coarse-only matching (images downscaled to 384 x 512) versus coarse-to-fine; from the arXiv v1 appendix (supplementary material in t…",[6088],[2343,75,76],[78],[8516],"MASt3R Coarse (coarse-only matching)",[8462],{"slug":8519,"sourceId":1510,"sourceLabel":8520,"sourceYear":681,"table":8521,"note":8522,"datasets":8523,"metrics":8525,"families":8526,"methods":8527,"methodIds":8531,"rows":274,"failures":274},"okvis2015-text-sec-vii-b1","Leutenegger et al., 2015","Text Sec.VII-B1","Vicon Loops (hand-held, 1200 m, Vicon 6D ground truth); error statistics accumulated over distance travelled from many aligned start poses (KITTI-styl…",[8524],"Vicon Loops (authors' dataset)",[23],[23],[8528,8529,8530],"aslam (stereo, proposed)","aslam-mono (proposed)","msckf-mono (MSCKF reference implementation)",[1509,1510],{"slug":8533,"sourceId":8534,"sourceLabel":8535,"sourceYear":1234,"table":108,"note":8536,"datasets":8537,"metrics":8539,"families":8540,"methods":8541,"methodIds":8550,"rows":322,"failures":274},"msckf2-2013-table-2","msckf2_2013","Li & Mourikis, 2013","Monte Carlo simulation (50 trials) generated from a real 13 min, 5.5 km urban vehicle dataset (ISIS IMU, monocular camera); values averaged over trial…",[8538],"simulation from 5.5 km urban vehicle dataset",[568,23],[25,23],[8542,8543,8544,4734,8545,8546,8547,8548,8549],"'Ideal' MSCKF (true states in Jacobians, simulation only)","AHP (EKF-SLAM, anchored homogeneous)","IDP (EKF-SLAM, inverse depth)","MSCKF 2.0","XYZ (EKF-SLAM, XYZ features)","m-AHP (first-estimates EKF-SLAM)","m-IDP (first-estimates EKF-SLAM)","m-XYZ (first-estimates EKF-SLAM)",[1509],{"slug":8552,"sourceId":8534,"sourceLabel":8535,"sourceYear":1234,"table":17,"note":8553,"datasets":8554,"metrics":8556,"families":8557,"methods":8558,"methodIds":8561,"rows":224,"failures":30},"msckf2-2013-table-3","Monte Carlo simulation (50 trials) generated from the Cheddar Gorge dataset (29 km, 56 min driving, Xsens IMU at 100 Hz, images at 20 Hz); all methods…",[8555],"simulation from Cheddar Gorge dataset",[568,23],[25,23],[8559,8560,4734,8545],"'Ideal' MSCKF","FLS (information-form fixed-lag smoother based on Sibley et al. 2010)",[1509,8562],"sibley2010swf",{"slug":8564,"sourceId":8534,"sourceLabel":8535,"sourceYear":1234,"table":33,"note":8565,"datasets":8566,"metrics":8567,"families":8568,"methods":8569,"methodIds":8574,"rows":721,"failures":356},"msckf2-2013-table-4","Cheddar Gorge Monte Carlo with camera-IMU extrinsics perturbed (sigma 0.01 m and 0.5 deg per axis); x and y parallel to ground, z along gravity; preci…",[8555],[23],[23],[8570,8571,8572,8573],"MSCKF 2.0 (calibration off, nominal extrinsics)","MSCKF 2.0 (calibration on)","MSCKF 2.0 (precise calibration)","m-AHP with online calibration",[],{"slug":8576,"sourceId":8534,"sourceLabel":8535,"sourceYear":1234,"table":7957,"note":8577,"datasets":8578,"metrics":8579,"families":8580,"methods":8581,"methodIds":8585,"rows":356,"failures":30},"msckf2-2013-text-sec-3-4","Estimator runtime per update in the 5.5 km-based simulation, kappa = 2 for EKF-SLAM",[8538],[38],[40],[8582,8583,8584,4734],"EKF-SLAM AHP","EKF-SLAM IDP","EKF-SLAM XYZ",[1509],{"slug":8587,"sourceId":8534,"sourceLabel":8535,"sourceYear":1234,"table":8588,"note":8589,"datasets":8590,"metrics":8592,"families":8593,"methods":8594,"methodIds":8596,"rows":654,"failures":30},"msckf2-2013-text-sec-9","Text Sec.9","Real vehicle run in Riverside, CA: 37 min, about 21.5 km, Xsens MTi-G at 100 Hz, one camera of a Bumblebee2 at 20 Hz, GPS-INS ground truth",[8591],"own vehicle dataset (Riverside, CA)",[23,38],[40,23],[8595,4734,8545],"FLS",[1509,8562],{"slug":8598,"sourceId":8599,"sourceLabel":8600,"sourceYear":921,"table":69,"note":8601,"datasets":8602,"metrics":8603,"families":8604,"methods":8605,"methodIds":8607,"rows":120,"failures":30},"li2014onlinetemporal-table-1","li2014onlinetemporal","Li & Mourikis, 2014","Map-based EKF simulation, 50 Monte Carlo trials, sinusoidal trajectory, 6 known landmarks per image (5 to 20 m), IMU 100 Hz, images 10 Hz, td drawn fr…",[4430],[568,23],[25,23],[8606],"proposed map-based EKF with online td",[8599],{"slug":8609,"sourceId":8599,"sourceLabel":8600,"sourceYear":921,"table":108,"note":8610,"datasets":8611,"metrics":8612,"families":8613,"methods":8614,"methodIds":8616,"rows":654,"failures":30},"li2014onlinetemporal-table-2","EKF-SLAM simulation in a 7 x 12 x 5 m room for 90 s at 0.37 m\u002Fs average, 50 persistent and 100 temporary features per image; RMSE averaged over Monte…",[4430],[568,23],[25,23],[8615],"proposed EKF-SLAM with online td and T_IC",[8599],{"slug":8618,"sourceId":8599,"sourceLabel":8600,"sourceYear":921,"table":17,"note":8619,"datasets":8620,"metrics":8622,"families":8623,"methods":8624,"methodIds":8629,"rows":2262,"failures":120},"li2014onlinetemporal-table-3","MSCKF VIO simulation from a real 13 min, 5.5 km ground-truth trajectory; 50 Monte Carlo trials; average RMSE and NEES; imprecise cases start from nomi…",[8621],"simulation (from real trajectory)",[23],[23],[8625,8626,8627,8628],"imprecise: T_IC estimation off, td estimation on","imprecise: T_IC estimation on, td estimation off","precise: T_IC and td perfectly known","proposed: T_IC and td estimation on",[8599],{"slug":8631,"sourceId":8599,"sourceLabel":8600,"sourceYear":921,"table":8632,"note":8633,"datasets":8634,"metrics":8636,"families":8637,"methods":8638,"methodIds":8640,"rows":63,"failures":30},"li2014onlinetemporal-text-sec-7-1-1","Text Sec. 7.1.1","Real indoor map-based localization with 20 LEDs, two loops returning to the known start; errors only at three known time instants",[8635],"own indoor lab dataset",[1551,23],[1553,23],[8639],"map-based EKF with online td",[8599],{"slug":8642,"sourceId":8599,"sourceLabel":8600,"sourceYear":921,"table":8643,"note":8644,"datasets":8645,"metrics":8646,"families":8647,"methods":8648,"methodIds":8650,"rows":274,"failures":30},"li2014onlinetemporal-text-sec-7-1-2","Text Sec. 7.1.2","Real indoor EKF-SLAM on the same dataset, LEDs plus Shi-Tomasi features",[8635],[1551,75,23],[1553,78,23],[8649],"EKF-SLAM with online td and T_IC",[8599],{"slug":8652,"sourceId":8599,"sourceLabel":8600,"sourceYear":921,"table":8653,"note":8654,"datasets":8655,"metrics":8657,"families":8658,"methods":8659,"methodIds":8661,"rows":154,"failures":154},"li2014onlinetemporal-text-sec-7-1-3","Text Sec. 7.1.3","Real outdoor driving, camera-IMU on car roof, about 7.3 km in 11 min; GPS-INS ground truth; error stated as below a bound",[8656],"own Riverside driving dataset",[23],[23],[8660],"MSCKF 2.0 with online td and T_IC",[8599],{"slug":8663,"sourceId":8599,"sourceLabel":8600,"sourceYear":921,"table":8664,"note":8665,"datasets":8666,"metrics":8667,"families":8668,"methods":8669,"methodIds":8670,"rows":274,"failures":30},"li2014onlinetemporal-text-sec-7-2-1","Text Sec. 7.2.1","Map-based simulation consistency; average NEES over trials and time steps; expected values 15, 6 and 1",[4430],[23],[23],[8606],[8599],{"slug":8672,"sourceId":8599,"sourceLabel":8600,"sourceYear":921,"table":8673,"note":8674,"datasets":8675,"metrics":8676,"families":8677,"methods":8678,"methodIds":8679,"rows":356,"failures":30},"li2014onlinetemporal-text-sec-7-2-2","Text Sec. 7.2.2","EKF-SLAM simulation consistency; expected values 15, 6, 1 and 3",[4430],[23],[23],[8615],[8599],{"slug":8681,"sourceId":3309,"sourceLabel":8682,"sourceYear":16,"table":69,"note":8683,"datasets":8684,"metrics":8686,"families":8687,"methods":8688,"methodIds":8698,"rows":8699,"failures":578},"lonet2019-table-1","Li et al., 2019","KITTI odometry metric: t_rel = average translational RMSE (%) and r_rel = average rotational RMSE (deg\u002F100 m) over 100-800 m lengths. LO-Net trained o…",[8685,469],"Ford Campus Vision and Lidar",[438,439],[441],[8689,8690,8691,8692,8693,8694,8695,8696,8697],"CLS [34]","GICP [30]","ICP-po2pl (PCL)","ICP-po2po (PCL)","LO-Net","LO-Net+Mapping","LOAM [45] (authors' modified re-run)","LOAM [45] (bracketed values quoted from the LOAM paper)","Velas et al. [35] (values from [35])",[478,1944,450,3309,3292],134,{"slug":8701,"sourceId":3309,"sourceLabel":8682,"sourceYear":16,"table":33,"note":8702,"datasets":8703,"metrics":8704,"families":8705,"methods":8706,"methodIds":8707,"rows":356,"failures":30},"lonet2019-table-4","Average runtime per scan on KITTI sequence 00, batch size 1 at test time; lidar rotates at 10 Hz so real time means under 0.1 s per scan.",[469],[38],[40],[8694],[3309],{"slug":8709,"sourceId":8710,"sourceLabel":8711,"sourceYear":698,"table":325,"note":8712,"datasets":8713,"metrics":8714,"families":8715,"methods":8716,"methodIds":8723,"rows":339,"failures":30},"saloam2021-table-ii","saloam2021","Li et al., 2021a","KITTI odometry 00-10; mean relative pose error over 100-800 m trajectories (rotation deg\u002F100m \u002F translation %); * marks sequences with loops; LOAM val…",[469],[438,439],[441],[8717,4050,8718,8719,8720,8721,8722],"FLOAM","LOAM* (from [19])","Ours-LOOP","Ours-ODOM","SUMA","SUMA++",[393,8724,450,8710,432,1968],"iscloam2020",{"slug":8726,"sourceId":8710,"sourceLabel":8711,"sourceYear":698,"table":279,"note":8727,"datasets":8728,"metrics":8729,"families":8730,"methods":8731,"methodIds":8732,"rows":2262,"failures":30},"saloam2021-table-iii","KITTI sequences with loops; absolute trajectory error (m); statistic and alignment not stated",[469],[329],[25],[4050,8719,8720,8721,8722],[8724,8710,432,1968],{"slug":8734,"sourceId":8710,"sourceLabel":8711,"sourceYear":698,"table":731,"note":8735,"datasets":8736,"metrics":8738,"families":8739,"methods":8740,"methodIds":8741,"rows":102,"failures":30},"saloam2021-table-iv","Ford Campus Vision and Lidar Dataset, models and parameters tuned on KITTI only; absolute trajectory error (m); statistic and alignment not stated",[8737],"Ford Campus Vision and Lidar Dataset",[329],[25],[8717,4050,8719,8720,8721,8722],[393,8724,8710,432,1968],{"slug":8743,"sourceId":337,"sourceLabel":8744,"sourceYear":698,"table":69,"note":8745,"datasets":8746,"metrics":8749,"families":8750,"methods":8751,"methodIds":8754,"rows":721,"failures":356},"liliom2021-table-1","Li et al., 2021b","APE RMSE of the final trajectory vs ground truth computed with evo; LOAM column is A-LOAM (footnote 6); LiLi-OM* is the spinning-LiDAR variant; LIO-SA…",[8747,8748,6441],"UTBM (EU long-term)","UrbanLoco",[568],[25],[4769,334,4770,461,8752,8753],"LeGO","LiLi-OM*",[392,395,337,4783,2117,338],{"slug":8756,"sourceId":337,"sourceLabel":8744,"sourceYear":698,"table":108,"note":8757,"datasets":8758,"metrics":8760,"families":8761,"methods":8762,"methodIds":8768,"rows":641,"failures":274},"liliom2021-table-2","End-to-end position error on the FR-IOSB campus data; HDL-64E + MTi-G-700 vs Livox Horizon + MTi-670 on the same platform; LOAM implementation not re-…",[8759],"FR-IOSB (own)",[1551],[1553],[8763,8764,8765,8766,8767],"LOAM (on Velodyne HDL-64E)","LeGO (on Velodyne HDL-64E)","LiHo, Livox-Horizon-LOAM (on Livox Horizon)","LiLi-OM (on Livox Horizon)","LiLi-OM* (on Velodyne HDL-64E)",[395,337],{"slug":8770,"sourceId":337,"sourceLabel":8744,"sourceYear":698,"table":17,"note":8771,"datasets":8772,"metrics":8774,"families":8775,"methods":8776,"methodIds":8781,"rows":1315,"failures":30},"liliom2021-table-3","End-to-end position error on KA-Urban backpack sequences, end points registered from satellite images; LiLi-OM-O has loop closure disabled; 'IMU remov…",[8773],"KA-Urban (own)",[1551],[1553],[8777,335,8778,8779,8780],"LiHo, Livox-Horizon-LOAM","LiLi-OM, IMU removed","LiLi-OM-O (no loop closure)","LiLi-OM-O, IMU removed",[337],{"slug":8783,"sourceId":337,"sourceLabel":8744,"sourceYear":698,"table":33,"note":8784,"datasets":8785,"metrics":8786,"families":8787,"methods":8788,"methodIds":8789,"rows":102,"failures":30},"liliom2021-table-4","Average runtime per frame of the three parallel ROS nodes; Velodyne HDL columns use the spinning-LiDAR preprocessing (LiLi-OM*), Livox columns the pro…",[8759,8773,8747,8748],[38],[40],[335],[337],{"slug":8791,"sourceId":8792,"sourceLabel":8793,"sourceYear":68,"table":69,"note":8794,"datasets":8795,"metrics":8797,"families":8798,"methods":8799,"methodIds":8805,"rows":1339,"failures":30},"li2026tunneldt-table-1","li2026tunneldt","Li et al., 2026b","Semantic segmentation IoU on the test split (scenes sliced every 5 m, 8:1:1 split); six classes; trained on AMD EPYC 7763 + NVIDIA A100; values in per…",[8796],"authors' tunnel dataset (~300 m), after centerline sampling (21.5 M points)",[23],[23],[8800,8801,8802,8803,8804],"FL-PointNet++","KPConv","PointNet","PointNet++","RandLA-Net",[8792],{"slug":8807,"sourceId":8792,"sourceLabel":8793,"sourceYear":68,"table":8808,"note":8809,"datasets":8810,"metrics":8812,"families":8813,"methods":8814,"methodIds":8817,"rows":578,"failures":274},"li2026tunneldt-text-sec-3-1","Text Sec.3.1","Handheld scan of a ~300 m construction-phase rock tunnel with weak texture and poor illumination; no ground-truth trajectory available, so stability i…",[8811],"authors' tunnel dataset (~300 m)",[23],[23],[8815,8816],"FAST-LIVO (baseline)","enhanced FAST-LIO (proposed)",[815,8792],{"slug":8819,"sourceId":8792,"sourceLabel":8793,"sourceYear":68,"table":3087,"note":8820,"datasets":8821,"metrics":8822,"families":8823,"methods":8824,"methodIds":8833,"rows":578,"failures":654},"li2026tunneldt-text-sec-3-2","Centerline sampling on the full tunnel cloud; laptop Intel i7-13700H + RTX 3050; baselines global PCA, iterative local PCA, UMS, MLS",[8811],[23],[23],[8825,8826,8827,8828,8829,8830,8831,8832],"Moving Least Squares (MLS) centerline","Uniform Mean Shift (UMS) centerline","global PCA with weighted-centroid update","global farthest point sampling (FPS)","iterative segment-wise local PCA","proposed centerline sampling","proposed global hull + ring-band RANSAC","radius filtering",[8792],{"slug":8835,"sourceId":8792,"sourceLabel":8793,"sourceYear":68,"table":4020,"note":8836,"datasets":8837,"metrics":8838,"families":8839,"methods":8840,"methodIds":8841,"rows":63,"failures":30},"li2026tunneldt-text-sec-3-3","FL-PointNet++ training convergence",[8811],[23],[23],[8800],[8792],{"slug":8843,"sourceId":8792,"sourceLabel":8793,"sourceYear":68,"table":7957,"note":8844,"datasets":8845,"metrics":8846,"families":8847,"methods":8848,"methodIds":8850,"rows":52,"failures":30},"li2026tunneldt-text-sec-3-4","Stable secondary-lining segments, 10 m slices along the refined centerline, ring band 0.02 m; reference is the 5.500 m design radius, not an independe…",[8811],[1819,23],[23,1821],[8849],"enhanced FAST-LIO reconstruction + normal-constrained centerline refinement",[8792],{"slug":8852,"sourceId":8853,"sourceLabel":8854,"sourceYear":1694,"table":69,"note":8855,"datasets":8856,"metrics":8858,"families":8859,"methods":8860,"methodIds":8863,"rows":2119,"failures":30},"lichti2007amcw-table-1","lichti2007amcw","Lichti, 2007","Self-calibration residual RMS per dataset without and with the 17-AP model",[8857],"Faro 880 self-calibration datasets",[23],[23],[8861,8862],"With APs","Without APs",[8853],{"slug":8865,"sourceId":8853,"sourceLabel":8854,"sourceYear":1694,"table":108,"note":8866,"datasets":8867,"metrics":8869,"families":8870,"methods":8871,"methodIds":8874,"rows":224,"failures":30},"lichti2007amcw-table-2","Independent accuracy check on calibration 9 day: 120 observations of 45 total-station check points from 3 scans; rigid-body transformation with survey…",[8868],"calibration 9 check points",[75,23],[78,23],[8872,8873],"With AP correction","Without AP correction",[8853],{"slug":8876,"sourceId":8853,"sourceLabel":8854,"sourceYear":1694,"table":254,"note":8877,"datasets":8878,"metrics":8880,"families":8881,"methods":8882,"methodIds":8883,"rows":120,"failures":30},"lichti2007amcw-text-sec-4-1","Pooled residual RMS over all 10 self-calibration datasets",[8879],"Faro 880 self-calibration datasets (all 10)",[23],[23],[8861,8862],[8853],{"slug":8885,"sourceId":8853,"sourceLabel":8854,"sourceYear":1694,"table":8886,"note":8887,"datasets":8888,"metrics":8891,"families":8892,"methods":8893,"methodIds":8895,"rows":63,"failures":30},"lichti2007amcw-text-sec-4-5","Text Sec. 4.5","RMS of the 16 inclinometer orientation-angle residuals (8 omega, 8 phi)",[8889,8890],"calibration 10 (19 Jan 2006)","calibration 9 (7 Dec 2005)",[23],[23],[8894],"Self-calibration with inclinometer observations",[8853],{"slug":8897,"sourceId":3526,"sourceLabel":8898,"sourceYear":698,"table":325,"note":8899,"datasets":8900,"metrics":8902,"families":8903,"methods":8904,"methodIds":8911,"rows":339,"failures":30},"erasor2021-table-ii","Lim et al., 2021","Static-map benchmark on five manually selected SemanticKITTI frame ranges with SuMa poses; PR and RR computed voxel-wise with 0.2 voxel size for all m…",[8901],"SemanticKITTI",[23],[23],[8905,8906,8907,8908,8909,8910],"ERASOR (Ours)","OctoMap - 0.05","OctoMap - 0.2","Peopleremover","Removert - RM3","Removert - RM3+RV1",[3526,3527,3528,8912],"schauer2018peopleremover",{"slug":8914,"sourceId":3526,"sourceLabel":8898,"sourceYear":698,"table":279,"note":8915,"datasets":8916,"metrics":8917,"families":8918,"methods":8919,"methodIds":8921,"rows":356,"failures":154},"erasor2021-table-iii","Runtime per iteration of each dynamic-removal method on SemanticKITTI sequence 01; hardware not reported",[8901],[38],[40],[8905,5916,8908,8920],"Removert",[3526,3527,3528,8912],{"slug":8923,"sourceId":8924,"sourceLabel":8925,"sourceYear":374,"table":325,"note":8926,"datasets":8927,"metrics":8929,"families":8930,"methods":8931,"methodIds":8934,"rows":301,"failures":356},"adalio2023-table-ii","adalio2023","Lim et al., 2023","HILTI-Oxford validation sequences with millimetre-level marker poses; each marker scored 10, 6 or 3 if the closest estimated pose is within 1, 10 or 1…",[8928],"HILTI-Oxford dataset (HILTI SLAM Challenge 2022 validation set)",[23],[23],[8932,8933],"AdaLIO (Ours)","Faster-LIO [17]",[8924,304],{"slug":8936,"sourceId":8937,"sourceLabel":8938,"sourceYear":2267,"table":1072,"note":8939,"datasets":8940,"metrics":8941,"families":8942,"methods":8943,"methodIds":8956,"rows":1991,"failures":29},"lim2024quatropp-table-6","lim2024quatropp","Lim et al., 2024","KITTI Seq. 00 odometry test with frame interval Delta (source i+Delta, target i); trel [%] and rrel [deg\u002F100m] by RPG evaluation tools; c2f = global r…",[1997],[438,439],[441],[4330,8944,8945,8946,8947,8948,522,8693,8949,8950,8951,8952,8953,1976,8954,8955],"A-LOAM + StickyPillars†","DMLO+M†","DMLO†","FGR","G-ICP","LO-Net+M","Quatro (Ours)","Quatro++ (Ours)","Quatro++-c2f (Ours)","Quatro-c2f (Ours)","TEASER++","VGICP",[392,478,7447,8937,3309,3292,432,1789,8957],"zhou2016fgr",{"slug":8959,"sourceId":8937,"sourceLabel":8938,"sourceYear":2267,"table":621,"note":8960,"datasets":8961,"metrics":8962,"families":8963,"methods":8964,"methodIds":8967,"rows":1069,"failures":30},"lim2024quatropp-table-7","absolute pose errors of full SLAM results on MulRan (Ouster OS1-64, vehicle); LeGO-LOAM variants: SC = ScanContext loop detection, TSC = TEASER++ + Sc…",[671],[329],[25],[2197,8965,4780,8966],"QSC-LeGO-LOAM (Ours)","TSC-LeGO-LOAM",[395,8937],{"slug":8969,"sourceId":8937,"sourceLabel":8938,"sourceYear":2267,"table":8970,"note":8971,"datasets":8972,"metrics":8974,"families":8975,"methods":8976,"methodIds":8978,"rows":63,"failures":30},"lim2024quatropp-text-fig-17-caption","Text Fig. 17 caption","average optimization time of the Quatro solver used in Quatro++ (values stated in the Fig. 17 caption; other methods' times are plots only)",[1997,8973],"NAVER LABS localization dataset",[38],[40],[8977],"Quatro (optimization only)",[],{"slug":8980,"sourceId":8937,"sourceLabel":8938,"sourceYear":2267,"table":8981,"note":8982,"datasets":8983,"metrics":8985,"families":8986,"methods":8987,"methodIds":8989,"rows":154,"failures":154},"lim2024quatropp-text-sec-7-4","Text Sec. 7.4","total time of Quatro++ (preprocessing, correspondence estimation and Quatro) stated as an upper bound; hardware not named for the total",[8984],"not stated",[38],[40],[8988],"Quatro++ (whole pipeline)",[8937],{"slug":8991,"sourceId":8992,"sourceLabel":8993,"sourceYear":562,"table":91,"note":8994,"datasets":8995,"metrics":8996,"families":8997,"methods":8998,"methodIds":9018,"rows":3766,"failures":30},"lim2025kissmatcher-table-i","lim2025kissmatcher","Lim et al., 2025","KITTI 10 m benchmark [23]: scan-to-scan global registration; success if translation \u003C 2 m and rotation \u003C 5 deg; RTE and RRE averaged over successful r…",[1997],[23,1933],[23,1935],[8999,9000,9001,2631,9002,9003,9004,9005,9006,9007,8948,9008,9009,9010,9011,9012,4271,9013,9014,9015,9016,9017],"3DFeat-Net","D3Feat","DIP","FPFH + FGR","FPFH + FGR + G-ICP","FPFH + Quatro","FPFH + Quatro + G-ICP","FPFH + TEASER + G-ICP","FPFH + TEASER++","GeDi","MapClosures, W = 1","MapClosures, W = 3","MapClosures, W = 5","Predator","Proposed + G-ICP","STD, W = 1","STD, W = 3","STD, W = 5","SpinNet",[2574,8992,3292,9019,1789,8957],"std2023",{"slug":9021,"sourceId":8992,"sourceLabel":8993,"sourceYear":562,"table":9022,"note":9023,"datasets":9024,"metrics":9027,"families":9028,"methods":9029,"methodIds":9032,"rows":274,"failures":154},"lim2025kissmatcher-text-sec-iii-c","Text Sec. III-C","FPFH on voxelized 64-channel LiDAR scans of 10K to 30K points, multi-threaded",[9025,9026],"KITTI and MulRan","voxelized 64-channel LiDAR scans (dataset not named in Sec. III-C; KITTI and MulRan are the 64-channel datasets used)",[23,38],[40,23],[9030,9031],"FPFH","Faster-PFH vs FPFH",[8992,1788],{"slug":9034,"sourceId":8992,"sourceLabel":8993,"sourceYear":562,"table":5451,"note":9035,"datasets":9036,"metrics":9038,"families":9039,"methods":9040,"methodIds":9042,"rows":154,"failures":154},"lim2025kissmatcher-text-sec-iv-e","speed-up of the entire pipeline over the full TEASER++ pipeline in large-scale registration at the kilometre level (Sec. IV-E, Fig. 1(b)); the dataset…",[9037],"not named (large-scale registration at kilometre level, Fig. 1(b))",[23],[23],[9041],"KISS-Matcher vs TEASER++ pipeline",[8992],{"slug":9044,"sourceId":8992,"sourceLabel":8993,"sourceYear":562,"table":9045,"note":9046,"datasets":9047,"metrics":9048,"families":9049,"methods":9050,"methodIds":9053,"rows":274,"failures":30},"lim2025kissmatcher-text-table-i-caption","Text Table I caption","entire pipeline rate (feature extraction and matching to pose estimation) on the KITTI 10 m benchmark, stated in the Table I caption",[1997],[148,38],[40],[9051,9012,9052],"KISS-Matcher","other outlier-robust registration pipelines (not individually named)",[8992],{"slug":9055,"sourceId":4784,"sourceLabel":9056,"sourceYear":213,"table":91,"note":9057,"datasets":9058,"metrics":9060,"families":9061,"methods":9062,"methodIds":9065,"rows":618,"failures":63},"loamlivox2020-table-i","Lin & Zhang, 2020","Time consumption per frame; both methods use piecewise processing; parallel columns use 3 threads for registration",[9059],"not_reported (data used for timing not stated)",[38],[40],[9063,9064],"Baseline (A-LOAM)","Ours (Loam_livox)",[392,4784],{"slug":9067,"sourceId":4784,"sourceLabel":9056,"sourceYear":213,"table":1464,"note":9068,"datasets":9069,"metrics":9072,"families":9073,"methods":9074,"methodIds":9076,"rows":274,"failures":30},"loamlivox2020-text-sec-v-b","Odometry distance between two positions compared with distance from GPS coordinates on Google Maps",[9070,9071],"author-collected Livox MID40 data","author-collected Livox MID40 data with mocap",[23],[23],[9075],"Loam_livox",[4784],{"slug":9078,"sourceId":2387,"sourceLabel":9079,"sourceYear":306,"table":325,"note":9080,"datasets":9081,"metrics":9083,"families":9084,"methods":9085,"methodIds":9086,"rows":618,"failures":30},"r3live2022-table-ii","Lin & Zhang, 2022","Odometry drift at the end of four HKUST campus trajectories that return to the start, measured with an ArUco marker board; no loop closure. Version of…",[9082],"R3LIVE Experiment-2 (authors' data)",[1551],[1553],[2381],[2387],{"slug":9088,"sourceId":2387,"sourceLabel":9079,"sourceYear":306,"table":279,"note":9089,"datasets":9090,"metrics":9092,"families":9093,"methods":9094,"methodIds":9097,"rows":9098,"failures":30},"r3live2022-table-iii","Relative rotation error (RRE, deg) and relative translation error (RTE, %) over all sub-sequences of 50 to 300 m in two seaport sequences (Belcher Bay…",[9091],"R3LIVE Experiment-3 (authors' data, D-GPS RTK)",[2411,330],[332],[1437,2802,4779,9095,9096,1606],"R3LIVE-HiRes","R3LIVE-RT",[321,2386,4786,2387,251],144,{"slug":9100,"sourceId":2387,"sourceLabel":9079,"sourceYear":306,"table":731,"note":9101,"datasets":9102,"metrics":9104,"families":9105,"methods":9106,"methodIds":9109,"rows":1042,"failures":30},"r3live2022-table-iv","Average per-frame time over all experiments; VIO time depends on image size and map point resolution (Pc res). Version-of-record values; arXiv v1 Tabl…",[9103],"all R3LIVE experiments",[38],[40],[9107,9108],"R3LIVE LIO","R3LIVE VIO",[2387],{"slug":9111,"sourceId":2387,"sourceLabel":9079,"sourceYear":306,"table":9112,"note":9113,"datasets":9114,"metrics":9116,"families":9117,"methods":9118,"methodIds":9119,"rows":63,"failures":30},"r3live2022-text-sec-vi-b","Text Sec.VI-B","Narrow T-shaped passage facing white walls (LiDAR degenerate and texture-less); end-pose drift against an ArUco marker board",[9115],"R3LIVE Experiment-1 (authors' data)",[1551],[1553],[2381],[2387],{"slug":9121,"sourceId":9122,"sourceLabel":9123,"sourceYear":2267,"table":279,"note":9124,"datasets":9125,"metrics":9126,"families":9127,"methods":9128,"methodIds":9131,"rows":3111,"failures":578},"r3livepp2024-table-iii","r3livepp2024","Lin & Zhang, 2024","VoR Table III: absolute position error (APE, m) with standard deviation on NCLT (front-facing camera and 3D LiDAR, Segway robot), computed on the odom…",[310],[329],[25],[811,9129,334,2380,9130,4779],"Fast-LIO2","Our (R3LIVE++)",[321,815,338,2386,4786,9122],{"slug":9133,"sourceId":9122,"sourceLabel":9123,"sourceYear":2267,"table":827,"note":9134,"datasets":9135,"metrics":9137,"families":9138,"methods":9139,"methodIds":9142,"rows":274,"failures":30},"r3livepp2024-table-vi","VoR Table VI average photometric error between re-projected radiance-map points and image pixels over all R3LIVE-dataset sequences (unit not stated);…",[9136],"R3LIVE-dataset",[23],[23],[2381,9140,9141],"R3LIVE++","baseline (most recent image colors each LiDAR frame)",[2387,9122],{"slug":9144,"sourceId":9122,"sourceLabel":9123,"sourceYear":2267,"table":838,"note":9145,"datasets":9146,"metrics":9147,"families":9148,"methods":9149,"methodIds":9150,"rows":356,"failures":30},"r3livepp2024-table-vii","VoR Table VII mean (with STD) of sequence-average processing time per LiDAR or camera frame, CPU only",[310,9136],[38],[40],[9140],[9122],{"slug":9152,"sourceId":9122,"sourceLabel":9123,"sourceYear":2267,"table":870,"note":9153,"datasets":9154,"metrics":9155,"families":9156,"methods":9157,"methodIds":9158,"rows":618,"failures":356},"r3livepp2024-text-sec-vi-e","Robustness tests on R3LIVE-dataset: degenerate_seq_00 and 01 in front of a stairway with the LiDAR facing the ground and a wall; degenerate_seq_02 a n…",[9136],[1551],[1553],[1437,334,9140],[321,338,9122],{"slug":9160,"sourceId":4786,"sourceLabel":9161,"sourceYear":698,"table":91,"note":9162,"datasets":9163,"metrics":9165,"families":9166,"methods":9167,"methodIds":9170,"rows":1991,"failures":30},"r2live2021-table-i","Lin et al., 2021","Version of record Table I: relative rotation error (RRE, deg) and relative translation error (RTE, %) over all sub-sequences of each length, two fast-…",[9164],"R2LIVE Experiment-4 (authors' data, D-GPS RTK)",[2411,330],[332],[9168,9169,4779,1606],"Camvox","Fast-Lio",[1476,4786,251],{"slug":9172,"sourceId":4786,"sourceLabel":9161,"sourceYear":698,"table":325,"note":9173,"datasets":9174,"metrics":9179,"families":9180,"methods":9181,"methodIds":9185,"rows":608,"failures":30},"r2live2021-table-ii","Average running time per update in Experiments 1-4 on desktop PC and on-board computer (identical in arXiv v1 Table I)",[9175,9176,9177,9178],"R2LIVE Experiment-1 (authors' data)","R2LIVE Experiment-2 (authors' data)","R2LIVE Experiment-3 (authors' data)","R2LIVE Experiment-4 (authors' data)",[38],[40],[9182,9183,9184],"R2LIVE FG-OPM","R2LIVE LI-Odom","R2LIVE VI-Odom",[4786],{"slug":9187,"sourceId":84,"sourceLabel":9188,"sourceYear":374,"table":731,"note":9189,"datasets":9190,"metrics":9191,"families":9192,"methods":9193,"methodIds":9196,"rows":618,"failures":30},"lin2023immesh-table-iv","Lin et al., 2023","Per-scan processing time averaged over all sequences of each dataset (10 Hz LiDAR); meshing and localization run in parallel; Table IV also lists maxi…",[1997,310,1636,2381],[38],[40],[9194,9195],"ImMesh localization module","ImMesh meshing module",[84],{"slug":9198,"sourceId":84,"sourceLabel":9188,"sourceYear":374,"table":818,"note":9199,"datasets":9200,"metrics":9202,"families":9203,"methods":9204,"methodIds":9207,"rows":1069,"failures":30},"lin2023immesh-table-v","Complex Urban Dataset; ImMesh fed frame by frame with ground-truth poses (pose estimation disabled); Poisson (official implementation, octree level 12…",[9201],"Complex Urban Dataset",[74,75,76,23],[78,23],[9205,9206],"ImMesh (ours)","Poi",[84],{"slug":9209,"sourceId":84,"sourceLabel":9188,"sourceYear":374,"table":827,"note":9210,"datasets":9211,"metrics":9213,"families":9214,"methods":9215,"methodIds":9218,"rows":1991,"failures":30},"lin2023immesh-table-vi","AirSim synthetic scenes (20 m x 10 m x 8 m) from depth images (FoV 120 x 80 deg) at three resolutions; ImMesh and TSDF (PCL, GPU, 0.2 m cells) given g…",[9212],"AirSim synthetic",[74,75,76,23],[78,23],[9216,9205,9206,9217],"Del","TSDF",[84],{"slug":9220,"sourceId":9221,"sourceLabel":9222,"sourceYear":2267,"table":69,"note":9223,"datasets":9224,"metrics":9225,"families":9226,"methods":9227,"methodIds":9240,"rows":1042,"failures":63},"dpvslam2024-table-1","dpvslam2024","Lipson et al., 2024","TUM RGB-D freiburg1 (9 sequences), monocular; only the average column extracted; '-' = not computed because of failures; values checked against the EC…",[2872],[329,148,219],[25,40],[9228,9229,9230,9231,9232,9233,9234,9235,9236,9237,9238,9239],"DPV-SLAM","DPV-SLAM++","DROID-SLAM [ 31 ]","DeFlowSLAM [ 45 ]","DeepFactors [ 4 ]","DeepTAM [ 52 ]","DeepV2D [ 30 ]","DeepV2D [TartanAir]","GO-SLAM [ 51 ]","ORB-SLAM2 [ 18 ]","ORB-SLAM3 [ 2 ]","TartanVO [ 36 ]",[6178,1511,763],{"slug":9242,"sourceId":9221,"sourceLabel":9222,"sourceYear":2267,"table":9243,"note":9244,"datasets":9245,"metrics":9246,"families":9247,"methods":9248,"methodIds":9252,"rows":9253,"failures":654},"dpvslam2024-table-2b","Table 2b","KITTI odometry sequences 00-10, monocular ATE; X = failure, '-' = average not computed; values checked against the ECCV 2024 version of record (same t…",[1997],[329,148],[25,40],[9228,9229,9249,9230,9250,9251,9237,9238],"DPVO [ 32 ]","DROID-VO [ 31 ]","LDSO [ 11 ]",[6178,1511,763],104,{"slug":9255,"sourceId":9221,"sourceLabel":9222,"sourceYear":2267,"table":17,"note":9256,"datasets":9257,"metrics":9258,"families":9259,"methods":9260,"methodIds":9268,"rows":3323,"failures":356},"dpvslam2024-table-3","EuRoC MAV 11 sequences, monocular; only the average column extracted; dagger = VO method; values checked against the ECCV 2024 version of record (same…",[1482],[329,148,219],[25,40],[9228,9229,9261,9230,9262,9232,9263,9264,9236,9251,9265,9238,9266,9267],"DPVO [ 32 ] †","DSO [ 11 ] †","DeepV2D [ 30 ] †","DeepV2D [TartanAir] †","ORB-SLAM [ 17 ]","SVO [ 8 ] †","TartanVO 1 [ 36 ] †",[6178,1507,119,763,1512],{"slug":9270,"sourceId":9221,"sourceLabel":9222,"sourceYear":2267,"table":33,"note":9271,"datasets":9272,"metrics":9274,"families":9275,"methods":9276,"methodIds":9277,"rows":618,"failures":154},"dpvslam2024-table-4","TartanAir monocular test set (ECCV 2020 SLAM challenge, MH000-MH007); only the average column extracted; values checked against the ECCV 2024 version…",[9273],"TartanAir",[329],[25],[9228,9229,9249,9230,9231,9234,9265,9239],[6178,119],{"slug":9279,"sourceId":9280,"sourceLabel":9281,"sourceYear":2267,"table":69,"note":9282,"datasets":9283,"metrics":9284,"families":9285,"methods":9286,"methodIds":9294,"rows":654,"failures":30},"loopyslam2024-table-1","loopyslam2024","Liso et al., 2024","ATE RMSE (cm) on Replica (synthetic RGB-D), Horn closed-form alignment before ATE (App. C); truncated to the average over the 8 scenes (Rm 0-2, Off 0-…",[3223],[568],[25],[9287,9288,9289,9290,9291,9292,9293],"ESLAM [28]","GO-SLAM [76]","Loopy-SLAM (Ours)","MIPS-Fusion [57]","NICE-SLAM [77]","Point-SLAM [45]","Vox-Fusion [73]",[3182,9280,3184,3185],{"slug":9296,"sourceId":9280,"sourceLabel":9281,"sourceYear":2267,"table":3189,"note":9297,"datasets":9298,"metrics":9299,"families":9300,"methods":9301,"methodIds":9305,"rows":2551,"failures":30},"loopyslam2024-table-11","Replica mesh reconstruction averaged over 8 scenes: meshes from marching cubes, ICP-aligned to ground truth before precision and recall; precision, re…",[3223],[74,23],[78,23],[9302,9287,9303,9304,9289,9291,9292,9293],"Co-SLAM [61]","GO-SLAM [76] (as reported)","GO-SLAM [76] (reproduced, random poses)",[3181,3182,9280,3184,3185],{"slug":9307,"sourceId":9280,"sourceLabel":9281,"sourceYear":2267,"table":108,"note":9308,"datasets":9309,"metrics":9310,"families":9311,"methods":9312,"methodIds":9324,"rows":9326,"failures":30},"loopyslam2024-table-2","ATE RMSE (cm) on TUM-RGBD, trajectories aligned with Horn's closed-form solution before ATE (App. C; whether scale was estimated is not stated); avera…",[6663],[568],[25],[9313,9314,9315,9302,9316,9287,9317,9318,9319,9320,9321,9291,9322,9292,9293,9323],"BAD-SLAM [48] (LC)","BundleFusion [13] (LC)","Cao et al. [7] (LC)","DI-Fusion [21]","ElasticFusion [71] (LC)","GO-SLAM [76] (LC)","Kintinuous [69] (LC)","Loopy-SLAM (Ours, LC)","MIPS-Fusion [57] (LC)","ORB-SLAM2 [34] (LC)","Yan et al. [72] (LC)",[9325,2897,3181,2915,3182,2448,9280,3184,1511,3185],"badslam2019",73,{"slug":9328,"sourceId":9280,"sourceLabel":9281,"sourceYear":2267,"table":17,"note":9329,"datasets":9330,"metrics":9331,"families":9332,"methods":9333,"methodIds":9334,"rows":102,"failures":30},"loopyslam2024-table-3","ATE RMSE (cm) on ScanNet after Horn closed-form alignment (App. C); reference poses come from BundleFusion; Avg.-6 and Avg.-9 average over 6 and 9 sce…",[2963],[568],[25],[9302,9287,9288,9289,9290,9291,9292,9293],[3181,3182,9280,3184,3185],{"slug":9336,"sourceId":9280,"sourceLabel":9281,"sourceYear":2267,"table":244,"note":9337,"datasets":9338,"metrics":9339,"families":9340,"methods":9341,"methodIds":9342,"rows":2337,"failures":30},"loopyslam2024-table-5","Runtime and memory on Replica office 0. Loopy-SLAM tracking and mapping times are identical to the Point-SLAM row (the text says they are equivalent e…",[3223],[219,38],[40],[9287,9288,9289,9291,9292,9293],[3182,9280,3184,3185],{"slug":9344,"sourceId":9280,"sourceLabel":9281,"sourceYear":2267,"table":5342,"note":9345,"datasets":9346,"metrics":9347,"families":9348,"methods":9349,"methodIds":9351,"rows":63,"failures":30},"loopyslam2024-text-sec-4-4","Loop-closure cost on TUM-RGBD fr1 desk: 7 PGOs; registrations can run in parallel with mapping except those of the active submap.",[6663],[23],[23],[9350],"Loopy-SLAM",[9280],{"slug":9353,"sourceId":7567,"sourceLabel":9354,"sourceYear":698,"table":91,"note":9355,"datasets":9356,"metrics":9358,"families":9359,"methods":9360,"methodIds":9363,"rows":356,"failures":30},"balm2021-table-i","Liu & Zhang, 2021","Handheld Livox Horizon walk on HKU campus, about 817 m in 20 min returning to the start; translation error at the end point (percentage of distance in…",[9357],"authors' handheld Livox Horizon data (HKU campus)",[1551],[1553],[9361,9362],"BALM","LOAM [10] (livox_mapping implementation)",[7567,2118],{"slug":9365,"sourceId":7567,"sourceLabel":9354,"sourceYear":698,"table":325,"note":9366,"datasets":9367,"metrics":9369,"families":9370,"methods":9371,"methodIds":9372,"rows":120,"failures":30},"balm2021-table-ii","Velodyne VLP-16 sample data released with LeGO-LOAM, 210 m path with identical start and end point; translation drift at return (percentage of distanc…",[9368],"LeGO-LOAM VLP-16 sample data",[1551],[1553],[9361,461,2197],[7567,395,2118],{"slug":9374,"sourceId":7567,"sourceLabel":9354,"sourceYear":698,"table":481,"note":9375,"datasets":9376,"metrics":9377,"families":9378,"methods":9379,"methodIds":9380,"rows":63,"failures":30},"balm2021-text-sec-vi-a","Elevation error near the start and end point of the 817 m handheld Livox Horizon walk, stated in text from the side views of Fig. 6(c,d)",[9357],[23],[23],[9361,9362],[7567,2118],{"slug":9382,"sourceId":7567,"sourceLabel":9354,"sourceYear":698,"table":4664,"note":9383,"datasets":9384,"metrics":9386,"families":9387,"methods":9388,"methodIds":9390,"rows":154,"failures":154},"balm2021-text-sec-vi-d","Local BA plus voxel-map update over a sliding window of the 20 most recent scans, triggered every 5 scans (2 Hz); text states it completes within 100…",[9385],"authors' Livox Horizon, Livox Mid-40 and VLP-16 experiments (A to C)",[23],[23],[9389],"BALM (local BA back-end)",[7567],{"slug":9392,"sourceId":9393,"sourceLabel":9394,"sourceYear":374,"table":9395,"note":9396,"datasets":9397,"metrics":9399,"families":9400,"methods":9401,"methodIds":9403,"rows":274,"failures":30},"balm2-2023-supplementary-table-v","balm2_2023","Liu et al., 2023a","Supplementary Table V","Supplementary application: LIO with EKF front-end and sliding-window local BA over 20 scans with IMU preintegration vs FAST-LIO2 (results read from th…",[9398],"utbm, uclk, nclt (11 sequences)",[568,38],[25,40],[1437,9402],"Local-BA (LIO with BALM2 sliding-window BA)",[9393,321],{"slug":9405,"sourceId":9393,"sourceLabel":9394,"sourceYear":374,"table":9406,"note":9407,"datasets":9408,"metrics":9409,"families":9410,"methods":9411,"methodIds":9416,"rows":654,"failures":30},"balm2-2023-supplementary-table-vii","Supplementary Table VII","Supplementary application: global BA over all KITTI poses initialised with MULLS odometry (loop closure enabled); CT-ICP with loop closure as referenc…",[469],[568],[25],[9361,9412,3131,9413,4169,9414,9415],"BAREG","EF","Our","PA (inner)",[7567,9393,596,9417,597],"eigenfactors2019",{"slug":9419,"sourceId":9393,"sourceLabel":9394,"sourceYear":374,"table":325,"note":9420,"datasets":9421,"metrics":9424,"families":9425,"methods":9426,"methodIds":9433,"rows":9434,"failures":30},"balm2-2023-table-ii","ATE RMSE (m) of multi-view registration; scans deskewed by FAST-LIO2 (odometry output discarded) and downsampled from 10 Hz to 2 Hz; ICP, GICP, NDT fr…",[9422,9423,1636,8748],"Hilti 2021, VIRAL and UrbanLoco (19 sequences)","Hilti SLAM Challenge 2021",[568],[25],[9361,9412,9413,9427,9428,9429,81,9430,9431,9432,9415],"GICP (PCL, incremental)","ICP (PCL, incremental)","NDT (PCL, incremental)","Ours (edge)","Ours (float)","PA",[7567,9393,478,9417,3292],116,{"slug":9436,"sourceId":9393,"sourceLabel":9394,"sourceYear":374,"table":279,"note":9437,"datasets":9438,"metrics":9439,"families":9440,"methods":9441,"methodIds":9442,"rows":2420,"failures":30},"balm2-2023-table-iii","Occupied 0.1 m cells of the registered point-cloud map (fewer is better, no reference map needed); all columns except Ours are increments over the Our…",[9423],[23],[23],[9361,9412,9413,9427,9428,9429,81,9430,9431,9432,9415],[7567,9393,478,9417,3292],{"slug":9444,"sourceId":9393,"sourceLabel":9394,"sourceYear":374,"table":731,"note":9445,"datasets":9446,"metrics":9447,"families":9448,"methods":9449,"methodIds":9450,"rows":29,"failures":30},"balm2-2023-table-iv","Total optimization time of the BA methods on the Table II inputs (pairwise methods excluded); the table does not state the time unit; desktop Intel i7…",[9422,9423],[23],[23],[9361,9412,9413,81,9430,9431,9432,9415],[7567,9393,9417],{"slug":9452,"sourceId":9453,"sourceLabel":9454,"sourceYear":374,"table":325,"note":9455,"datasets":9456,"metrics":9457,"families":9458,"methods":9459,"methodIds":9461,"rows":415,"failures":30},"hba2023-table-ii","hba2023","Liu et al., 2023b","KITTI with loop-closed MULLS poses as HBA input; RMSE of ATE printed as rotation (deg)\u002Ftranslation (m); only the translation part is extracted; the '(…",[1997],[568],[25],[3131,461,9460,4169,4271,1976],"LiTAMIN2",[596,9453,2118,597,432],{"slug":9463,"sourceId":9453,"sourceLabel":9454,"sourceYear":374,"table":731,"note":9464,"datasets":9465,"metrics":9467,"families":9468,"methods":9469,"methodIds":9471,"rows":416,"failures":30},"hba2023-table-iv","New College long_experiment (N = 26557 frames; ref. [20], the Newer College dataset paper); HBA input is FAST-LIO2 odometry without loop closure; the…",[9466],"New College (Newer College dataset family)",[568],[25],[3131,1437,9470,334,4271],"GICP Matching Factor",[596,321,9453,338],{"slug":9473,"sourceId":9453,"sourceLabel":9454,"sourceYear":374,"table":818,"note":9474,"datasets":9475,"metrics":9476,"families":9477,"methods":9478,"methodIds":9480,"rows":6014,"failures":30},"hba2023-table-v","KITTI with MULLS poses without loop closure as HBA input; RMSE of ATE printed as rotation (deg)\u002Ftranslation (m); only translation extracted; baselines…",[1997],[568],[25],[3131,461,9460,4169,4271,1976,9479],"Voxel Map",[596,9453,2118,597,432,7515],{"slug":9482,"sourceId":9453,"sourceLabel":9454,"sourceYear":374,"table":827,"note":9483,"datasets":9484,"metrics":9486,"families":9487,"methods":9488,"methodIds":9489,"rows":356,"failures":30},"hba2023-table-vi","Self-collected solid-state LiDAR scenes without ground truth; mean map entropy (lower is more consistent); HBA input is FAST-LIO2 without loop closure",[9485],"self-collected",[23],[23],[1437,4271],[321,9453],{"slug":9491,"sourceId":9453,"sourceLabel":9454,"sourceYear":374,"table":852,"note":9492,"datasets":9493,"metrics":9494,"families":9495,"methods":9496,"methodIds":9500,"rows":1315,"failures":30},"hba2023-table-viii","MulRan DCC sequences with LIO-SAM loop-closed input ('Initial'); original BA, reduced block-diagonal BA and proposed hierarchical BA; RMSE of ATE and…",[671],[568,23],[25,23],[9497,9498,4271,9499],"Initial (LIO-SAM with loop closure)","Original BA","Reduced BA",[9393,9453,338],{"slug":9502,"sourceId":9503,"sourceLabel":9504,"sourceYear":2267,"table":9505,"note":9506,"datasets":9507,"metrics":9509,"families":9510,"methods":9511,"methodIds":9513,"rows":274,"failures":274},"glio2024-sec-iv-timing","glio2024","Liu et al., 2024","Sec. IV timing","Timing stated in the text (computer not specified)",[9508],"UrbanNav (Hong Kong)",[38],[40],[9512],"GLIO",[9503],{"slug":9515,"sourceId":9503,"sourceLabel":9504,"sourceYear":2267,"table":91,"note":9516,"datasets":9517,"metrics":9518,"families":9519,"methods":9520,"methodIds":9526,"rows":9527,"failures":30},"glio2024-table-i","UrbanNav dataset; positioning error against NovAtel SPAN-CPT RTK\u002FINS ground truth in metres; UrbanNav TST: starts under an overpass, dense tall office…",[9508],[329,22,23],[25,23],[9521,9522,9523,9524,9525],"GLIO-DS (full two-stage)","GLIO-SS (single-stage only)","LIO (LILI-OM, aligned to the world frame by ground truth)","LIO-GNSS (LIO-SAM with DGNSS from RTKLIB)","RTKLIB (GNSS RTK)",[9503,337,338],31,{"slug":9529,"sourceId":9503,"sourceLabel":9504,"sourceYear":2267,"table":325,"note":9530,"datasets":9531,"metrics":9532,"families":9533,"methods":9534,"methodIds":9535,"rows":9527,"failures":30},"glio2024-table-ii","UrbanNav dataset; positioning error against NovAtel SPAN-CPT RTK\u002FINS ground truth in metres; UrbanNav Whampoa: over 25 min and 4.5 km from open sky in…",[9508],[329,22,23],[25,23],[9521,9522,9523,9524,9525],[9503,337,338],{"slug":9537,"sourceId":9538,"sourceLabel":9539,"sourceYear":562,"table":9540,"note":9541,"datasets":9542,"metrics":9544,"families":9545,"methods":9546,"methodIds":9550,"rows":102,"failures":30},"slam3r2025-supp-table-8","slam3r2025","Liu et al., 2025","Supp. Table 8","Three sampled scenes per dataset (supplementary); average accuracy and completeness in cm (unit by analogy with Tables 1-2)",[9543,2963,6106],"ETH3D",[75,76],[78],[9547,9548,9549],"DUSt3R [ 64 ]","SLAM3R (Ours)","Spann3R [ 61 ]",[8482],{"slug":9552,"sourceId":9538,"sourceLabel":9539,"sourceYear":562,"table":69,"note":9553,"datasets":9554,"metrics":9556,"families":9557,"methods":9558,"methodIds":9561,"rows":6348,"failures":416},"slam3r2025-table-1","7 Scenes, one-twentieth of frames of each test sequence as input video; accuracy and completeness in cm against back-projected ground-truth depth; SLA…",[9555],"7-Scenes",[148,75,76],[40,78],[9547,9559,9548,9560,9549],"MASt3R [ 28 ]","SLAM3R-NoConf (Ours)",[8482,8462],{"slug":9563,"sourceId":9538,"sourceLabel":9539,"sourceYear":562,"table":108,"note":9564,"datasets":9565,"metrics":9566,"families":9567,"methods":9568,"methodIds":9573,"rows":849,"failures":52},"slam3r2025-table-2","Replica full videos; accuracy and completeness in cm (average of 8 scenes; per-scene values not extracted); * = values reported in NICER-SLAM; DUSt3R…",[3223],[148,75,76],[40,78],[9569,9570,9547,9571,9559,9572,9548,9560,9549],"DIM-SLAM [ 29 ] (value from NICER-SLAM)","DROID-SLAM [ 56 ] (value from NICER-SLAM)","GO-SLAM [ 75 ]","NICER-SLAM [ 79 ] (value from NICER-SLAM)",[6178,8482,8462],{"slug":9575,"sourceId":9538,"sourceLabel":9539,"sourceYear":562,"table":17,"note":9576,"datasets":9577,"metrics":9578,"families":9579,"methods":9580,"methodIds":9582,"rows":102,"failures":63},"slam3r2025-table-3","Camera pose accuracy; SLAM3R, DUSt3R and MASt3R poses derived from predicted points with PnP-RANSAC and ground-truth intrinsics; average over test sce…",[9555,3223],[568],[25],[9581,9570,9547,9571,9559,9572,9548,9560,9549],"DIM-SLAM [ 29 ]",[6178,8482,8462],{"slug":9584,"sourceId":9585,"sourceLabel":9586,"sourceYear":68,"table":9587,"note":9588,"datasets":9589,"metrics":9603,"families":9604,"methods":9605,"methodIds":9609,"rows":1980,"failures":2420},"voxelslam2026-table-2-full-slam-with-lc","voxelslam2026","Liu et al., 2026","Table 2 (full SLAM with LC)","Hilti handheld sequences; ATE exported from the Hilti evaluation website; full SLAM with loop closure (Our (Full) adds global mapping)",[9590,9591,9592,9593,9594,9595,9596,9597,9598,9599,9600,9601,9602],"Hilti handheld sequence exp01-construction (name per Table C1)","Hilti handheld sequence exp02-construction (name per Table C1)","Hilti handheld sequence exp03-construction (name per Table C1)","Hilti handheld sequence exp07-long-corridor (name per Table C1)","Hilti handheld sequence exp09-cupola (name per Table C1)","Hilti handheld sequence exp11-lower-gallery (name per Table C1)","Hilti handheld sequence exp15-upper-gallery (name per Table C1)","Hilti handheld sequence exp21-outside (name per Table C1)","Hilti handheld sequence site1-handheld-1 (name per Table C1)","Hilti handheld sequence site1-handheld-2 (name per Table C1)","Hilti handheld sequence site1-handheld-3 (name per Table C1)","Hilti handheld sequence site1-handheld-4 (name per Table C1)","Hilti handheld sequence site1-handheld-5 (name per Table C1)",[568],[25],[334,9606,2197,335,9607,9608],"LTA-OM","Our (Full)","Our (Odom+LM+LC)",[395,337,338,9610,9585],"ltaom2024",{"slug":9612,"sourceId":9585,"sourceLabel":9586,"sourceYear":68,"table":9613,"note":9614,"datasets":9615,"metrics":9616,"families":9617,"methods":9618,"methodIds":9621,"rows":9622,"failures":641},"voxelslam2026-table-2-odometry-without-lc","Table 2 (odometry without LC)","Hilti handheld sequences (Hesai XT-32, BMI085 400 Hz); ATE exported from the Hilti evaluation website; odometry without loop closure; all methods with…",[9590,9591,9592,9593,9594,9595,9596,9597,9598,9599,9600,9601,9602],[568],[25],[1437,317,4769,334,2197,335,9619,9620,4052],"Our (Odom)","Our (Odom+LM)",[304,321,395,337,4783,338,4054,9585],117,{"slug":9624,"sourceId":9585,"sourceLabel":9586,"sourceYear":68,"table":244,"note":9625,"datasets":9626,"metrics":9628,"families":9629,"methods":9630,"methodIds":9633,"rows":224,"failures":30},"voxelslam2026-table-5","Multisession SLAM: sequences fed in the order hilti13, 12, 11, 10, 09; ATE of each session and of the merged multisession trajectory, pose graph optim…",[9598,9599,9600,9601,9602,9627],"Hilti site1-handheld-1 to 5 merged in one world frame",[568],[25],[9631,9632],"Voxel-SLAM (PGO only)","Voxel-SLAM (global mapping)",[9585],{"slug":9635,"sourceId":9636,"sourceLabel":9637,"sourceYear":90,"table":7910,"note":9638,"datasets":9639,"metrics":9640,"families":9641,"methods":9642,"methodIds":9644,"rows":154,"failures":30},"lorensen1987marchingcubes-text-sec-5-1","lorensen1987marchingcubes","Lorensen & Cline, 1987","Speed-up from reusing edge intersections of previous pixels and lines",[],[23],[23],[9643],"marching cubes with coherence",[9636],{"slug":9646,"sourceId":9636,"sourceLabel":9637,"sourceYear":90,"table":668,"note":9647,"datasets":9648,"metrics":9651,"families":9652,"methods":9653,"methodIds":9655,"rows":63,"failures":30},"lorensen1987marchingcubes-text-sec-6","Implementation times and model sizes stated in the text; no accuracy evaluation; C implementation, times depend on number of surfaces and data resolut…",[9649,9650],"CT study","SPECT study",[23],[23],[9654],"marching cubes",[9636],{"slug":9657,"sourceId":9636,"sourceLabel":9637,"sourceYear":90,"table":6771,"note":9658,"datasets":9659,"metrics":9663,"families":9664,"methods":9665,"methodIds":9666,"rows":356,"failures":30},"lorensen1987marchingcubes-text-sec-7","Triangle counts of the case-study models",[9660,9661,9662],"CT head (93 axial slices, 1.5 mm, 0.8 mm pixels)","MR head (128 coronal slices, 1.9 mm)","SPECT heart (29 coronal slices, 64 x 64)",[23],[23],[9654],[9636],{"slug":9668,"sourceId":9669,"sourceLabel":9670,"sourceYear":1111,"table":731,"note":9671,"datasets":9672,"metrics":9674,"families":9675,"methods":9676,"methodIds":9678,"rows":3547,"failures":30},"lupton2012preint-table-iv","lupton2012preint","Lupton & Sukkarieh, 2012","Validation in a normal office: hand-held unit carried back and forth between two floor marks 9 m apart and set down on each mark, where its true locat…",[9673],"office validation sequence (Sec. VIII-C)",[23],[23],[9677],"stereo-inertial navigation with preintegrated inertial delta observations and SWFI (30-pose window)",[9669],{"slug":9680,"sourceId":9669,"sourceLabel":9670,"sourceYear":1111,"table":9681,"note":9682,"datasets":9683,"metrics":9684,"families":9685,"methods":9686,"methodIds":9687,"rows":154,"failures":30},"lupton2012preint-text-sec-viii-c","Text Sec.VIII-C","Validation in an office: hand-held unit carried back and forth between two floor marks 9 m apart and set down on the marks; estimated states at the st…",[9673],[1551],[1553],[9677],[9669],{"slug":9689,"sourceId":9669,"sourceLabel":9670,"sourceYear":1111,"table":9690,"note":9691,"datasets":9692,"metrics":9693,"families":9694,"methods":9695,"methodIds":9696,"rows":274,"failures":30},"lupton2012preint-text-sec-viii-c-gravity","Text Sec.VIII-C gravity","Gravity vector estimated independently in successive sliding windows at the stationary poses of Table IV, without supplying the true magnitude; true v…",[9673],[23],[23],[9677],[9669],{"slug":9698,"sourceId":9669,"sourceLabel":9670,"sourceYear":1111,"table":9699,"note":9700,"datasets":9701,"metrics":9702,"families":9703,"methods":9704,"methodIds":9706,"rows":154,"failures":154},"lupton2012preint-text-sec-viii-e","Text Sec.VIII-E","Same validation sequence processed with stereo observations only; true distance of the second stop from the start is 9 m",[9673],[23],[23],[9705],"stereo-only estimation",[],{"slug":9708,"sourceId":9669,"sourceLabel":9670,"sourceYear":1111,"table":9709,"note":9710,"datasets":9711,"metrics":9713,"families":9714,"methods":9715,"methodIds":9716,"rows":356,"failures":30},"lupton2012preint-text-sec-viii-f","Text Sec.VIII-F","Real-world hand-held walking datasets that start while moving and end near the start; final error of the last pose relative to the first; no loop clos…",[9712],"University of Sydney buildings (Rose St. and Link)",[1551],[1553],[9677],[9669],{"slug":9718,"sourceId":9669,"sourceLabel":9670,"sourceYear":1111,"table":9719,"note":9720,"datasets":9721,"metrics":9722,"families":9723,"methods":9724,"methodIds":9727,"rows":63,"failures":30},"lupton2012preint-text-sec-viii-g","Text Sec.VIII-G","Dataset 1 processed with the left camera only plus IMU; weak range prior between the first two poses removed after ten image pairs; final position err…",[9712],[1551],[1553],[9725,9726],"monocular-inertial navigation (scale from accelerometer)","stereo-inertial navigation",[9669],{"slug":9729,"sourceId":9730,"sourceLabel":9731,"sourceYear":213,"table":91,"note":9732,"datasets":9733,"metrics":9735,"families":9736,"methods":9737,"methodIds":9739,"rows":721,"failures":30},"lv2020licalib-table-i","lv2020licalib","Lv et al., 2020","SD of IMU-to-LiDAR extrinsic estimates over five trials per scene after eight iterations (means omitted)",[9734],"own real-world sequences",[23],[23],[9738],"LI-Calib",[9730],{"slug":9741,"sourceId":9730,"sourceLabel":9731,"sourceYear":213,"table":325,"note":9742,"datasets":9743,"metrics":9744,"families":9745,"methods":9746,"methodIds":9747,"rows":618,"failures":30},"lv2020licalib-table-ii","Difference between calibrated relative pose of IMU2 or IMU3 w.r.t. IMU1 (mean over indoor datasets) and CAD reference",[9734],[23],[23],[9738],[9730],{"slug":9749,"sourceId":9730,"sourceLabel":9731,"sourceYear":213,"table":1452,"note":9750,"datasets":9751,"metrics":9752,"families":9753,"methods":9754,"methodIds":9755,"rows":356,"failures":30},"lv2020licalib-text-sec-v-a","Monte Carlo simulation, 10 sequences of 10 s, three orthogonal planes, sinusoidal IMU motion; the text gives each result as value +\u002F- value without de…",[4430],[23],[23],[9738],[9730],{"slug":9757,"sourceId":9730,"sourceLabel":9731,"sourceYear":213,"table":1464,"note":9758,"datasets":9759,"metrics":9761,"families":9762,"methods":9763,"methodIds":9765,"rows":154,"failures":30},"lv2020licalib-text-sec-v-b","Spline trajectory vs Vicon over ten sequences of about 15 s; value is the average over sequences, per-sequence statistic not stated",[9760],"own Vicon-room sequences",[22],[25],[9764],"LI-Calib continuous-time trajectory",[9730],{"slug":9767,"sourceId":9768,"sourceLabel":9769,"sourceYear":698,"table":325,"note":9770,"datasets":9771,"metrics":9773,"families":9774,"methods":9775,"methodIds":9777,"rows":1069,"failures":30},"clins2021-table-ii","clins2021","Lv et al., 2021","LIOM room-scale sequences with ground truth (fast\u002Fmid\u002Fslow motion); translation and rotation RMSE; LOAM and LIOM values copied from the LIOM paper; CL…",[9772],"LIOM dataset (Ye et al., ICRA 2019)",[568,23],[25,23],[9776,334,4770,461],"CLINS",[9768,2117,338,2118],{"slug":9779,"sourceId":9768,"sourceLabel":9769,"sourceYear":698,"table":731,"note":9780,"datasets":9781,"metrics":9784,"families":9785,"methods":9786,"methodIds":9789,"rows":1042,"failures":30},"clins2021-table-iv","Large-scale vehicle sequences; APE RMSE (evo) against provided ground truth (RTK-GPS for YQ, dataset ground truth for KAIST); (odom) = without loop co…",[9782,9783],"KAIST Urban (Complex Urban dataset)","YQ (authors' campus sequences)",[568],[25],[9776,9787,334,9788,4770],"CLINS(odom)","LIO-SAM (odom)",[9768,2117,338],{"slug":9791,"sourceId":9768,"sourceLabel":9769,"sourceYear":698,"table":1441,"note":9792,"datasets":9793,"metrics":9794,"families":9795,"methods":9796,"methodIds":9797,"rows":154,"failures":30},"clins2021-text-sec-v-d","Timing of one non-rigid registration (four or five LM iterations, automatic derivatives)",[2070],[38],[40],[9776],[9768],{"slug":9799,"sourceId":9800,"sourceLabel":9801,"sourceYear":374,"table":279,"note":9802,"datasets":9803,"metrics":9804,"families":9805,"methods":9806,"methodIds":9817,"rows":339,"failures":30},"clic2023-table-iii","clic2023","Lv et al., 2023","NTU VIRAL dataset (MAV, indoor and outdoor); APE RMSE in metres; sensors L = LiDAR, I = IMU, C = camera, L2 = two LiDARs; rows marked (2) are results…",[1636],[568],[25],[9807,9808,9809,9810,9811,9812,9813,9814,9815,9816],"CLIC (w\u002Fo loop) [L, I, C]","CLIC2 (w\u002Fo loop) [L2, I, C]","CLINS (w\u002Fo loop) [L, I]","CLIO (w\u002Fo loop) [L, I]","CLIO2 (w\u002Fo loop) [L2, I]","LIO-SAM(2) [L, I]","MILIOM (2 LiDARs)(2) [L2, I]","MILIOM (horz. LiDAR)(2) [L, I]","VIRAL (2 LiDARs)(2) [L2, I, C]","VIRAL (horz. LiDAR)(2) [L, I]",[9800,9768,338],{"slug":9819,"sourceId":9800,"sourceLabel":9801,"sourceYear":374,"table":731,"note":9820,"datasets":9821,"metrics":9822,"families":9823,"methods":9824,"methodIds":9829,"rows":224,"failures":30},"clic2023-table-iv","Newer College Dataset (handheld, 64-beam Ouster with internal IMU); APE RMSE in metres; LiDAR-IMU methods only",[1378],[568],[25],[9825,9826,9827,9828],"CLIO (w\u002F loop)","CLIO (w\u002Fo loop)","LIO-SAM (w\u002F loop)","LIO-SAM (w\u002Fo loop)",[9800,338],{"slug":9831,"sourceId":9800,"sourceLabel":9801,"sourceYear":374,"table":818,"note":9832,"datasets":9833,"metrics":9835,"families":9836,"methods":9837,"methodIds":9845,"rows":102,"failures":274},"clic2023-table-v","LVI-SAM dataset (handheld and Jackal, outdoor open vegetated and geometrically degenerate areas; 16-beam LiDAR 10 Hz, camera 20 Hz, IMU 500 Hz); APE R…",[9834],"LVI-SAM dataset",[568],[25],[9838,9839,9807,9840,9810,9841,9842,9843,9844],"CLIC (w\u002F loop) [L, I, C]","CLIC (w\u002F loop, w\u002F calib) [L, I, C]","CLIO (w\u002F loop) [L, I]","LIO-SAM (w\u002F loop) [L, I]","LIO-SAM (w\u002Fo loop) [L, I]","LVI-SAM (w\u002F loop) [L, I, C]","LVI-SAM (w\u002Fo loop) [L, I, C]",[9800,338,2386],{"slug":9847,"sourceId":9800,"sourceLabel":9801,"sourceYear":374,"table":838,"note":9848,"datasets":9849,"metrics":9851,"families":9852,"methods":9853,"methodIds":9857,"rows":102,"failures":30},"clic2023-table-vii","Self-collected Vicon Room dataset (indoor, handheld random motion, same rig as YQ: 16-beam LiDAR, camera, IMU); motion-capture ground truth; APE RMSE…",[9850],"CLIC Vicon Room dataset (authors)",[568],[25],[9854,9855,9856],"CLIC (w\u002Fo loop)","LIC-Fusion 2.0 (w\u002Fo loop)","LVI-SAM (w\u002Fo loop)",[9800,9858,2386],"licfusion2_2020",{"slug":9860,"sourceId":9800,"sourceLabel":9801,"sourceYear":374,"table":852,"note":9861,"datasets":9862,"metrics":9863,"families":9864,"methods":9865,"methodIds":9867,"rows":641,"failures":30},"clic2023-table-viii","Time consumption (seconds) of main modules over the whole eee_01 sequence (397 s) of NTU VIRAL on an Intel i7-7700K desktop with 32 GB RAM",[1636],[23],[23],[8054,9776,9866],"CLIO",[9800,9768],{"slug":9869,"sourceId":9870,"sourceLabel":9871,"sourceYear":698,"table":9872,"note":9873,"datasets":9874,"metrics":9876,"families":9877,"methods":9878,"methodIds":9881,"rows":63,"failures":30},"slamtoolbox2021-text-summary-and-features","slamtoolbox2021","Macenski & Jambrecic, 2021","Text Summary and Features","Scale and speed statements without a controlled benchmark",[3980,9875],"not_applicable (deployments)",[23],[23],[9879,9880],"SLAM Toolbox","SLAM Toolbox vs Open Karto",[9870],{"slug":9883,"sourceId":9884,"sourceLabel":9885,"sourceYear":562,"table":69,"note":9886,"datasets":9887,"metrics":9888,"families":9889,"methods":9890,"methodIds":9897,"rows":301,"failures":30},"vggtslam2025-table-1","vggtslam2025","Maggio et al., 2025","ATE RMSE on 7-Scenes computed with evo (alignment not stated); calibrated intrinsics; value reported from MASt3R-SLAM",[9555],[568],[25],[6667,9891,9892,9893,9894,9895,9896],"DROID-SLAM*","MASt3R-SLAM","MASt3R-SLAM*","NICER-SLAM","Ours (SL(4), w = 32)","Ours (Sim(3), w = 32)",[6178,9898,9884],"mast3rslam2025",{"slug":9900,"sourceId":9884,"sourceLabel":9885,"sourceYear":562,"table":108,"note":9901,"datasets":9902,"metrics":9903,"families":9904,"methods":9905,"methodIds":9908,"rows":1069,"failures":154},"vggtslam2025-table-2","ATE RMSE on TUM RGB-D (RGB only) computed with evo (alignment not stated); uncalibrated rows; floor sequence degenerate for SL(4) homography",[2872],[568],[25],[9228,9229,6667,9891,2877,9906,9907,9892,9893,892,9895,9896],"DeepV2D","GO-SLAM",[6178,9898,763,9884],{"slug":9910,"sourceId":9884,"sourceLabel":9885,"sourceYear":562,"table":17,"note":9911,"datasets":9912,"metrics":9913,"families":9914,"methods":9915,"methodIds":9918,"rows":429,"failures":63},"vggtslam2025-table-3","Dense reconstruction on 7-Scenes following the MASt3R-SLAM protocol, RMSE in metres; calibrated; @n means a keyframe every n images",[9555],[568,2343,75,76],[25,78],[6667,9892,9893,9895,9896,9916,9917],"Spann3R @2","Spann3R @20",[6178,9898,9884],{"slug":9920,"sourceId":9884,"sourceLabel":9885,"sourceYear":562,"table":33,"note":9921,"datasets":9922,"metrics":9924,"families":9925,"methods":9926,"methodIds":9929,"rows":578,"failures":30},"vggtslam2025-table-4","Runtime per stage on the custom office_loop sequence with window size w = 16, averaged over five runs; stage times cover all frames of a submap (NeurI…",[9923],"office_loop (authors' custom sequence)",[23],[23],[9927,9928],"VGGT-SLAM w\u002F SL(4)","VGGT-SLAM w\u002F Sim(3)",[9884],{"slug":9931,"sourceId":9932,"sourceLabel":9933,"sourceYear":1694,"table":9934,"note":9935,"datasets":9936,"metrics":9938,"families":9939,"methods":9940,"methodIds":9942,"rows":154,"failures":154},"magnusson2007ndt3d-text-fig-11-caption","magnusson2007ndt3d","Magnusson et al., 2007","Text Fig. 11 caption","first 65 KVARNTORP-LOOP scans registered with manual intervention where registration failed and for scans without odometry; loop of about 330 m",[9937],"KVARNTORP-LOOP",[1551],[1553],[9941],"scan registration with manual intervention (algorithm not named in the caption)",[],{"slug":9944,"sourceId":9932,"sourceLabel":9933,"sourceYear":1694,"table":9945,"note":9946,"datasets":9947,"metrics":9951,"families":9952,"methods":9953,"methodIds":9959,"rows":52,"failures":654},"magnusson2007ndt3d-text-sec-5-2-1","Text Sec. 5.2.1","JUNCTION pair (Optab prototype scanner, both scans from the same pose, ground truth = zero motion); 100 runs per setting from start poses on a sphere…",[9948,9949,9950],"JUNCTION","JUNCTION and TUNNEL","TUNNEL",[23,1933],[23,1935],[9954,9955,9956,9957,9958],"3D-NDT (baseline, fixed 1 m cells)","3D-NDT, additive octree subdivision","3D-NDT, iterative subdivision; additive subdivision with infinite outer bounds","ICP (baseline)","ICP vs 3D-NDT",[478,9932],{"slug":9961,"sourceId":9932,"sourceLabel":9933,"sourceYear":1694,"table":9962,"note":9963,"datasets":9964,"metrics":9965,"families":9966,"methods":9967,"methodIds":9973,"rows":654,"failures":63},"magnusson2007ndt3d-text-sec-5-2-2","Text Sec. 5.2.2","KVARNTORP-LOOP scans 17-66 (50 consecutive pairs), SICK LMS 200 on Tjorven; 8000 random data-scan samples (about 8%), all model points; initial poses…",[9937],[23,1933],[23,1935],[9968,9969,9970,9971,9972],"3D-NDT iterative vs single-resolution variants","3D-NDT vs ICP","3D-NDT, iterative subdivision with infinite outer bounds","3D-NDT, octree subdivision","ICP (point-to-point)",[478,9932],{"slug":9975,"sourceId":9976,"sourceLabel":9977,"sourceYear":1157,"table":9978,"note":9979,"datasets":9980,"metrics":9982,"families":9983,"methods":9984,"methodIds":9987,"rows":120,"failures":30},"magnusson2009icpndt-text-fig-7-caption","magnusson2009icpndt","Magnusson et al., 2009","Text Fig. 7 caption","data set A (scans 32 and 33, slightly curved tunnel, 8000 samples each); 441 start poses offset in the horizontal plane (translation offsets, each tri…",[9981],"Kvarntorp data set A",[1933],[1935],[522,9985,9986],"NDT (iterative, 2, 1, 0.5 m cells)","NDT with trilinear interpolation",[478,9932,9976],{"slug":9989,"sourceId":9976,"sourceLabel":9977,"sourceYear":1157,"table":9990,"note":9979,"datasets":9991,"metrics":9992,"families":9993,"methods":9994,"methodIds":9995,"rows":274,"failures":30},"magnusson2009icpndt-text-fig-8-caption","Text Fig. 8 caption",[9981],[1933],[1935],[522,9985,9986],[478,9932,9976],{"slug":9997,"sourceId":9976,"sourceLabel":9977,"sourceYear":1157,"table":9998,"note":9999,"datasets":10000,"metrics":10002,"families":10003,"methods":10004,"methodIds":10006,"rows":154,"failures":154},"magnusson2009icpndt-text-sec-ii-c","Text Sec. II-C","execution time of NDT with trilinear interpolation relative to NDT without it (up to eight PDFs evaluated per point)",[10001],"Kvarntorp data (experiments of this paper)",[23],[23],[10005],"NDT with trilinear interpolation vs NDT",[9976],{"slug":10008,"sourceId":9976,"sourceLabel":9977,"sourceYear":1157,"table":10009,"note":10010,"datasets":10011,"metrics":10013,"families":10014,"methods":10015,"methodIds":10016,"rows":274,"failures":30},"magnusson2009icpndt-text-sec-iv-d-2","Text Sec. IV-D-2","data set B incremental registration; number of scans whose odometry initial pose had to be manually altered to reach a usable result",[10012],"Kvarntorp data set B",[23],[23],[522,9985,9986],[478,9932,9976],{"slug":10018,"sourceId":9976,"sourceLabel":9977,"sourceYear":1157,"table":10019,"note":10020,"datasets":10021,"metrics":10022,"families":10023,"methods":10024,"methodIds":10025,"rows":274,"failures":274},"magnusson2009icpndt-text-sec-iv-d-3","Text Sec. IV-D-3","data set B: 55 scans around a loop of about 150 m, 8000 samples per scan, incremental pairwise registration from odometry; accumulated translation err…",[10012],[1551],[1553],[522,9985,9986],[478,9932,9976],{"slug":10027,"sourceId":10028,"sourceLabel":10029,"sourceYear":681,"table":10030,"note":10031,"datasets":10032,"metrics":10034,"families":10035,"methods":10036,"methodIds":10042,"rows":578,"failures":30},"magnusson2015beyondpoints-fig-3-execution-time-table","magnusson2015beyondpoints","Magnusson et al., 2015","Fig. 3 (execution-time table)","execution time per registration over all six ETH data sets, including pre-processing and excluding file loading; single-threaded; different CPUs per m…",[10033],"ETH Challenging Laser Registration (six data sets)",[38],[40],[10037,10038,10039,10040,10041],"D2D-NDT (perception_oru)","MUMC DC-OFF","MUMC DC-ON","P2D-NDT (perception_oru)","Plane ICP (libpointmatcher point-to-plane baseline)",[1944,9932,10043],"stoyanov2012d2dndt",{"slug":10045,"sourceId":10028,"sourceLabel":10029,"sourceYear":681,"table":1452,"note":10046,"datasets":10047,"metrics":10051,"families":10052,"methods":10053,"methodIds":10054,"rows":416,"failures":154},"magnusson2015beyondpoints-text-sec-v-a","MUMC global registration without initial guess on the 35 unique scan pairs per data set; DC-ON declares a registration failed when translation is not…",[10048,10049,10050],"Gazebo (winter)","Stairs","Wood (summer)",[23,1933],[23,1935],[10038,10039,10040],[9932],{"slug":10056,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10059,"note":10060,"datasets":10061,"metrics":10065,"families":10066,"methods":10067,"methodIds":10069,"rows":274,"failures":30},"magnusson2009thesis-table-8-1","magnusson2009thesis","Magnusson, 2009","Table 8.1","loop detection over all scan pairs; maximum recall with less than 1% false positives; ground truth = scan pairs closer than tr (Mission-4-1 also withi…",[10062,10063,10064],"AASS-Loop (60 omnidirectional scans, 111 m)","Hannover-2 (922 omnidirectional scans, about 1.24 km)","Mission-4-1 (131 scans, 180 deg field of view, about 370 m)",[23],[23],[10068],"NDT surface-shape histograms (loop detection)",[10057],{"slug":10071,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10072,"note":10073,"datasets":10074,"metrics":10078,"families":10079,"methods":10080,"methodIds":10081,"rows":224,"failures":30},"magnusson2009thesis-table-8-2","Table 8.2","SLAM-scenario loop detection: each scan matched to its most similar scan more than 30 steps away; true positive if the scan is manually labelled revis…",[10075,10076,10077],"AASS-Loop (23 revisited, 37 non-revisited scans)","Hannover-2 (428 revisited, 494 non-revisited scans)","Mission-4-1 (35 revisited, 95 non-revisited scans)",[23],[23],[10068],[10057],{"slug":10083,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10084,"note":10085,"datasets":10086,"metrics":10090,"families":10091,"methods":10092,"methodIds":10093,"rows":274,"failures":30},"magnusson2009thesis-table-8-3","Table 8.3","average time to create one surface-shape histogram; C++ on a laptop with a 1.6 GHz Intel Celeron and 2 GiB RAM",[10087,10088,10089],"AASS-Loop (112 000 points per scan, 2.4 histograms per scan)","Hannover-2 (15 000 points per scan, 3.2 histograms per scan)","Mission-4-1 (70 000 points per scan, 2.8 histograms per scan)",[38],[40],[10068],[10057],{"slug":10095,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10096,"note":10097,"datasets":10098,"metrics":10100,"families":10101,"methods":10102,"methodIds":10105,"rows":63,"failures":30},"magnusson2009thesis-text-sec-6-4-1-discretisation-methods","Text Sec. 6.4.1 (discretisation methods)","Straight scan pair (featureless Kvarntorp tunnel section), baseline offsets 1 m and 0.2 rad, 100 start poses; fixed 2 m cells vs octree discretisation",[10099],"Straight (Kvarntorp-Loop scans 51 and 52)",[1933],[1935],[10103,10104],"NDT with fixed 2 m cells","NDT with octree discretisation",[10057],{"slug":10107,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10108,"note":10109,"datasets":10110,"metrics":10112,"families":10113,"methods":10114,"methodIds":10116,"rows":154,"failures":154},"magnusson2009thesis-text-sec-6-4-1-sample-ratio","Text Sec. 6.4.1 (sample ratio)","baseline 3D-NDT with 0.5% spatially distributed samples of the current scan (all reference points), 100 start poses",[10111],"Crossing",[1933],[1935],[10115],"NDT (baseline)",[10057],{"slug":10118,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10119,"note":10120,"datasets":10121,"metrics":10123,"families":10124,"methods":10125,"methodIds":10127,"rows":356,"failures":30},"magnusson2009thesis-text-sec-6-4-2-crossing","Text Sec. 6.4.2 (Crossing)","Crossing scan pair (Kvarntorp tunnel junction, robot not moved, practically 100% overlap); batches of 100 start poses with fixed offset magnitude; suc…",[10122],"Crossing (Kvarntorp-Loop scans 36 and 38)",[1933],[1935],[10126,10115],"ICP (thesis implementation, point-to-point, fixed 0.5 m outlier threshold)",[478,10057],{"slug":10129,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10130,"note":10131,"datasets":10132,"metrics":10134,"families":10135,"methods":10136,"methodIds":10139,"rows":52,"failures":30},"magnusson2009thesis-text-sec-6-4-2-figs-6-20-6-21-captions","Text Sec. 6.4.2 (Figs. 6.20-6.21 captions)","collaborative comparison: one slightly curved Kvarntorp tunnel scan pair (8 000 subsampled points each), 441 start poses with horizontal-plane transla…",[10133],"Kvarntorp tunnel scan pair (collaborative comparison)",[1933],[1935],[10137,10138,9986],"ICP (University of Osnabrück implementation, parameters selected by that group)","NDT (baseline: iterative discretisation with linked cells)",[478,10057],{"slug":10141,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10142,"note":10143,"datasets":10144,"metrics":10147,"families":10148,"methods":10149,"methodIds":10150,"rows":120,"failures":30},"magnusson2009thesis-text-sec-6-4-3-figs-6-27-6-28-captions","Text Sec. 6.4.3 (Figs. 6.27-6.28 captions)","stop-and-scan sequences in the Kvarntorp mine registered pairwise from odometry initial poses; success = within 0.20 m and 0.05 rad of manually determ…",[10145,10146],"Kvarntorp-Loop (Tjorven, 48 scans)","Mission-4 (Kurt3D, 55 scans, closed loop)",[23,1933],[23,1935],[9957,10115,9986],[478,10057],{"slug":10152,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10153,"note":10154,"datasets":10155,"metrics":10157,"families":10158,"methods":10159,"methodIds":10161,"rows":63,"failures":30},"magnusson2009thesis-text-sec-8-2-1","Text Sec. 8.2.1","AASS-Loop: pairwise 3D-NDT registration from odometry initial poses, used as ground truth for loop detection; accumulated pose error between scan 1 an…",[10156],"AASS-Loop (60 scans, 111 m)",[1551],[1553],[10160],"3D-NDT pairwise registration",[10057],{"slug":10163,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10164,"note":10165,"datasets":10166,"metrics":10168,"families":10169,"methods":10170,"methodIds":10171,"rows":154,"failures":154},"magnusson2009thesis-text-sec-8-2-5","Text Sec. 8.2.5","AASS-Loop: 144 histograms, 20 736 similarity measures computed in 0.14 s in total",[10167],"AASS-Loop",[38],[40],[10068],[10057],{"slug":10173,"sourceId":10057,"sourceLabel":10058,"sourceYear":1157,"table":10174,"note":10175,"datasets":10176,"metrics":10178,"families":10179,"methods":10180,"methodIds":10182,"rows":63,"failures":30},"magnusson2009thesis-text-sec-9-3-2","Text Sec. 9.3.2","time to label one subsampled muck-pile scan (10 000 to 22 000 points) with the surface-shape classification",[10177],"Kemi mine muck piles (locations A to D)",[38],[40],[10181],"NDT-inspired surface-shape classification (boulder detection)",[10057],{"slug":10184,"sourceId":10185,"sourceLabel":10186,"sourceYear":345,"table":2713,"note":10187,"datasets":10188,"metrics":10190,"families":10191,"methods":10192,"methodIds":10196,"rows":416,"failures":30},"makkonen2017zebshaft-text-sec-4","makkonen2017zebshaft","Makkonen et al., 2017","Cloud-to-cloud distance between time-classified down and up passes in their overlap; internal consistency only, no external reference.",[10189],"Pyhasalmi Mine, Timo's Shaft",[3417,23],[78,23],[10193,10194,10195],"ZEB1","ZEB1 SLAM-generated point cloud (GeoSLAM processing)","ZEB1 cloud after cable-based semi-manual correction",[1180],{"slug":10198,"sourceId":10199,"sourceLabel":10200,"sourceYear":68,"table":91,"note":10201,"datasets":10202,"metrics":10203,"families":10204,"methods":10205,"methodIds":10208,"rows":396,"failures":30},"rkolio2026-table-i","rkolio2026","Malladi et al., 2026","Oxford Spires backpack (Hesai QT64); ground truth by registering undistorted scans to a TLS map; averages over all sequences of each scene; odometry w…",[72],[329,330],[25,332],[1436,1437,571,10206,10207],"Ours (RKO-LIO)","VILENS-SLAM (SLAM reference, results from Tao et al.)",[1392,321,577,10199],{"slug":10210,"sourceId":10199,"sourceLabel":10200,"sourceYear":68,"table":325,"note":10211,"datasets":10212,"metrics":10214,"families":10215,"methods":10216,"methodIds":10219,"rows":598,"failures":154},"rkolio2026-table-ii","Own car sequences (OS1-128 with built-in InvenSense IMU; Forest 20 km, Rural 52 km); reference by offline LiDAR bundle adjustment with RTK-GPS; ATE on…",[10213],"own car dataset",[329],[25],[1436,1437,571,10206,10217,10218],"Ours, no-AR (ablation)","Ours, no-AVG, no-AR (ablation)",[1392,321,577,10199],{"slug":10221,"sourceId":10199,"sourceLabel":10200,"sourceYear":68,"table":279,"note":10222,"datasets":10223,"metrics":10225,"families":10226,"methods":10227,"methodIds":10229,"rows":598,"failures":30},"rkolio2026-table-iii","Leg-KILO dataset, Unitree Go1 quadruped with VLP-16 and 500 Hz IMU; Indoor ground truth from a prior map, others from offline optimization with loop c…",[10224],"Leg-KILO dataset",[329,330],[25,332],[1436,1437,571,10228,10206],"Leg-KILO",[1392,321,577,10199],{"slug":10231,"sourceId":10199,"sourceLabel":10200,"sourceYear":68,"table":731,"note":10232,"datasets":10233,"metrics":10235,"families":10236,"methods":10237,"methodIds":10238,"rows":608,"failures":578},"rkolio2026-table-iv","DigiForests backpack sessions (Hesai XT32, QT32, QT64; LiDAR inclined 45 deg in the first season); reference trajectories from offline VILENS with GNS…",[10234],"DigiForests",[329,330],[25,332],[1436,1437,571,10206],[1392,321,577,10199],{"slug":10240,"sourceId":10199,"sourceLabel":10200,"sourceYear":68,"table":10241,"note":10242,"datasets":10243,"metrics":10245,"families":10246,"methods":10247,"methodIds":10248,"rows":63,"failures":30},"rkolio2026-text-sec-iv-b-drz-living-lab","Text Sec. IV-B (DRZ Living Lab)","DRZ Living Lab drone dataset (Ouster OS-0 on DJI M210 v2), 9 sequences with motion-capture ground truth; averages stated in text",[10244],"DRZ Living Lab",[329,330],[25,332],[10206],[10199],{"slug":10250,"sourceId":3183,"sourceLabel":10251,"sourceYear":2267,"table":10252,"note":10253,"datasets":10254,"metrics":10255,"families":10256,"methods":10257,"methodIds":10258,"rows":356,"failures":30},"monogs2024-supp-tables-9-10","Matsuki et al., 2024","Supp. Tables 9-10","End-to-end system FPS = processed frames divided by total time (multi-process unless marked sp); rendering FPS is reported separately (769 FPS).",[3223,2872],[148],[40],[5866],[3183],{"slug":10260,"sourceId":3183,"sourceLabel":10251,"sourceYear":2267,"table":69,"note":10261,"datasets":10262,"metrics":10263,"families":10264,"methods":10265,"methodIds":10283,"rows":10284,"failures":30},"monogs2024-table-1","Keyframe ATE RMSE (cm) on TUM RGB-D, average of three runs; monocular results use scale alignment, RGB-D results rigid alignment without scale (Supp.…",[2872],[568],[25],[10266,10267,10268,10269,10270,10271,10272,10273,10274,10275,10276,10277,10278,10279,10280,10281,10282],"BAD-SLAM [31] (RGB-D, LC)","Co-SLAM [41] (RGB-D, no LC)","DI-Fusion [8] (RGB-D, no LC)","DROID-SLAM [38] (mono, LC)","DROID-VO [38] (mono, no LC)","DSO [5] (mono, no LC)","DepthCov-VO [4] (mono, no LC)","ESLAM [9] (RGB-D, no LC)","Kintinous [42] (RGB-D, LC)","MonoGS (Ours, RGB-D)","MonoGS (Ours, mono)","NICE-SLAM [48] (RGB-D, no LC)","ORB-SLAM2 [21] (RGB-D, LC)","ORB-SLAM2 [21] (mono, LC)","Point-SLAM [29] (RGB-D, no LC)","Vox-Fusion [45] (RGB-D, no LC)","iMAP [35] (RGB-D, no LC)",[9325,3181,6178,1507,3182,6378,2448,3183,3184,1511,3185],68,{"slug":10286,"sourceId":3183,"sourceLabel":10251,"sourceYear":2267,"table":10287,"note":10288,"datasets":10289,"metrics":10290,"families":10291,"methods":10292,"methodIds":10298,"rows":3819,"failures":416},"monogs2024-table-14","Table 14","ATE RMSE (m) on EuRoC Machine Hall with depth from stereo (Supp. 9.5); classical baseline values from the ORB-SLAM3 paper [1]; Point-SLAM failed on al…",[1482],[568],[25],[10293,10294,10295,10296,10297],"MonoGS (Ours, stereo depth)","ORB-SLAM3 [1]","Point-SLAM [29]","SVO [6]","Vins-Fusion [28]",[3183,763,3185,1512,1513],{"slug":10300,"sourceId":3183,"sourceLabel":10251,"sourceYear":2267,"table":108,"note":10301,"datasets":10302,"metrics":10303,"families":10304,"methods":10305,"methodIds":10311,"rows":322,"failures":30},"monogs2024-table-2","Keyframe ATE RMSE (cm) on Replica, RGB-D only (Replica has purely rotational motions); baselines from Point-SLAM; Ours = multi-process real-time imple…",[3223],[568],[25],[10306,10307,10308,3174,10295,10309,10310],"ESLAM [9]","MonoGS (Ours sp, single-process)","MonoGS (Ours, multi-process)","Vox-Fusion [45]","iMAP [35]",[3182,6378,3183,3184,3185],{"slug":10313,"sourceId":6100,"sourceLabel":10314,"sourceYear":213,"table":69,"note":10315,"datasets":10316,"metrics":10320,"families":10321,"methods":10322,"methodIds":10327,"rows":721,"failures":274},"nerf2020-table-1","Mildenhall et al., 2020","Novel-view synthesis quality: PSNR (dB) and SSIM higher is better, LPIPS lower is better; Diffuse Synthetic 360 (DeepVoxels, 4 objects, 479 input view…",[10317,10318,10319],"Diffuse Synthetic 360 (DeepVoxels)","Real Forward-Facing","Realistic Synthetic 360",[23],[23],[10323,10324,10325,10326],"LLFF [27]","NV [23]","NeRF (Ours)","SRN [41]",[6100],{"slug":10329,"sourceId":6100,"sourceLabel":10314,"sourceYear":213,"table":244,"note":10330,"datasets":10331,"metrics":10332,"families":10333,"methods":10334,"methodIds":10337,"rows":415,"failures":30},"nerf2020-table-5","Per-scene novel-view synthesis metrics on the Real Forward-Facing dataset (8 scenes captured with a forward-facing handheld cellphone); SRN evaluated…",[10318],[23],[23],[10335,10325,10336],"LLFF [28]","SRN [42]",[6100],{"slug":10339,"sourceId":6100,"sourceLabel":10314,"sourceYear":213,"table":10340,"note":10341,"datasets":10342,"metrics":10345,"families":10346,"methods":10347,"methodIds":10349,"rows":274,"failures":154},"nerf2020-text-sec-5-3-6-3-app-a","Text Sec.5.3, 6.3, App. A","Optimization, rendering and storage cost of NeRF as stated in the text.",[10343,10344],"Realistic Synthetic 360 and Real Forward-Facing","per scene",[219,23,38],[40,23],[10348],"NeRF",[6100],{"slug":10351,"sourceId":10352,"sourceLabel":10353,"sourceYear":107,"table":91,"note":10354,"datasets":10355,"metrics":10356,"families":10357,"methods":10358,"methodIds":10361,"rows":641,"failures":30},"cblox2018-table-i","cblox2018","Millane et al., 2018","ICL-NUIM living room (synthetic, noisy depth); RMSE between mesh vertices (or surfel centres) and the closest ground-truth surface point after alignme…",[2870],[75],[78],[5233,10359,10360],"Ours (C-blox)","Voxblox (GT Poses)",[10352,2915,85],{"slug":10363,"sourceId":10352,"sourceLabel":10353,"sourceYear":107,"table":325,"note":10364,"datasets":10365,"metrics":10367,"families":10368,"methods":10369,"methodIds":10373,"rows":1042,"failures":30},"cblox2018-table-ii","CARLA simulated drives l0 and l1 through two synthetic cities; reference geometry is a voxblox reconstruction with ground-truth poses and 0.25 m voxel…",[10366],"CARLA (simulated)",[75,23],[78,23],[10370,10371,10360,10372],"Ours (subvolume fusion OFF, ablation)","Ours (subvolume fusion ON)","Voxblox (ORB-SLAM Poses)",[10352,85],{"slug":10375,"sourceId":10352,"sourceLabel":10353,"sourceYear":107,"table":3144,"note":10376,"datasets":10377,"metrics":10379,"families":10380,"methods":10381,"methodIds":10384,"rows":63,"failures":30},"cblox2018-text-sec-v-c","Average time to integrate depth data per frame in the industrial MAV environment, original voxblox integrator versus the proposed fast integrator",[10378],"authors' MAV industrial flights",[38],[40],[10382,10383],"C-blox fast integrator","voxblox original integrator",[10352,85],{"slug":10386,"sourceId":10387,"sourceLabel":10388,"sourceYear":2267,"table":91,"note":10389,"datasets":10390,"metrics":10391,"families":10392,"methods":10393,"methodIds":10396,"rows":2119,"failures":224},"millane2024nvblox-table-i","millane2024nvblox","Millane et al., 2024","Component runtimes averaged over 8 Replica and 5 Redwood sequences at 5 cm voxels; ESDF and mesh computed every 4 frames; voxblox does not separate TS…",[2943,3223],[23,38],[40,23],[10394,10395],"nvblox","voxblox",[10387,85],{"slug":10398,"sourceId":10387,"sourceLabel":10388,"sourceYear":2267,"table":325,"note":10399,"datasets":10400,"metrics":10403,"families":10404,"methods":10405,"methodIds":10407,"rows":2252,"failures":30},"millane2024nvblox-table-ii","Incremental ESDF on the Desktop platform; error is the median absolute voxel-wise difference to a voxelized ESDF ground truth computed from reconstruc…",[10401,10402,2943],"Average","Cow and lady",[23],[23],[10406,10394,10395],"Fiesta",[10387,85],{"slug":10409,"sourceId":10387,"sourceLabel":10388,"sourceYear":2267,"table":279,"note":10410,"datasets":10411,"metrics":10413,"families":10414,"methods":10415,"methodIds":10416,"rows":224,"failures":30},"millane2024nvblox-table-iii","Distance query throughput on GPU averaged over several sequences; cor. = spatially correlated query points, uncor. = uncorrelated",[2943,10412],"Sun3D",[23],[23],[10394],[10387],{"slug":10418,"sourceId":10387,"sourceLabel":10388,"sourceYear":2267,"table":10419,"note":10420,"datasets":10421,"metrics":10423,"families":10424,"methods":10425,"methodIds":10426,"rows":63,"failures":30},"millane2024nvblox-text-sec-v-e","Text Sec. V-E","Drone dataset with a 64-beam Ouster OS1, FAST-LIO poses, 25 m integration range; resolution given as 5 cm in the text but 10 cm in the Fig. 7 caption",[10422],"drone LiDAR dataset of Voxgraph [6]",[38],[40],[10394],[10387],{"slug":10428,"sourceId":10429,"sourceLabel":10430,"sourceYear":10431,"table":10432,"note":10433,"datasets":10434,"metrics":10436,"families":10437,"methods":10438,"methodIds":10441,"rows":63,"failures":30},"fastslam2002-text-experimental-results","fastslam2002","Montemerlo et al., 2002",2002,"Text Experimental Results","Physical testbed: Pioneer robot with SICK laser mapping rocks in a Mars-rover research arena; FastSLAM map compared with manually determined landmark…",[10435,4430],"NASA-funded Mars rover test arena",[75,23],[78,23],[10439,10440],"FastSLAM (M = 10 samples)","FastSLAM (M = 100)",[10429],{"slug":10443,"sourceId":10444,"sourceLabel":10445,"sourceYear":538,"table":10446,"note":10447,"datasets":10448,"metrics":10450,"families":10451,"methods":10452,"methodIds":10456,"rows":274,"failures":30},"fastslam2-2003-table-sec-6","fastslam2_2003","Montemerlo et al., 2003","Table Sec. 6","Total time to process the Victoria Park data set on a 1 GHz Pentium PC; data acquisition took 1,550 s",[10449],"Victoria Park (Sydney)",[23],[23],[10453,10454,10455],"EKF","FastSLAM 2.0, M=1 particle","regular FastSLAM, M=50 particles",[10429,10444],{"slug":10458,"sourceId":10444,"sourceLabel":10445,"sourceYear":538,"table":668,"note":10459,"datasets":10460,"metrics":10461,"families":10462,"methods":10463,"methodIds":10467,"rows":274,"failures":30},"fastslam2-2003-text-sec-6","Victoria Park raw odometry path compared with DGPS (evaluation only)",[10449],[23],[23],[10464,10465,10466],"FastSLAM 2.0, M = 1, with feature management","FastSLAM 2.0, M = 1, without feature management","raw odometry",[10444],{"slug":10469,"sourceId":10470,"sourceLabel":10471,"sourceYear":4540,"table":91,"note":10472,"datasets":10473,"metrics":10475,"families":10476,"methods":10477,"methodIds":10487,"rows":102,"failures":30},"velodyneslam2011-table-i","velodyneslam2011","Moosmann & Stiller, 2011","End-point error (Euclidean distance between estimated end position and the end position obtained by ICP of the last scan to the first scan) on two loo…",[10474],"Velodyne SLAM dataset (AnnieWay)",[1551],[1553],[10478,10479,10480,10481,10482,10483,10484,10485,10486],"IMU (integrated navigation system: GPS, wheel speed, inertial)","Setting 1 (mapping no, de-skewing no, adaptation no)","Setting 2 (mapping no, de-skewing no, adaptation yes)","Setting 3 (mapping no, de-skewing yes, adaptation no)","Setting 4 (mapping no, de-skewing yes, adaptation yes)","Setting 5 (mapping yes, de-skewing no, adaptation no)","Setting 6 (mapping yes, de-skewing no, adaptation yes)","Setting 7 (mapping yes, de-skewing yes, adaptation no)","Setting 8 (mapping yes, de-skewing yes, adaptation yes)",[10470],{"slug":10489,"sourceId":10490,"sourceLabel":10491,"sourceYear":698,"table":7757,"note":10492,"datasets":10493,"metrics":10495,"families":10496,"methods":10497,"methodIds":10499,"rows":63,"failures":30},"moura2021bimslam-text-sec-v-a","moura2021bimslam","Moura et al., 2021","Gazebo simulation of an office building ground floor with the BIM-derived .pbstream loaded in a frozen state; robot started at a random indoor locatio…",[10494],"Gazebo simulation (office building IFC model)",[23],[23],[10498],"Cartographer with BIM-derived pose graph",[10490],{"slug":10501,"sourceId":10490,"sourceLabel":10491,"sourceYear":698,"table":10502,"note":10503,"datasets":10504,"metrics":10506,"families":10507,"methods":10508,"methodIds":10509,"rows":274,"failures":154},"moura2021bimslam-text-sec-v-b1","Text Sec.V-B1","Real test on the lower floor of Eurecat's industrial laboratory with a basic IFC model; many non-structural objects (furniture, protection nets)",[10505],"Eurecat real test",[1551,23],[1553,23],[10498],[10490],{"slug":10511,"sourceId":10490,"sourceLabel":10491,"sourceYear":698,"table":10512,"note":10513,"datasets":10514,"metrics":10515,"families":10516,"methods":10517,"methodIds":10518,"rows":154,"failures":154},"moura2021bimslam-text-sec-v-b2","Text Sec.V-B2","New SLAM session map compared with the reference IFC model in CloudCompare; matched portions only",[10505],[23],[23],[10498],[10490],{"slug":10520,"sourceId":1509,"sourceLabel":10521,"sourceYear":1694,"table":1994,"note":10522,"datasets":10523,"metrics":10525,"families":10526,"methods":10527,"methodIds":10528,"rows":274,"failures":154},"mourikis2007msckf-text-sec-iv","Mourikis & Roumeliotis, 2007","Car-mounted camera and IMU in a residential area of Minneapolis, 1598 images (3 Hz) over about 9 min, 3.2 km; no GPS ground truth; final error inferre…",[10524],"Minneapolis residential driving sequence",[1551,148],[40,1553],[4734],[1509],{"slug":10530,"sourceId":1511,"sourceLabel":10531,"sourceYear":345,"table":91,"note":10532,"datasets":10533,"metrics":10534,"families":10535,"methods":10536,"methodIds":10538,"rows":3766,"failures":30},"orbslam2-2017-table-i","Mur-Artal & Tardos, 2017","KITTI odometry training sequences, stereo: average relative translation error trel (%) and rotation error rrel (deg\u002F100 m) per the KITTI metric, and a…",[469],[568,438,439],[25,441],[1493,10537],"Stereo LSD-SLAM",[1511],{"slug":10540,"sourceId":1511,"sourceLabel":10531,"sourceYear":345,"table":325,"note":10541,"datasets":10542,"metrics":10543,"families":10544,"methods":10545,"methodIds":10546,"rows":3323,"failures":52},"orbslam2-2017-table-ii","EuRoC stereo, translation RMSE (m); ORB-SLAM2 median of 5 runs; Stereo LSD-SLAM values only published for three sequences; X = tracking lost",[1482],[568],[25],[1493,10537],[1511],{"slug":10548,"sourceId":1511,"sourceLabel":10531,"sourceYear":345,"table":279,"note":10549,"datasets":10550,"metrics":10551,"families":10552,"methods":10553,"methodIds":10556,"rows":1339,"failures":356},"orbslam2-2017-table-iii","TUM RGB-D translation RMSE (m); ORB-SLAM2 median of 5 runs with the 4% freiburg2 depth scale bias compensated; other values as published by the origin…",[2872],[568],[25],[2907,5233,2909,10554,10555],"ORB-SLAM2 (RGB-D)","RGBD SLAM",[2915,2448,1511],{"slug":10558,"sourceId":1511,"sourceLabel":10531,"sourceYear":345,"table":731,"note":10559,"datasets":10560,"metrics":10561,"families":10562,"methods":10563,"methodIds":10564,"rows":224,"failures":30},"orbslam2-2017-table-iv","Mean time per thread task (ms, mean of the thread total); loop and full-BA values are single measurements because each sequence has one loop; componen…",[1482,469,2872],[23,38],[40,23],[6668],[1511],{"slug":10566,"sourceId":119,"sourceLabel":10567,"sourceYear":681,"table":91,"note":10568,"datasets":10569,"metrics":10571,"families":10572,"methods":10573,"methodIds":10575,"rows":356,"failures":30},"orbslam2015-table-i","Mur-Artal et al., 2015","NewCollege tracking and local mapping thread times (ms); only thread totals transcribed",[10570],"NewCollege",[23,38],[40,23],[10574],"ORB-SLAM",[119],{"slug":10577,"sourceId":119,"sourceLabel":10567,"sourceYear":681,"table":325,"note":10578,"datasets":10579,"metrics":10580,"families":10581,"methods":10582,"methodIds":10583,"rows":120,"failures":30},"orbslam2015-table-ii","NewCollege loop closing: total loop correction time (s) for each of the 6 detected loops (keyframes in map 287 to 4496); detection sub-times not trans…",[10570],[23],[23],[10574],[119],{"slug":10585,"sourceId":119,"sourceLabel":10567,"sourceYear":681,"table":279,"note":10586,"datasets":10587,"metrics":10588,"families":10589,"methods":10590,"methodIds":10592,"rows":10593,"failures":3323},"orbslam2015-table-iii","TUM RGB-D keyframe ATE RMSE (cm), median over 5 executions; ORB-SLAM, PTAM (two manually chosen initial frames) and LSD-SLAM (first 10 keyframes disca…",[2872],[568],[25],[2924,10574,4739,10591],"RGBD-SLAM",[2881,119,3866],71,{"slug":10595,"sourceId":119,"sourceLabel":10567,"sourceYear":681,"table":731,"note":10596,"datasets":10597,"metrics":10598,"families":10599,"methods":10600,"methodIds":10601,"rows":618,"failures":154},"orbslam2015-table-iv","Relocalization: map built from the first 30 s of fr2_xyz (2769 frames to relocalize) or from fr3_sitting_xyz (859 frames of fr3_walking_xyz to relocal…",[2872],[23,1933],[23,1935],[10574,4739],[119,3866],{"slug":10603,"sourceId":119,"sourceLabel":10567,"sourceYear":681,"table":818,"note":10604,"datasets":10605,"metrics":10606,"families":10607,"methods":10608,"methodIds":10610,"rows":2388,"failures":274},"orbslam2015-table-v","KITTI odometry, keyframe trajectory RMSE (m), median of 5 executions, Sim(3) alignment; right columns after 20 LM iterations of full BA at the end of…",[469],[568,23],[25,23],[10574,10609],"ORB-SLAM + Global BA (20 its.)",[119],{"slug":10612,"sourceId":119,"sourceLabel":10567,"sourceYear":681,"table":827,"note":10613,"datasets":10614,"metrics":10615,"families":10616,"methods":10617,"methodIds":10626,"rows":641,"failures":30},"orbslam2015-table-vi","Loop-closing strategies on KITTI 09 in the same execution: none, full BA (LM iterations in brackets), Essential Graph pose-graph optimisation (theta_m…",[469],[568,23],[25,23],[10618,10619,10620,10621,10622,10623,10624,10625],"ORB-SLAM, BA (100)","ORB-SLAM, BA (20)","ORB-SLAM, EG (100)","ORB-SLAM, EG (100) + BA (20)","ORB-SLAM, EG (15)","ORB-SLAM, EG (200)","ORB-SLAM, EG (50)","ORB-SLAM, no loop closing",[119],{"slug":10628,"sourceId":9898,"sourceLabel":10629,"sourceYear":562,"table":10630,"note":10631,"datasets":10632,"metrics":10634,"families":10635,"methods":10636,"methodIds":10639,"rows":224,"failures":30},"mast3rslam2025-fig-5-table","Murai et al., 2025","Fig. 5 table","ETH3D-SLAM train sequences, monocular, no frame subsampling; mean ATE (m) over completed sequences and area under the success curve up to the ATE thre…",[10633],"ETH3D-SLAM",[22,23],[25,23],[9228,9229,10637,6667,10638,892],"DPVO","MASt3R-SLAM (Ours)",[6178,9898,763],{"slug":10641,"sourceId":9898,"sourceLabel":10629,"sourceYear":562,"table":69,"note":10642,"datasets":10643,"metrics":10644,"families":10645,"methods":10646,"methodIds":10657,"rows":1042,"failures":63},"mast3rslam2025-table-1","ATE RMSE (m) on TUM RGB-D (monocular), scaled trajectory alignment; x = failure; '-' = average not computed. Uncalibrated DROID-SLAM* uses GeoCalib in…",[2872],[568],[25],[10647,10648,10649,10650,10651,10652,10653,10654,10655,10656],"DPV-SLAM [22]","DPV-SLAM++ [22]","DROID-SLAM [45]","DROID-SLAM* (uncalibrated, GeoCalib)","DeepFactors [6]","DeepV2D [42]","GO-SLAM [54]","MASt3R-SLAM (Ours*, uncalibrated)","MASt3R-SLAM (Ours, calibrated)","ORB-SLAM3 [4]",[6178,9898,763],{"slug":10659,"sourceId":9898,"sourceLabel":10629,"sourceYear":562,"table":108,"note":10660,"datasets":10661,"metrics":10662,"families":10663,"methods":10664,"methodIds":10665,"rows":2882,"failures":30},"mast3rslam2025-table-2","ATE RMSE (m) on 7-Scenes, monocular RGB, scaled trajectory alignment; sequences follow NICER-SLAM; NICER-SLAM values reported from NICER-SLAM; frames…",[9555],[568],[25],[6667,10654,10655,9894],[6178,9898],{"slug":10667,"sourceId":9898,"sourceLabel":10629,"sourceYear":562,"table":17,"note":10668,"datasets":10669,"metrics":10670,"families":10671,"methods":10672,"methodIds":10673,"rows":2882,"failures":63},"mast3rslam2025-table-3","Reconstruction evaluation (m). Accuracy and completion are RMSE of nearest-neighbour distances with a 0.5 m maximum distance, Chamfer is their average…",[9555,1482],[568,2343,75,76],[25,78],[6667,10654,10655,9916,9917],[6178,9898],{"slug":10675,"sourceId":9898,"sourceLabel":10629,"sourceYear":562,"table":633,"note":10676,"datasets":10677,"metrics":10679,"families":10680,"methods":10681,"methodIds":10682,"rows":274,"failures":30},"mast3rslam2025-table-8","Average component runtimes of the single-threaded system over TUM fr1\u002Froom, 7-Scenes chess and EuRoC MH01 (Average row).",[10678],"TUM RGB-D, 7-Scenes, EuRoC",[148,23,38],[40,23],[9892],[9898],{"slug":10684,"sourceId":9898,"sourceLabel":10629,"sourceYear":562,"table":644,"note":10685,"datasets":10686,"metrics":10687,"families":10688,"methods":10689,"methodIds":10690,"rows":52,"failures":154},"mast3rslam2025-table-9","ATE RMSE (m) on EuRoC, monocular, scaled alignment; truncated to the 11-sequence average; uncalibrated run used undistorted images; ORB-SLAM failed (x…",[1482],[568],[25],[10647,10648,10649,10651,10652,10653,10654,10655,10574],[6178,9898],{"slug":10692,"sourceId":10693,"sourceLabel":10694,"sourceYear":1234,"table":325,"note":10695,"datasets":10696,"metrics":10699,"families":10700,"methods":10701,"methodIds":10724,"rows":3766,"failures":30},"museth2013vdb-table-ii","museth2013vdb","Museth, 2013","Enright sphere and 8x variant at effective resolution 4096^3 (51,033,829 and 263,418,462 active voxels, 10-voxel band); relative time per random looku…",[10697,10698],"Enright 8x variant","Enright test",[219,23],[40,23],[10702,10703,10704,10705,10706,10707,10708,10709,10710,10711,10712,10713,10714,10715,10716,10717,10718,10719,10720,10721,10722,10723],"DT-Grid","Octree# (depth 13)","Octree* (depth 18)","[10,3]","[3,3,3,3]","[4,4,4,4]","[5,5,5,5]","[6,4,3]","[6,5,4,2]","[6,5,4,3]","[6,5,4,3] (row 7)","[6,5,4,4]","[6,5,4,5]","[6,6,6,6]","[7,6,5,4,3]","[F3DSF,3]","[F3DSF,4]","[F3DSF,5]","[Hash,4,3,2]","[Hash,5,4,3]","[Map,4,3,2]","[Map,5,4,3]",[10693],{"slug":10726,"sourceId":10693,"sourceLabel":10694,"sourceYear":1234,"table":279,"note":10727,"datasets":10728,"metrics":10729,"families":10730,"methods":10731,"methodIds":10733,"rows":102,"failures":30},"museth2013vdb-table-iii","CPU seconds for one Enright time step (advection, three renormalizations, narrow-band rebuild; TVD-RK3, WENO5, band 10); [6,5,4,3]|| is multithreaded…",[10698],[23],[23],[10702,10711,10732],"[6,5,4,3] multithreaded",[10693],{"slug":10735,"sourceId":10693,"sourceLabel":10694,"sourceYear":1234,"table":731,"note":10736,"datasets":10737,"metrics":10738,"families":10739,"methods":10740,"methodIds":10745,"rows":721,"failures":154},"museth2013vdb-table-iv","Memory footprint (MB) of the standard Enright dataset; VDB given in-core and partially out-of-core; no compression or quantization in-core; F3DSF bloc…",[10698],[219],[40],[10702,10718,10741,10742,10743,10744],"[Hash,4,3,2] in-core","[Hash,4,3,2] out-of-core","[Hash,5,4,3] in-core","[Hash,5,4,3] out-of-core",[10693],{"slug":10747,"sourceId":10693,"sourceLabel":10694,"sourceYear":1234,"table":838,"note":10748,"datasets":10749,"metrics":10750,"families":10751,"methods":10752,"methodIds":10756,"rows":102,"failures":154},"museth2013vdb-table-vii","CPU seconds for dilating the Enright narrow band by one voxel; Optimal = fastest available algorithm, Brute-Force = VDB random-access insertion",[10698],[23],[23],[10753,10754,10755],"DT-Grid Optimal","[6,5,4,3] Brute-Force","[6,5,4,3] Optimal",[10693],{"slug":10758,"sourceId":10759,"sourceLabel":10760,"sourceYear":2267,"table":91,"note":10761,"datasets":10762,"metrics":10764,"families":10765,"methods":10766,"methodIds":10778,"rows":2388,"failures":356},"nair2024hilti2023-table-i","nair2024hilti2023","Nair et al., 2024","Single-session leaderboard top entries aggregated over all single-session sequences (Sites 1-3, handheld and robot); GCP-based sparse ground truth fro…",[10763],"Hilti SLAM Challenge 2023",[568,23],[25,23],[10767,10768,10769,10770,10771,10772,10773,10774,10775,10776,10777],"Lidar-based: 1. KAIST URL, based on [20]-[23] (AdaLIO frontend, Quatro loop detection, G-ICP, GTSAM factor graph)","Lidar-based: 2. HKU-MaRS, based on [11], [24]-[26] (FAST-LIO2, VoxelMap, BALM2, HBA)","Lidar-based: 3. Innopolis Univ., Strelka (not published)","Lidar-based: 4. SNU RPM Lab, based on [22], [23], [27] (G-ICP, GTSAM, Point-LIO)","Lidar-based: 5. Tsinghua Univ., FT-LVIO [28]","Lidar-based: 6. ANYbotics, FrankenPharos (not published)","Lidar-based: 7. B. Kim et al., based on [11], [21], [23], [29] (FAST-LIO2, Quatro, GTSAM, VGICP)","Lidar-based: 8. NTU IOT, based on [11], [25], [30] (FAST-LIO2, BALM2, CT-ICP)","Vision-based: 1. Tencent XR, MAVIS [31]","Vision-based: 2. KAIST URL, Stereo UV SLAM [32]","Vision-based: 3. ASL ETHZ, based on [33], [34] (OKVIS, maplab 2.0)",[],{"slug":10780,"sourceId":10759,"sourceLabel":10760,"sourceYear":2267,"table":325,"note":10781,"datasets":10782,"metrics":10783,"families":10784,"methods":10785,"methodIds":10789,"rows":224,"failures":30},"nair2024hilti2023-table-ii","Multi-session leaderboards: all trajectories of a location submitted in one common frame and scored as one trajectory; GCP-based sparse ground truth.",[10763],[568,23],[25,23],[10786,10787,10775,10788],"Lidar-based: 1. ETHZ ASL, based on [11], [34] (FAST-LIO2, maplab 2.0)","Lidar-based: 2. Innopolis Univ, Strelka (not published)","Vision-based: 2. ETHZ ASL, based on [33], [34] (OKVIS, maplab 2.0)",[],{"slug":10791,"sourceId":10759,"sourceLabel":10760,"sourceYear":2267,"table":10792,"note":10793,"datasets":10794,"metrics":10796,"families":10797,"methods":10798,"methodIds":10800,"rows":356,"failures":30},"nair2024hilti2023-text-sec-iii-d","Text Sec. III-D","Relative accuracy of the proposed LiDAR-intensity GCP detector on a 6x6 grid pre-surveyed with Trimble X7; Kabsch-aligned; 3-DoF Euclidean error; abso…",[10795],"Pre-surveyed GCP test grid",[23],[23],[10799],"Proposed LiDAR GCP detector (Algorithm 1) on Robosense Bpearl",[],{"slug":10802,"sourceId":10759,"sourceLabel":10760,"sourceYear":2267,"table":5442,"note":10803,"datasets":10804,"metrics":10805,"families":10806,"methods":10807,"methodIds":10809,"rows":274,"failures":30},"nair2024hilti2023-text-sec-iv-d","Share of registered Trimble X7 scans with registration uncertainty of 3 mm or less, per site; GCPs were not extracted from higher-uncertainty leaf sca…",[10763],[23],[23],[10808],"Trimble X7 TLS registration (field software auto registration)",[],{"slug":10811,"sourceId":10812,"sourceLabel":10813,"sourceYear":16,"table":69,"note":10814,"datasets":10815,"metrics":10816,"families":10817,"methods":10818,"methodIds":10821,"rows":2388,"failures":30},"mc2slam2019-table-1","mc2slam2019","Neuhaus et al., 2019","KITTI odometry training set; translation drift per metre (%) of LOAM [24], IMLS [6] and MC2SLAM; LiDAR only (no IMU in KITTI odometry); scans re-disto…",[469],[438],[441],[10819,10820,81],"IMLS [6]","LOAM [24]",[450,10812],{"slug":10823,"sourceId":10812,"sourceLabel":10813,"sourceYear":16,"table":108,"note":10824,"datasets":10825,"metrics":10827,"families":10828,"methods":10829,"methodIds":10833,"rows":224,"failures":30},"mc2slam2019-table-2","Own HDL-32 datasets; translation drift per metre (%) computed at manually registered loop checkpoints and averaged over 10 runs with different random…",[10826],"MC2SLAM own datasets",[23],[23],[10830,10831,10832],"Local Map no, IMU yes","Local Map yes, IMU no","Local Map yes, IMU yes",[10812],{"slug":10835,"sourceId":10812,"sourceLabel":10813,"sourceYear":16,"table":952,"note":10836,"datasets":10837,"metrics":10838,"families":10839,"methods":10840,"methodIds":10842,"rows":154,"failures":30},"mc2slam2019-text-sec-5","KITTI online test set result as listed on the KITTI website (entry MC2SLAM), shared 4th\u002F5th place among laser-based methods at the time of writing",[469],[438],[441],[10841],"MC2SLAM",[10812],{"slug":10844,"sourceId":10812,"sourceLabel":10813,"sourceYear":16,"table":10845,"note":10846,"datasets":10847,"metrics":10848,"families":10849,"methods":10850,"methodIds":10851,"rows":356,"failures":30},"mc2slam2019-text-sec-5-runtime","Text Sec.5 Runtime","Runtime of MC2SLAM components on campus run 1: mean over 1000 frames of campus run 1; 500 query points",[10826],[38],[40],[10841],[10812],{"slug":10853,"sourceId":4750,"sourceLabel":10854,"sourceYear":4540,"table":6610,"note":10855,"datasets":10856,"metrics":10858,"families":10859,"methods":10860,"methodIds":10862,"rows":154,"failures":30},"kinectfusion2011-text-sec-1","Newcombe et al., 2011b","Tracking rate stated in text; tracking and mapping run at the Kinect frame rate in constant time for a given voxel resolution",[10857],"live Kinect input",[148],[40],[10861],"KinectFusion",[4750],{"slug":10864,"sourceId":4750,"sourceLabel":10854,"sourceYear":4540,"table":10865,"note":10866,"datasets":10867,"metrics":10868,"families":10869,"methods":10870,"methodIds":10871,"rows":154,"failures":30},"kinectfusion2011-text-sec-3-3","Text Sec. 3.3","TSDF integration throughput stated in text; operation is memory bound",[3980],[38],[40],[10861],[4750],{"slug":10873,"sourceId":10874,"sourceLabel":10875,"sourceYear":306,"table":33,"note":10876,"datasets":10877,"metrics":10878,"families":10879,"methods":10880,"methodIds":10883,"rows":721,"failures":30},"nguyen2022ntuviral-table-4","nguyen2022ntuviral","Nguyen et al., 2022a","ATE of SLAM frameworks without ground-truth time shift on the nine original sequences; estimate converted to the prism point, resampled and aligned wi…",[1636],[329],[25],[334,10881,2382,10882],"MLOAM","VIRAL-SLAM",[338,1513],{"slug":10885,"sourceId":10886,"sourceLabel":10887,"sourceYear":306,"table":325,"note":10888,"datasets":10889,"metrics":10891,"families":10892,"methods":10893,"methodIds":10898,"rows":598,"failures":30},"viralfusion2022-table-ii","viralfusion2022","Nguyen et al., 2022b","EuRoC Vicon Room V1\u002FV2 with UWB simulated from Vicon (4 anchors, 80 Hz, 0.05 m noise); VINS = VINS-Fusion stereo-inertial, BA = loop closure and globa…",[10890],"EuRoC MAV (simulated UWB)",[568],[25],[10894,81,10895,10896,10897],"ORB-SLAM 3","Ours (w\u002Fo OSL)","VINS (w\u002Fo BA)","VINS (with BA)",[763,1513,10886],{"slug":10900,"sourceId":10886,"sourceLabel":10887,"sourceYear":306,"table":279,"note":10888,"datasets":10901,"metrics":10902,"families":10903,"methods":10904,"methodIds":10905,"rows":598,"failures":30},"viralfusion2022-table-iii",[10890],[23],[23],[10894,81,10895,10896,10897],[763,1513,10886],{"slug":10907,"sourceId":10886,"sourceLabel":10887,"sourceYear":306,"table":731,"note":10908,"datasets":10909,"metrics":10911,"families":10912,"methods":10913,"methodIds":10918,"rows":322,"failures":120},"viralfusion2022-table-iv","AirSim Building_99 indoor foyer simulation: 10 Hz stereo, two 10 Hz LiDARs (horizontal and vertical, A-LOAM), 400 Hz IMU, two UWB nodes with two anten…",[10910],"AirSim Building_99 (authors' simulation)",[568,23],[25,23],[10914,892,10896,10897,10915,10916,10917],"Horizontal LOAM","VIRAL (w\u002Fo OSL)","VIRAL (with OSL)","Vertical LOAM",[392,763,1513,10886],{"slug":10920,"sourceId":10886,"sourceLabel":10887,"sourceYear":306,"table":827,"note":10921,"datasets":10922,"metrics":10924,"families":10925,"methods":10926,"methodIds":10927,"rows":102,"failures":30},"viralfusion2022-table-vi","Field flight tests (three flights; UAV moving in a vertical plane parallel to the inspected structure) with camera, VN-100 IMU, two Ouster OS1 LiDARs…",[10923],"VIRAL field flight tests (authors)",[568],[25],[10914,10896,10897,10915,10916,10917],[392,1513,10886],{"slug":10929,"sourceId":10886,"sourceLabel":10887,"sourceYear":306,"table":838,"note":10921,"datasets":10930,"metrics":10931,"families":10932,"methods":10933,"methodIds":10934,"rows":102,"failures":30},"viralfusion2022-table-vii",[10923],[23],[23],[10914,10896,10897,10915,10916,10917],[392,1513,10886],{"slug":10936,"sourceId":1393,"sourceLabel":10937,"sourceYear":374,"table":91,"note":10938,"datasets":10939,"metrics":10940,"families":10941,"methods":10942,"methodIds":10945,"rows":415,"failures":416},"slict2023-table-i","Nguyen et al., 2023","NTU VIRAL; horizontal and vertical LiDARs merged as input for all methods; SLICT loop closure disabled; 400 ms window with 16 intervals; x = divergenc…",[1636],[329],[25],[1437,334,10943,10944],"MARS","SLICT",[321,338,1393],{"slug":10947,"sourceId":1393,"sourceLabel":10937,"sourceYear":374,"table":325,"note":10948,"datasets":10949,"metrics":10950,"families":10951,"methods":10952,"methodIds":10953,"rows":1042,"failures":654},"slict2023-table-ii","Newer College (Ouster 64-channel, built-in 100 Hz IMU); settings as NTU VIRAL but 2 states per interval; LIO-SAM not run because it needs IMU orientat…",[1378],[329],[25],[1437,334,10943,10944],[321,338,1393],{"slug":10955,"sourceId":1393,"sourceLabel":10937,"sourceYear":374,"table":279,"note":10956,"datasets":10957,"metrics":10959,"families":10960,"methods":10961,"methodIds":10964,"rows":102,"failures":416},"slict2023-table-iii","In-house NTU campus ATV dataset (Ouster OS1-128 + Livox Mid-70 merged, VN100 IMU), ground truth by registering scans to a Leica RTC360 map; LC = loop…",[10958],"SLICT in-house NTU campus dataset",[329],[25],[1437,334,10962,10943,10944,10963],"LIO-SAM (LC)","SLICT (LC)",[321,338,1393],{"slug":10966,"sourceId":1393,"sourceLabel":10937,"sourceYear":374,"table":1182,"note":10967,"datasets":10968,"metrics":10969,"families":10970,"methods":10971,"methodIds":10972,"rows":63,"failures":30},"slict2023-text-sec-iv-a","Computational load of SLICT on NTU VIRAL nya_02; LiDAR period 100 ms",[1636],[38],[40],[10944],[1393],{"slug":10974,"sourceId":2916,"sourceLabel":10975,"sourceYear":1234,"table":10976,"note":10977,"datasets":10978,"metrics":10980,"families":10981,"methods":10982,"methodIds":10985,"rows":224,"failures":63},"voxelhashing2013-text-sec-9-1","Nießner et al., 2013","Text Sec. 9.1","Average over all live test scenes (Kinect for Windows or Asus Xtion, 30 Hz) of the entire pipeline including display rendering; ICP with 15 iterations",[10979],"own live captures",[219,38],[40],[10983,10984],"Regular dense voxel grid (estimate)","Voxel hashing (proposed)",[2916],{"slug":10987,"sourceId":2916,"sourceLabel":10975,"sourceYear":1234,"table":10988,"note":10989,"datasets":10990,"metrics":10991,"families":10992,"methods":10993,"methodIds":10995,"rows":154,"failures":30},"voxelhashing2013-text-sec-9-2","Text Sec. 9.2","Hierarchical Fusion (Chen et al. 2013 reference implementation) whole pipeline on the same hardware, below the 30 Hz input rate",[10979],[148],[40],[10994],"Hierarchical Fusion (Chen et al. 2013)",[],{"slug":10997,"sourceId":10998,"sourceLabel":10999,"sourceYear":213,"table":69,"note":11000,"datasets":11001,"metrics":11006,"families":11007,"methods":11008,"methodIds":11010,"rows":224,"failures":154},"nikoohemat2020indoor3d-table-1","nikoohemat2020indoor3d","Nikoohemat et al., 2020","Per-dataset outcome of the proposed pipeline: rooms correctly reconstructed, doors detected and number of manual visual operations.",[11002,11003,11004,11005],"Fire brigade #1","Fire brigade #2","Penthouse (Mura et al.)","TU Delft",[23],[23],[11009],"proposed pipeline",[10998],{"slug":11012,"sourceId":10998,"sourceLabel":10999,"sourceYear":213,"table":108,"note":11013,"datasets":11014,"metrics":11015,"families":11016,"methods":11017,"methodIds":11018,"rows":52,"failures":30},"nikoohemat2020indoor3d-table-2","Point-wise comparison of automatic permanent-structure labels with manual labels, Fire brigade #2; semantic labeling accuracy, not geometric accuracy.",[11003],[23],[23],[11009],[10998],{"slug":11020,"sourceId":10998,"sourceLabel":10999,"sourceYear":213,"table":33,"note":11021,"datasets":11022,"metrics":11023,"families":11024,"methods":11025,"methodIds":11027,"rows":356,"failures":30},"nikoohemat2020indoor3d-table-4","Surface growing segmentation time per dataset (point-to-plane distance 0.10, 0.08, 0.08 and 0.04 m respectively).",[11002,11003,11004,11005],[23],[23],[11026],"surface growing segmentation (pipeline step)",[10998],{"slug":11029,"sourceId":10998,"sourceLabel":10999,"sourceYear":213,"table":3023,"note":11030,"datasets":11031,"metrics":11032,"families":11033,"methods":11034,"methodIds":11036,"rows":154,"failures":30},"nikoohemat2020indoor3d-text-sec-7-2","Manual visual inspection effort for Fire brigade #2.",[11003],[23],[23],[11035],"proposed pipeline (visual correction step)",[10998],{"slug":11038,"sourceId":10998,"sourceLabel":10999,"sourceYear":213,"table":11039,"note":11040,"datasets":11041,"metrics":11043,"families":11044,"methods":11045,"methodIds":11046,"rows":154,"failures":30},"nikoohemat2020indoor3d-text-sec-7-5","Text Sec.7.5","Whole pipeline runtime excluding segmentation for a dataset with 7 million points, about 800 surfaces and 25 rooms.",[11042],"not stated (7 million point dataset)",[23],[23],[11009],[10998],{"slug":11048,"sourceId":11049,"sourceLabel":11050,"sourceYear":11051,"table":69,"note":11052,"datasets":11053,"metrics":11055,"families":11056,"methods":11057,"methodIds":11060,"rows":224,"failures":30},"nister2004vo-table-1","nister2004vo","Nistér et al., 2004",2004,"Metric accuracy of stereo visual odometry on three outdoor ground-vehicle runs on a wooded trail; total path length by DGPS versus visual odometry and…",[11054],"own ground-vehicle runs",[23],[23],[11058,11059],"DGPS reference","Visual odometry (stereo scheme)",[],{"slug":11062,"sourceId":11049,"sourceLabel":11050,"sourceYear":11051,"table":108,"note":11063,"datasets":11064,"metrics":11065,"families":11066,"methods":11067,"methodIds":11068,"rows":120,"failures":30},"nister2004vo-table-2","Frame-to-frame discrepancy in vehicle heading (yaw) between visual odometry and the INS for the three runs",[11054],[2411],[332],[11059],[],{"slug":11070,"sourceId":11049,"sourceLabel":11050,"sourceYear":11051,"table":11071,"note":11072,"datasets":11073,"metrics":11074,"families":11075,"methods":11076,"methodIds":11077,"rows":154,"failures":30},"nister2004vo-text-fig-5-caption","Text Fig.5 caption","Three tight laps of about 20 m diameter, 184 m travelled, returning to the start location",[11054],[1551],[1553],[11059],[],{"slug":11079,"sourceId":11049,"sourceLabel":11050,"sourceYear":11051,"table":6714,"note":11080,"datasets":11081,"metrics":11082,"families":11083,"methods":11084,"methodIds":11085,"rows":154,"failures":30},"nister2004vo-text-sec-5-1","Processing rate on the vehicle limited by other concurrent tasks",[11054],[148],[40],[11059],[],{"slug":11087,"sourceId":3291,"sourceLabel":11088,"sourceYear":698,"table":91,"note":11089,"datasets":11090,"metrics":11092,"families":11093,"methods":11094,"methodIds":11096,"rows":224,"failures":30},"nubert2021delora-table-i","Nubert et al., 2021","ANYmal test mission (LiDAR mounted upside down); relative pose deviation of DeLORA poses combined with the LOAM mapping module against the open-source…",[11091],"ANYmal CLA basement (own data)",[23],[23],[11095],"Ours with mapping (DeLORA + LOAM mapping module)",[3291],{"slug":11098,"sourceId":3291,"sourceLabel":11088,"sourceYear":698,"table":325,"note":11099,"datasets":11100,"metrics":11101,"families":11102,"methods":11103,"methodIds":11114,"rows":3766,"failures":274},"nubert2021delora-table-ii","KITTI odometry, errors over all subsequences of 100 to 800 m; DeLORA trained self-supervised on 00-08, tested on 09 and 10; only the 00-08 mean of LO-…",[469],[438,439],[441],[11104,11105,11106,11107,81,11108,11109,11110,11111,11112,11113],"DeepLO [24]","LO-Net [21]","LO-Net+Map","LOAM [3]","Ours+Map (LOAM scan-to-map refinement)","SUMA [13]","SfMLearner [22]","UnDeepVO [8]","Velas et al. [20]","Zhu et al. [23]",[450,3309,3291,432],{"slug":11116,"sourceId":3291,"sourceLabel":11088,"sourceYear":698,"table":279,"note":11117,"datasets":11118,"metrics":11119,"families":11120,"methods":11121,"methodIds":11124,"rows":618,"failures":30},"nubert2021delora-table-iii","Loss ablation on KITTI, networks trained from scratch on 00-06 and tested on 07-10",[469],[438,439],[441],[11122,11123],"DeLORA loss variant: p2pl","DeLORA loss variant: p2pl + pl2pl",[3291],{"slug":11126,"sourceId":3291,"sourceLabel":11088,"sourceYear":698,"table":2225,"note":11127,"datasets":11128,"metrics":11129,"families":11130,"methods":11131,"methodIds":11133,"rows":63,"failures":30},"nubert2021delora-text-sec-iv-a","Inference time of a single prediction during the ANYmal test, about 32,000 points per scan, range image H = 16, W = 720",[11091],[38],[40],[11132],"DeLORA",[3291],{"slug":11135,"sourceId":11136,"sourceLabel":11137,"sourceYear":306,"table":91,"note":11138,"datasets":11139,"metrics":11141,"families":11142,"methods":11143,"methodIds":11146,"rows":120,"failures":30},"nubert2022constructionfusion-table-i","nubert2022constructionfusion","Nubert et al., 2022a","Relative position error of each estimator w.r.t. RTK GNSS positions during the Construction Task (GNSS also a fused input)",[11140],"Construction Task",[23],[23],[11144,4271,11145],"MSF","TSIF",[11136],{"slug":11148,"sourceId":11136,"sourceLabel":11137,"sourceYear":306,"table":325,"note":11149,"datasets":11150,"metrics":11152,"families":11153,"methods":11154,"methodIds":11155,"rows":120,"failures":30},"nubert2022constructionfusion-table-ii","State-estimation latency from IMU measurement arrival to propagated estimate, Navigation Task",[11151],"Navigation Task",[23],[23],[11144,4271,11145],[11136],{"slug":11157,"sourceId":11136,"sourceLabel":11137,"sourceYear":306,"table":11158,"note":11159,"datasets":11160,"metrics":11161,"families":11162,"methods":11163,"methodIds":11164,"rows":63,"failures":30},"nubert2022constructionfusion-text-sec-v-b1","Text Sec. V-B1","Consistency: deviation between 100 Hz propagated poses and optimized estimates (about 25 Hz), Navigation Task",[11151],[23],[23],[4271],[11136],{"slug":11166,"sourceId":11167,"sourceLabel":11168,"sourceYear":306,"table":731,"note":11169,"datasets":11170,"metrics":11172,"families":11173,"methods":11174,"methodIds":11176,"rows":608,"failures":30},"nubert2022learninglocalizability-table-iv","nubert2022learninglocalizability","Nubert et al., 2022b","Localizability classification averaged over six dimensions; both networks trained 60 epochs on the same simulated splits; test set simulated from the…",[11171],"simulated localizability dataset",[23],[23],[8802,11175],"ResUNet (proposed)",[11167],{"slug":11178,"sourceId":11167,"sourceLabel":11168,"sourceYear":306,"table":1464,"note":11179,"datasets":11180,"metrics":11181,"families":11182,"methods":11183,"methodIds":11186,"rows":63,"failures":30},"nubert2022learninglocalizability-text-sec-v-b","Inference time of the PyTorch model inside a ROS node, no specific code optimization",[3980],[38],[40],[11184,11185],"ResUNet (proposed), CPU-only","ResUNet (proposed), GPU",[11167],{"slug":11188,"sourceId":11189,"sourceLabel":11190,"sourceYear":68,"table":731,"note":11191,"datasets":11192,"metrics":11194,"families":11195,"methods":11196,"methodIds":11198,"rows":2420,"failures":30},"holisticfusion2026-table-iv","holisticfusion2026","Nubert et al., 2026","Mission overview and offline optimization complexity (offline batch optimization time for the whole mission); evaluation PC Intel i9 13900K",[11193],"Holistic Fusion missions (authors)",[23],[23],[11197],"HF Offline",[11189],{"slug":11200,"sourceId":11189,"sourceLabel":11190,"sourceYear":68,"table":803,"note":11201,"datasets":11202,"metrics":11204,"families":11205,"methods":11206,"methodIds":11214,"rows":429,"failures":429},"holisticfusion2026-table-ix","ANYmal indoor locomotion dataset, five sequences, averaged; Qualisys mocap ground truth; mean values stored, standard deviation in the outcome field;…",[11203],"ANYmal indoor locomotion dataset (authors)",[22,2411,330,23],[25,23,332],[11207,11208,11209,11210,11211,11212,11213],"HF Offline (IMU + LR + leg kinematics, tightly fused)","HF World (IMU + LR + leg kinematics, tightly fused)","HF World (IMU + LR + leg odometry velocity, loosely fused)","HF World (IMU + leg kinematics, tightly fused)","HF World (IMU + leg odometry velocity, loosely fused)","Open3D-SLAM (LR only, low rate)","TSIF (IMU + leg kinematics, tightly fused)",[11189],{"slug":11216,"sourceId":11189,"sourceLabel":11190,"sourceYear":68,"table":818,"note":11217,"datasets":11218,"metrics":11220,"families":11221,"methods":11222,"methodIds":11235,"rows":1069,"failures":30},"holisticfusion2026-table-v","ANYmal autonomous hikes (Forest and Mountain\u002FSeealpsee); global ATE [m] and ARE [deg] against post-processed ground truth (offline HF batch optimizati…",[11219],"ANYmal hike missions (authors)",[329,23],[25,23],[11223,11224,11225,11226,11227,11228,11229,11230,11231,11232,11233,11234],"HF (GNSS filtered) - Odom","HF (GNSS filtered) - World","HF (LR-between) - Odom","HF (LR-between) - World","HF - Odom","HF - World","HF GNSS+IMU - Odom","HF GNSS+IMU - World","MINS - World (div.)","MINS - World (split)","Open3D SLAM - LR","TSIF - Odom",[11189,8402],{"slug":11237,"sourceId":11189,"sourceLabel":11190,"sourceYear":68,"table":827,"note":11238,"datasets":11239,"metrics":11240,"families":11241,"methods":11242,"methodIds":11245,"rows":721,"failures":154},"holisticfusion2026-table-vi","Average local estimation quality and smoothness over the two hikes; RTE and RRE averaged over all 1 m pairs; NOJ = jumps above 10 cm between consecuti…",[11219],[2411,330,23],[23,332],[11223,11224,11225,11226,11227,11228,11243,11244,11234],"MINS - World","Open3D-SLAM - LR (\u003C=10 Hz)",[11189,8402],{"slug":11247,"sourceId":11189,"sourceLabel":11190,"sourceYear":68,"table":838,"note":11248,"datasets":11249,"metrics":11250,"families":11251,"methods":11252,"methodIds":11256,"rows":224,"failures":224},"holisticfusion2026-table-vii","ANYmal hike: computational complexity and accuracy versus state-creation rate; mean values stored, standard deviation in the outcome field; evaluation…",[11219],[22,23,38],[25,40,23],[11253,11254,11255],"HF at 10 Hz state creation","HF at 100 Hz state creation","HF at 40 Hz state creation",[11189],{"slug":11258,"sourceId":11259,"sourceLabel":11260,"sourceYear":1694,"table":325,"note":11261,"datasets":11262,"metrics":11264,"families":11265,"methods":11266,"methodIds":11268,"rows":356,"failures":30},"nuchter2007-6dslam-table-ii","nuchter2007_6dslam","Nüchter et al., 2007","Length ratios measured in an uncalibrated aerial photo compared with ratios in the final 77-scan point model of the Schloss Birlinghoven campus",[11263],"author-collected Kurt3D data (Schloss Birlinghoven campus)",[23],[23],[11267],"6D SLAM point model (ICP, loop closing, global relaxation)",[11259],{"slug":11270,"sourceId":11259,"sourceLabel":11260,"sourceYear":1694,"table":11271,"note":11272,"datasets":11273,"metrics":11275,"families":11276,"methods":11277,"methodIds":11279,"rows":154,"failures":30},"nuchter2007-6dslam-text-fig-13-caption","Text Fig. 13 caption","Closed-loop span measured in the registered point cloud versus meter rule (2080 cm)",[11274],"author-collected Kurt3D data (Birlinghoven robotic lab, 32 scans)",[23],[23],[11278],"6D SLAM point model",[11259],{"slug":11281,"sourceId":11259,"sourceLabel":11260,"sourceYear":1694,"table":4863,"note":11282,"datasets":11283,"metrics":11285,"families":11286,"methods":11287,"methodIds":11289,"rows":154,"failures":30},"nuchter2007-6dslam-text-sec-4-3","Effect of approximate kd-tree search on ICP running time",[11284],"not_reported (two 3D scans)",[23],[23],[11288],"ICP with approximate kd-tree search",[11259],{"slug":11291,"sourceId":11259,"sourceLabel":11260,"sourceYear":1694,"table":3618,"note":11292,"datasets":11293,"metrics":11295,"families":11296,"methods":11297,"methodIds":11299,"rows":63,"failures":30},"nuchter2007-6dslam-text-sec-5-3","Error tolerance of the initial estimate for successful ICP registration in the Birlinghoven data set",[11294],"author-collected Kurt3D data (Birlinghoven robotic lab)",[23],[23],[11298],"ICP scan matching",[11259],{"slug":11301,"sourceId":11259,"sourceLabel":11260,"sourceYear":1694,"table":11302,"note":11303,"datasets":11304,"metrics":11305,"families":11306,"methods":11307,"methodIds":11310,"rows":63,"failures":30},"nuchter2007-6dslam-text-sec-5-4","Text Sec. 5.4","Processing times for the 77-scan campus data set",[11263],[23],[23],[11308,11309],"6D SLAM","6D SLAM global relaxation",[11259],{"slug":11312,"sourceId":11313,"sourceLabel":11314,"sourceYear":698,"table":325,"note":11315,"datasets":11316,"metrics":11318,"families":11319,"methods":11320,"methodIds":11331,"rows":2262,"failures":30},"rloam2021-table-ii","rloam2021","Oelsch et al., 2021","Scenario 1, airplane as reference object; APE (cm) and RE (deg) against Gazebo ground truth for 2 to 35 correspondence and optimization iterations; LO…",[11317],"R-LOAM Gazebo simulated datasets",[329,23],[25,23],[11321,11322,11323,11324,11325,11326,11327,11328,11329,11330],"LOAM [1], #Iter 15","LOAM [1], #Iter 2 (def)","LOAM [1], #Iter 25","LOAM [1], #Iter 35","LOAM [1], #Iter 5","R-LOAM, #Iter 15","R-LOAM, #Iter 2 (def)","R-LOAM, #Iter 25","R-LOAM, #Iter 35","R-LOAM, #Iter 5",[392,11313],{"slug":11333,"sourceId":11313,"sourceLabel":11314,"sourceYear":698,"table":279,"note":11334,"datasets":11335,"metrics":11336,"families":11337,"methods":11338,"methodIds":11339,"rows":2262,"failures":30},"rloam2021-table-iii","Scenario 2, van as small reference object; APE (cm) and RE (deg) against Gazebo ground truth for 2 to 35 iterations; LOAM = A-LOAM",[11317],[329,23],[25,23],[11321,11322,11323,11324,11325,11326,11327,11328,11329,11330],[392,11313],{"slug":11341,"sourceId":11313,"sourceLabel":11314,"sourceYear":698,"table":731,"note":11342,"datasets":11343,"metrics":11344,"families":11345,"methods":11346,"methodIds":11347,"rows":2262,"failures":578},"rloam2021-table-iv","Scenario 3, Eiffel Tower as reference object; APE (cm) and RE (deg) against Gazebo ground truth for 2 to 35 iterations; LOAM = A-LOAM; authors state L…",[11317],[329,23],[25,23],[11321,11322,11323,11324,11325,11326,11327,11328,11329,11330],[392,11313],{"slug":11349,"sourceId":11350,"sourceLabel":11351,"sourceYear":306,"table":91,"note":11352,"datasets":11353,"metrics":11355,"families":11356,"methods":11357,"methodIds":11359,"rows":52,"failures":30},"roloam2022-table-i","roloam2022","Oelsch et al., 2022","Share of scan-to-model aligned poses below an APE threshold, using LOAM map-optimized poses as initial guess",[11354],"RO-LOAM hangar datasets (octocopter, VLP-16, Leica MS60 ground truth)",[23],[23],[11358],"scan-to-model alignment (ICP) initialized with LOAM poses",[11350],{"slug":11361,"sourceId":11350,"sourceLabel":11351,"sourceYear":306,"table":325,"note":11362,"datasets":11363,"metrics":11364,"families":11365,"methods":11366,"methodIds":11371,"rows":1042,"failures":30},"roloam2022-table-ii","Parameter study on Dataset 1 for LOAM + RO with L = 50; APE of map-optimized poses (cm) and number of successful TMOs",[11354],[329,23],[25,23],[11367,11368,11369,11370],"LOAM + RO (M = 19, L = 50)","LOAM + RO (M = 29, L = 50)","LOAM + RO (M = 4, L = 50)","LOAM + RO (M = 9, L = 50)",[11350],{"slug":11373,"sourceId":11350,"sourceLabel":11351,"sourceYear":306,"table":279,"note":11374,"datasets":11375,"metrics":11376,"families":11377,"methods":11378,"methodIds":11383,"rows":1042,"failures":30},"roloam2022-table-iii","Parameter study on Dataset 1 for LOAM + RO with M = 9; APE of map-optimized poses (cm) and number of successful TMOs",[11354],[329,23],[25,23],[11379,11380,11381,11382],"LOAM + RO (L = 100, M = 9)","LOAM + RO (L = 15, M = 9)","LOAM + RO (L = 200, M = 9)","LOAM + RO (L = 300, M = 9)",[11350],{"slug":11385,"sourceId":11350,"sourceLabel":11351,"sourceYear":306,"table":731,"note":11386,"datasets":11387,"metrics":11388,"families":11389,"methods":11390,"methodIds":11396,"rows":339,"failures":30},"roloam2022-table-iv","Three hangar datasets with M = 9 and L = 15; means of 5 online runs; APE mapping = map-optimized poses, APE TMO = poses used for TMO; 3-DoF position g…",[11354],[329,148,23],[25,40,23],[11391,11392,7419,11393,11394,11395],"LOAM + RO","LOAM + RO (TMO poses)","R-LOAM + RO","R-LOAM + RO (TMO poses)","R-LOAM [2]",[392,11313,11350],{"slug":11398,"sourceId":85,"sourceLabel":11399,"sourceYear":345,"table":11400,"note":11401,"datasets":11402,"metrics":11403,"families":11404,"methods":11405,"methodIds":11407,"rows":63,"failures":30},"oleynikova2017voxblox-text-fig-6-caption","Oleynikova et al., 2017","Text Fig. 6 caption","EuRoC V1_01_easy, single thread on a quad-core i7 at 2.5 GHz; factors stated in the caption, not read off the plot",[743],[23],[23],[11406],"voxblox grouped raycasting",[85],{"slug":11409,"sourceId":85,"sourceLabel":11399,"sourceYear":345,"table":11410,"note":11411,"datasets":11412,"metrics":11414,"families":11415,"methods":11416,"methodIds":11418,"rows":120,"failures":30},"oleynikova2017voxblox-text-sec-vi-b1","Text Sec. VI-B1","Simulated noiseless RGB-D (320 x 240, 5 m range) at 50 random poses in a 10 m cube with 3 planes, a sphere and a cube; half-truncation-band ESDF, full…",[11413],"synthetic ESDF benchmark",[23],[23],[11417],"voxblox, Half Truncation Band (Full)",[85],{"slug":11420,"sourceId":11421,"sourceLabel":11422,"sourceYear":1032,"table":11400,"note":11423,"datasets":11424,"metrics":11426,"families":11427,"methods":11428,"methodIds":11430,"rows":154,"failures":30},"olson2010passivesync-text-fig-6-caption","olson2010passivesync","Olson, 2010","Synthetic test: uniformly distributed random latency up to 0.5 s, observations every 1 s; proposed-method errors only plotted and not extracted",[11425],"synthetic timing data",[23],[23],[11429],"No synchronization (naive arrival-time stamping)",[],{"slug":11432,"sourceId":11433,"sourceLabel":11434,"sourceYear":213,"table":4020,"note":11435,"datasets":11436,"metrics":11438,"families":11439,"methods":11440,"methodIds":11444,"rows":356,"failures":30},"otero2020mobileindoormapping-text-sec-3-3","otero2020mobileindoormapping","Otero et al., 2020","Authors' summary statistics over the 21 reviewed devices using manufacturer datasheet values (Tables 2-3); coefficient type not stated",[11437],"datasheets of 21 commercial indoor mapping devices",[23],[23],[11441,11442,11443],"21 commercial indoor mapping devices","2D LiDAR devices (includes ZEB-HORIZON at 3,000,000 pts\u002Fs)","3D LiDAR devices",[],{"slug":11446,"sourceId":11433,"sourceLabel":11434,"sourceYear":213,"table":11447,"note":11448,"datasets":11449,"metrics":11450,"families":11451,"methods":11452,"methodIds":11453,"rows":63,"failures":30},"otero2020mobileindoormapping-text-sec-3-5","Text Sec.3.5","Correlation between device weight and operating time over the reviewed devices (Tables 2 and 6); the section reports two different values",[11437],[23],[23],[11441],[],{"slug":11455,"sourceId":11456,"sourceLabel":11457,"sourceYear":16,"table":325,"note":11458,"datasets":11459,"metrics":11460,"families":11461,"methods":11462,"methodIds":11468,"rows":598,"failures":154},"refusion2019-table-ii","refusion2019","Palazzolo et al., 2019","TUM RGB-D dynamic scenes; ATE RMS; ReFusion uses virtual depth from 10 frames (about 0.3 s delay) to fill invalid depth; ReFusion, SF and MF are dense…",[2872],[568],[25],[11463,11464,11465,11466,11467],"DS (G) (DynaSLAM geometric)","DS (N+G) (DynaSLAM neural network + geometric)","MF (MaskFusion, values from its paper)","Ours (ReFusion)","SF (StaticFusion)",[11456,11469],"staticfusion2018",{"slug":11471,"sourceId":11456,"sourceLabel":11457,"sourceYear":16,"table":279,"note":11472,"datasets":11473,"metrics":11475,"families":11476,"methods":11477,"methodIds":11478,"rows":1991,"failures":30},"refusion2019-table-iii","Bonn RGB-D Dynamic Dataset (24 highly dynamic scenes, ASUS Xtion Pro LIVE, OptiTrack Prime 13 ground truth); ATE RMS; 'o box' = obstructing box, 'no b…",[11474],"Bonn RGB-D Dynamic Dataset",[568],[25],[11463,11464,11466,11467],[11456,11469],{"slug":11480,"sourceId":11481,"sourceLabel":11482,"sourceYear":698,"table":325,"note":11483,"datasets":11484,"metrics":11486,"families":11487,"methods":11488,"methodIds":11491,"rows":11492,"failures":340},"locus2021-table-ii","locus2021","Palieri et al., 2021","Husky field datasets from the SubT Urban (Alpha, Beta courses at the Satsop power plant) and Tunnel (Safety Research course, Bruceton mine) circuits;…",[11485],"DARPA SubT Husky datasets (CoSTAR)",[329,3417,113],[25,40,78],[2185,7372,4759,8717,6345,334,11489,11490],"LOCUS","LOCUS FGA",[392,2116,393,2117,338,11481],112,{"slug":11494,"sourceId":11481,"sourceLabel":11482,"sourceYear":698,"table":279,"note":11495,"datasets":11496,"metrics":11497,"families":11498,"methods":11499,"methodIds":11500,"rows":1315,"failures":1315},"locus2021-table-iii","Robustness tests on the Urban datasets: WIO and IMU streams cut after 1200 s, WIO cut after 1200 s, or a 10 s LiDAR gap while moving; OK = negligible…",[11485],[23],[23],[2185,7372,4759,8717,6345,334,11489],[392,2116,393,2117,338,11481],{"slug":11502,"sourceId":11481,"sourceLabel":11482,"sourceYear":698,"table":818,"note":11503,"datasets":11504,"metrics":11506,"families":11507,"methods":11508,"methodIds":11511,"rows":578,"failures":30},"locus2021-table-v","Average number of LiDAR scans dropped per second during the four live competition runs (10 Hz input, no buffering, so a drop means processing exceeded…",[11505],"DARPA SubT Urban Circuit competition runs",[23],[23],[11509,11510],"LOCUS (Husky, live)","LOCUS (Spot, live)",[11481],{"slug":11513,"sourceId":11481,"sourceLabel":11482,"sourceYear":698,"table":11514,"note":11515,"datasets":11516,"metrics":11517,"families":11518,"methods":11519,"methodIds":11520,"rows":63,"failures":30},"locus2021-text-sec-iii-c2","Text Sec.III-C2","Live LOCUS on Spot during the Urban Alpha 2 competition run (multi-level exploration)",[11505],[329],[25],[11510],[11481],{"slug":11522,"sourceId":597,"sourceLabel":11523,"sourceYear":698,"table":325,"note":11524,"datasets":11525,"metrics":11526,"families":11527,"methods":11528,"methodIds":11544,"rows":11545,"failures":30},"mulls2021-table-ii","Pan et al., 2021","KITTI odometry ATE [%] and ARE [deg\u002F100m], averaged over 100-800 m segments; baseline values from original papers and KITTI leaderboard; * = with loop…",[469],[438,439,38],[40,441],[11529,11530,11531,2113,11532,11533,11534,11535,11536,11537,11538,11539,11540,11541,11542,11543],"FALO [25]","IMLS-SLAM [11]","LO-Net [18]","LiTAMIN2 [51]*","LoDoNet [28]","MC2SLAM [13]","MULLS-LO(mc)","MULLS-LO(s1)","MULLS-SLAM(m1)*","MULLS-SLAM(m5)*","MULLS-SLAM(mc)*","MULLS-SLAM(s5m5)*","PSF-LO [27]","S4-SLAM [26]*","SUMA++ [16]*",[2118,3309,597,1968],135,{"slug":11547,"sourceId":597,"sourceLabel":11523,"sourceYear":698,"table":818,"note":11548,"datasets":11549,"metrics":11551,"families":11552,"methods":11553,"methodIds":11554,"rows":356,"failures":30},"mulls2021-table-v","Runtime per frame in detail with about 2k feature points in the current frame and 20k in the local map",[11550],"not_reported (typical frame)",[23,38],[40,23],[4169],[597],{"slug":11556,"sourceId":597,"sourceLabel":11523,"sourceYear":698,"table":11557,"note":11558,"datasets":11559,"metrics":11561,"families":11562,"methods":11563,"methodIds":11564,"rows":154,"failures":30},"mulls2021-text-sec-iv-b1","Text Sec. IV-B1","Map quality against a TLS point cloud: mean distance from each map point to its nearest neighbor in the TLS cloud",[11560],"ISPRS MIMAP (backpack, VLP32C and HDL32E)",[3417],[78],[4169],[597],{"slug":11566,"sourceId":4785,"sourceLabel":11567,"sourceYear":2267,"table":731,"note":11568,"datasets":11569,"metrics":11570,"families":11571,"methods":11572,"methodIds":11581,"rows":102,"failures":30},"pinslam2024-table-iv","Pan et al., 2024","KITTI SLAM comparison with motion-compensated scans, ATE RMSE [m] at decimeter precision; only the averages over sequences with loops (Avg.*) and over…",[469],[568],[25],[11573,11574,11575,11576,4778,11577,11578,11579,11580],"HLBA [37] (offline)","Litamin2 [93]","MULLS [52]","PIN-LO","SC-F-LOAM [82, 27] (offline PGO)","SC-KISS-ICP [78, 27] (offline PGO)","SC-LeGO-LOAM [68, 27]","SuMa [4]",[9453,597,4785,432],{"slug":11583,"sourceId":4785,"sourceLabel":11567,"sourceYear":2267,"table":803,"note":11584,"datasets":11585,"metrics":11586,"families":11587,"methods":11588,"methodIds":11597,"rows":52,"failures":30},"pinslam2024-table-ix","Replica RGB-D camera tracking, ATE RMSE [cm], loop closure disabled; only the average column over 8 sequences extracted; baseline values as listed by…",[3223],[568],[25],[11589,11590,11591,4778,11592,11593,11594,11595,11596],"Co-SLAM [83]","ESLAM [24]","NICE-SLAM [101]","PIN-SLAM w\u002Fo BA","PIN-SLAM w\u002Fo color","Point-SLAM [65]","Vox-Fusion [92]","iMAP [71]",[3181,3182,6378,3184,4785,3185],{"slug":11599,"sourceId":4785,"sourceLabel":11567,"sourceYear":2267,"table":838,"note":11600,"datasets":11601,"metrics":11602,"families":11603,"methods":11604,"methodIds":11608,"rows":1790,"failures":224},"pinslam2024-table-vii","Newer College handheld LiDAR (OS1-64 long sequences, OS0-128 shorter sequences), reference poses from registering each scan to a survey-grade TLS map;…",[3253],[568],[25],[11605,11606,11607,11575,11576,4778,11579,11580],"F-LOAM [82]","KISS-ICP [78]","MD-SLAM [13]",[393,577,597,4785,432],{"slug":11610,"sourceId":4785,"sourceLabel":11567,"sourceYear":2267,"table":852,"note":11611,"datasets":11612,"metrics":11614,"families":11615,"methods":11616,"methodIds":11618,"rows":429,"failures":30},"pinslam2024-table-viii","Hilti-21 handheld OS0-64 LiDAR sequences (indoor offices, labs and basements; outdoor construction sites); reference trajectories from a total station…",[11613],"Hilti-21 (Hilti SLAM Challenge 2021)",[568],[25],[11605,11617,11606,4778],"HDLGraph-SLAM [30]",[393,577,394,4785],{"slug":11620,"sourceId":4785,"sourceLabel":11567,"sourceYear":2267,"table":11621,"note":11622,"datasets":11623,"metrics":11624,"families":11625,"methods":11626,"methodIds":11634,"rows":301,"failures":356},"pinslam2024-table-xi","Table XI","Newer College 3D reconstruction against the survey-grade TLS reference model (mm-level accuracy); Quad from 02_long, Math Institute from math_easy; 20…",[3253],[2343,74,75,76],[78],[11627,11628,11629,11630,11631,11632,11633],"NKSR [20] with KISS-ICP poses","Nerf-LOAM [11] own odometry","PIN-SLAM (own odometry)","Puma [76] own odometry","SHINE [100] with KISS-ICP poses","SLAMesh [62] own odometry","VDB-Fusion [77] with KISS-ICP poses",[3248,4785,11635,3263,3264,3265],"ruan2023slamesh",{"slug":11637,"sourceId":4785,"sourceLabel":11567,"sourceYear":2267,"table":11638,"note":11639,"datasets":11640,"metrics":11641,"families":11642,"methods":11643,"methodIds":11647,"rows":52,"failures":30},"pinslam2024-table-xvi","Table XVI","Average operation speed and localization error over KITTI 00-10 on a single NVIDIA A4000 GPU; light version uses fewer mapping and odometry iterations",[469],[438,148,38],[40,441],[11644,11645,11646],"Nerf-LOAM [11]","PIN-SLAM (full)","PIN-SLAM (light)",[3248,4785],{"slug":11649,"sourceId":11650,"sourceLabel":11651,"sourceYear":562,"table":325,"note":11652,"datasets":11653,"metrics":11654,"families":11655,"methods":11656,"methodIds":11663,"rows":1991,"failures":30},"pings2025-table-ii","pings2025","Pan et al., 2025","Oxford Spires surface reconstruction against the millimetre-accurate Leica RTC360 TLS reference map; localization disabled and ground-truth poses used…",[72],[2343,74,75,76],[78],[11657,11658,11659,11660,11661,11662],"GSS [11]","Nerfacto [62] (offline)","OpenMVS [5] (offline)","PIN-SLAM [51]","PINGS (Ours)","VDB-Fusion [67]",[11650,4785,3265],{"slug":11665,"sourceId":11650,"sourceLabel":11651,"sourceYear":562,"table":279,"note":11666,"datasets":11667,"metrics":11669,"families":11670,"methods":11671,"methodIds":11678,"rows":2882,"failures":30},"pings2025-table-iii","In-house car dataset (Bonn), full sequences; reference poses from offline LiDAR bundle adjustment with RTK-GNSS, point cloud alignment and geo-referen…",[11668],"in-house car dataset",[329,438],[25,441],[11672,11673,11674,11675,11660,11661,11676,11677],"F-LOAM [69]","KISS-ICP [68]","MULLS [50]","PIN odometry [51]","PINGS odometry","SuMa [3]",[393,577,597,11650,4785,432],{"slug":11680,"sourceId":11650,"sourceLabel":11651,"sourceYear":562,"table":4401,"note":11681,"datasets":11682,"metrics":11684,"families":11685,"methods":11686,"methodIds":11687,"rows":154,"failures":30},"pings2025-text-sec-v","Overall processing time stated in the limitations; SDF mapping and LiDAR odometry run at sensor frame rate, radiance-field mapping dominates",[11683],"in-house car dataset and Oxford Spires (not specified)",[38],[40],[11661],[11650],{"slug":11689,"sourceId":11690,"sourceLabel":11691,"sourceYear":107,"table":91,"note":11692,"datasets":11693,"metrics":11695,"families":11696,"methods":11697,"methodIds":11700,"rows":29,"failures":356},"pang2018ndticp-table-i","pang2018ndticp","Pang et al., 2018","MCity route of 350 m at 17 mph; localisation MAE and average registration time for different reference-map resolutions; ICP rows for 121 and 400 point…",[11694],"MCity test route (350 m)",[329,38],[25,40],[11698,11699],"ICP (kd-tree, point-to-point)","NDT",[478,9932],{"slug":11702,"sourceId":11690,"sourceLabel":11691,"sourceYear":107,"table":325,"note":11703,"datasets":11704,"metrics":11705,"families":11706,"methods":11707,"methodIds":11714,"rows":224,"failures":30},"pang2018ndticp-table-ii","MCity 350 m route; ICP with one parameter varied per row (values of the other parameters not stated)",[11694],[329,38],[25,40],[11708,11709,11710,11711,11712,11713],"ICP (Euclidean fitness threshold (m) = 0.05)","ICP (Euclidean fitness threshold (m) = 0.1)","ICP (Euclidean fitness threshold (m) = 0.2)","ICP (transformation difference threshold (m) = 0.005)","ICP (transformation difference threshold (m) = 0.01)","ICP (transformation difference threshold (m) = 0.02)",[478],{"slug":11716,"sourceId":11690,"sourceLabel":11691,"sourceYear":107,"table":279,"note":11717,"datasets":11718,"metrics":11719,"families":11720,"methods":11721,"methodIds":11731,"rows":102,"failures":30},"pang2018ndticp-table-iii","MCity 350 m route; NDT with one parameter varied per row (values of the other parameters not stated)",[11694],[329,38],[25,40],[11722,11723,11724,11725,11726,11727,11728,11729,11730],"NDT (maximum step size (m) = 0.05)","NDT (maximum step size (m) = 0.1)","NDT (maximum step size (m) = 0.2)","NDT (transformation difference threshold (m) = 0.005)","NDT (transformation difference threshold (m) = 0.01)","NDT (transformation difference threshold (m) = 0.02)","NDT (voxel size (m) = 0.5)","NDT (voxel size (m) = 1)","NDT (voxel size (m) = 2)",[9932],{"slug":11733,"sourceId":11690,"sourceLabel":11691,"sourceYear":107,"table":731,"note":11734,"datasets":11735,"metrics":11737,"families":11738,"methods":11739,"methodIds":11742,"rows":356,"failures":30},"pang2018ndticp-table-iv","autonomous path following on MSU West Circle Drive with a map built from late July and early August 2017 data; December 2017 test with snow cover, bot…",[11736],"MSU West Circle Drive",[23],[23],[11740,11741],"ICP based localization","NDT based localization",[478,9932],{"slug":11744,"sourceId":11690,"sourceLabel":11691,"sourceYear":107,"table":10009,"note":11745,"datasets":11746,"metrics":11747,"families":11748,"methods":11749,"methodIds":11750,"rows":63,"failures":30},"pang2018ndticp-text-sec-iv-d-2","driving around West Circle Drive with many vehicles and other dynamic objects; example with a large bus blocking nearly half of the field of view",[11736],[329],[25],[522,11699],[478,9932],{"slug":11752,"sourceId":11690,"sourceLabel":11691,"sourceYear":107,"table":5451,"note":11753,"datasets":11754,"metrics":11756,"families":11757,"methods":11758,"methodIds":11759,"rows":120,"failures":30},"pang2018ndticp-text-sec-iv-e","MCity route of 1 km driven at three speed levels: level 1 5 to 10 mph, level 2 15 to 20 mph, level 3 25 to 30 mph",[11755],"MCity route (1 km)",[329],[25],[522,11699],[478,9932],{"slug":11761,"sourceId":11690,"sourceLabel":11691,"sourceYear":107,"table":5461,"note":11762,"datasets":11763,"metrics":11765,"families":11766,"methods":11767,"methodIds":11768,"rows":416,"failures":154},"pang2018ndticp-text-sec-iv-f","MCity special segments: a turning segment at about 15 mph, and a circular open area of 40 m radius with very few vertical features at 17 mph, compared…",[11764],"MCity",[329],[25],[522,11699],[478,9932],{"slug":11770,"sourceId":11771,"sourceLabel":11772,"sourceYear":107,"table":91,"note":11773,"datasets":11774,"metrics":11776,"families":11777,"methods":11778,"methodIds":11781,"rows":356,"failures":30},"elasticlidarfusion2018-table-i","elasticlidarfusion2018","Park et al., 2018","global loop-closure optimisation cost for the map of Fig. 1 (office); proposed closes the loop at Fig. 5 (i), CT-SLAM batch-optimises the whole subsam…",[11775],"authors' hand-held spinning LiDAR data",[23],[23],[11779,11780],"CT-SLAM [3] (global batch trajectory optimisation)","Proposed (Elastic LiDAR Fusion, deformation graph)",[11771,1180],{"slug":11783,"sourceId":11771,"sourceLabel":11772,"sourceYear":107,"table":325,"note":11784,"datasets":11785,"metrics":11786,"families":11787,"methods":11788,"methodIds":11790,"rows":356,"failures":30},"elasticlidarfusion2018-table-ii","absolute trajectory RMSE between the deformed trajectory of the proposed method and the globally optimised CT-SLAM [3] trajectory (reference is anothe…",[11775],[568],[25],[11789],"Proposed (Elastic LiDAR Fusion)",[11771],{"slug":11792,"sourceId":11771,"sourceLabel":11772,"sourceYear":107,"table":279,"note":11793,"datasets":11794,"metrics":11795,"families":11796,"methods":11797,"methodIds":11800,"rows":618,"failures":30},"elasticlidarfusion2018-table-iii","floor patches of 0.7 m radius (Fig. 8b); error = mean projective distance of points or surfels to the mean plane of each patch (relative noise, no gro…",[11775],[1830],[78],[11798,11799],"CT-SLAM [3] (raw point cloud)","Proposed (Elastic LiDAR Fusion, fused surfels)",[11771,1180],{"slug":11802,"sourceId":11803,"sourceLabel":11804,"sourceYear":306,"table":325,"note":11805,"datasets":11806,"metrics":11808,"families":11809,"methods":11810,"methodIds":11816,"rows":578,"failures":30},"elasticity-ct2022-table-ii","elasticity_ct2022","Park et al., 2022","simulation: 5 s local window, simulated angular velocity and linear acceleration at 100 Hz with bias and Gaussian noise, 1000 random timestamped spars…",[11807],"simulation (local trajectory optimisation)",[329,23],[25,23],[11811,11812,11813,11814,11815],"Approximation model, Spline Direct, SE(3), 101 controls, 606 states [9]","Approximation model, Spline Direct, SE(3), 11 controls, 66 states [9]","Approximation model, Spline Direct, SE(3), 51 controls, 306 states [9]","Composition model, Linear interpolation, Linear Composition, SO(3)+R3 update, 11 compositions, 66 states [3]","Composition model, Linear se(3) interpolation, Spline Composition, SE(3) update, 11 controls, 66 states (Ours)",[11803,1180],{"slug":11818,"sourceId":11803,"sourceLabel":11804,"sourceYear":306,"table":731,"note":11784,"datasets":11819,"metrics":11821,"families":11822,"methods":11823,"methodIds":11825,"rows":120,"failures":30},"elasticity-ct2022-table-iv",[11820],"authors' hand-held datasets",[568],[25],[11824],"Proposed (ElasticLiDAR++, deformed trajectory)",[11803],{"slug":11827,"sourceId":11803,"sourceLabel":11804,"sourceYear":306,"table":818,"note":11828,"datasets":11829,"metrics":11831,"families":11832,"methods":11833,"methodIds":11836,"rows":1069,"failures":30},"elasticity-ct2022-table-v","known planar patches; position error = projective distance to patch mean plane (mm), normal error in rad; CT-SLAM [3] cloud is undistorted by its glob…",[11830],"authors' real datasets",[23,1830],[78,23],[11834,11835],"CT-SLAM [3] (raw points)","Proposed (fused surfels)",[11803,1180],{"slug":11838,"sourceId":11803,"sourceLabel":11804,"sourceYear":306,"table":827,"note":11839,"datasets":11840,"metrics":11842,"families":11843,"methods":11844,"methodIds":11849,"rows":1069,"failures":30},"elasticity-ct2022-table-vi","loop-closure misalignment estimation on mixed indoor and outdoor data; ground truth from the globally optimised trajectory; 10 locations x 50 random i…",[11841],"authors' mixed indoor and outdoor point clouds",[23],[23],[11845,11846,11847,11848],"(a) Sparse surfel ICP (configuration of previous work [2])","(b) Open3D global registration (FPFH + RANSAC) [60]","(c) SHOT initialisation + point-to-plane ICP [61]","(d) Proposed sequential metric localisation",[11803,11771,11850],"zhou2018open3d",{"slug":11852,"sourceId":11803,"sourceLabel":11804,"sourceYear":306,"table":5451,"note":11853,"datasets":11854,"metrics":11856,"families":11857,"methods":11858,"methodIds":11861,"rows":356,"failures":30},"elasticity-ct2022-text-sec-iv-e","full-stack simulation of a rotating single-beam LiDAR and IMU in an office-like structure (Fig. 7); mean relative trajectory error to simulated ground…",[11855],"simulation (full sensor stack)",[2411,330],[332],[11859,11860],"Proposed (ElasticLiDAR++)","method in [3] (CT-SLAM)",[11803,1180],{"slug":11863,"sourceId":11803,"sourceLabel":11804,"sourceYear":306,"table":11864,"note":11865,"datasets":11866,"metrics":11868,"families":11869,"methods":11870,"methodIds":11871,"rows":154,"failures":30},"elasticity-ct2022-text-sec-viii-b","Text Sec. VIII-B","average elapsed time of the implementation; surfel fusion implemented in single-thread MATLAB",[11867],"authors' VLP-16 datasets",[23],[23],[11859],[11803],{"slug":11873,"sourceId":11874,"sourceLabel":11875,"sourceYear":2267,"table":11876,"note":11877,"datasets":11878,"metrics":11879,"families":11880,"methods":11881,"methodIds":11882,"rows":29,"failures":30},"rtgslam2024-supp-table-11","rtgslam2024","Peng et al., 2024","Supp. Table 11","Replica geometry accuracy versus classical and learned SLAM (supplementary); GO-SLAM completion considers unscanned regions",[3223],[75,76,23],[78,23],[5252,5233,9907,81],[2897,2915],{"slug":11884,"sourceId":11874,"sourceLabel":11875,"sourceYear":2267,"table":6381,"note":11885,"datasets":11886,"metrics":11887,"families":11888,"methods":11889,"methodIds":11890,"rows":608,"failures":30},"rtgslam2024-supp-table-5","Replica geometry accuracy (supplementary)",[3223],[75,76,23],[78,23],[3166,3168,3174,81,3175,3177],[3181,3182,3184,3185,3186],{"slug":11892,"sourceId":11874,"sourceLabel":11875,"sourceYear":2267,"table":11893,"note":11894,"datasets":11895,"metrics":11896,"families":11897,"methods":11898,"methodIds":11899,"rows":120,"failures":30},"rtgslam2024-supp-table-6","Supp. Table 6","Replica tracking accuracy (cm), average of 8 scenes; per-scene values not extracted",[3223],[329],[25],[3166,3168,3174,81,3175,3177],[3181,3182,3184,3185,3186],{"slug":11901,"sourceId":11874,"sourceLabel":11875,"sourceYear":2267,"table":11902,"note":11903,"datasets":11904,"metrics":11905,"families":11906,"methods":11907,"methodIds":11909,"rows":224,"failures":30},"rtgslam2024-supp-table-7","Supp. Table 7","Time and memory on TUM RGB-D (supplementary)",[2872],[148,219],[40],[11908,3168,3174,81,3175,3177],"CO-SLAM",[3181,3182,3184,3185,3186],{"slug":11911,"sourceId":11874,"sourceLabel":11875,"sourceYear":2267,"table":69,"note":11912,"datasets":11913,"metrics":11915,"families":11916,"methods":11917,"methodIds":11918,"rows":721,"failures":63},"rtgslam2024-table-1","Time and memory on Replica office0 and the self-scanned Azure home scene; all methods rerun with official code on the same desktop; X = out of memory",[11914,3223],"Azure (self-scanned)",[148,219],[40],[3166,3168,3174,81,3175,3177],[3181,3182,3184,3185,3186],{"slug":11920,"sourceId":11874,"sourceLabel":11875,"sourceYear":2267,"table":108,"note":11921,"datasets":11922,"metrics":11923,"families":11924,"methods":11925,"methodIds":11927,"rows":721,"failures":30},"rtgslam2024-table-2","TUM RGB-D tracking accuracy (cm); neural methods rerun with official code; ElasticFusion, ORB-SLAM2 and BAD-SLAM listed as classical references",[2872],[329],[25],[11926,3166,3168,5233,3174,6668,81,3175,3177],"BAD-SLAM",[9325,3181,2915,3182,3184,1511,3185,3186],{"slug":11929,"sourceId":11874,"sourceLabel":11875,"sourceYear":2267,"table":17,"note":11930,"datasets":11931,"metrics":11932,"families":11933,"methods":11934,"methodIds":11935,"rows":608,"failures":30},"rtgslam2024-table-3","ScanNet++ geometry on 4 scenes using ground-truth camera poses; NeRF methods meshed by marching cubes at 1 cm, Point-SLAM by TSDF of re-rendered depth…",[6662],[75,76,23],[78,23],[3166,3168,3174,81,3175,3177],[3181,3182,3184,3185,3186],{"slug":11937,"sourceId":11938,"sourceLabel":11939,"sourceYear":11940,"table":108,"note":11941,"datasets":11942,"metrics":11944,"families":11945,"methods":11946,"methodIds":11949,"rows":224,"failures":30},"pfister2000surfels-table-2","pfister2000surfels","Pfister et al., 2000",2000,"Model sizes: number of surfels and file size for the full (3 LDIs) and 3-to-1 reduced LDC trees; three LODs and three surfel mipmap levels",[11943],"synthetic surfel objects",[219,23],[40,23],[11947,11948],"LDC tree, 3 LDIs","LDC tree, 3-to-1 reduced",[11938],{"slug":11951,"sourceId":11938,"sourceLabel":11939,"sourceYear":11940,"table":17,"note":11952,"datasets":11953,"metrics":11954,"families":11955,"methods":11956,"methodIds":11958,"rows":2420,"failures":30},"pfister2000surfels-table-3","Frame rates averaged over one minute of arbitrary rotation, unoptimized C, pull-push reconstruction; all models 3-to-1 reduced except Wasp 3LDI; Wasp…",[11943],[148],[40],[11957],"surfel renderer",[11938],{"slug":11960,"sourceId":11938,"sourceLabel":11939,"sourceYear":11940,"table":11961,"note":11962,"datasets":11963,"metrics":11964,"families":11965,"methods":11966,"methodIds":11969,"rows":274,"failures":30},"pfister2000surfels-text-sec-8","Text Sec. 8","Wasp model: 128k polygons with 2.3 MB of textures rendered by software-only OpenGL on Windows NT with GL_LINEAR_MIPMAP_NEAREST, versus the unoptimized…",[11943],[148],[40],[11967,11968,11957],"Mesa OpenGL","Microsoft OpenGL (opengl32.lib)",[11938],{"slug":11971,"sourceId":2385,"sourceLabel":11972,"sourceYear":2267,"table":91,"note":11973,"datasets":11974,"metrics":11976,"families":11977,"methods":11978,"methodIds":11986,"rows":301,"failures":224},"coinlio2024-table-i","Pfreundschuh et al., 2024","Newer College Dataset, hand-held 128-beam Ouster OS0; ATE RMSE (m) \u002F RTE (%) over 10 m segments via evo; RTE > 20% declared failed (x) and ATE not rep…",[11975],"Newer College Dataset (multi-camera extension)",[568,330],[25,332],[11979,11980,11981,11982,11983,11984,11985],"Du and Beltrame [8]","FAST-LIO2 [1]","KISS-ICP [10]","LIO-SAM [12]","MD-SLAM [6]","Ours (COIN-LIO)","RI-LIO [7]",[2385,321,577,338],{"slug":11988,"sourceId":2385,"sourceLabel":11972,"sourceYear":2267,"table":325,"note":11989,"datasets":11990,"metrics":11992,"families":11993,"methods":11994,"methodIds":11995,"rows":11996,"failures":11997},"coinlio2024-table-ii","ENWIDE dataset, hand-held Ouster OS0-128 with integrated IMU; ground-truth positions from Leica MS60 (about 3 cm); ATE RMSE (m) \u002F RTE (%) over 10 m se…",[11991],"ENWIDE",[568,330],[25,332],[11979,11980,11981,11982,11983,11984,11985],[2385,321,577,338],89,51,{"slug":11999,"sourceId":2385,"sourceLabel":11972,"sourceYear":2267,"table":279,"note":12000,"datasets":12001,"metrics":12002,"families":12003,"methods":12004,"methodIds":12010,"rows":641,"failures":154},"coinlio2024-table-iii","Ablation of COIN-LIO image type and patch selection policy on ENWIDE; ATE RMSE (m); x = failed",[11991],[568],[25],[12005,12006,12007,12008,12009],"COIN-LIO ablation: Filtered image + Complementary features (proposed)","COIN-LIO ablation: Filtered image + Random features","COIN-LIO ablation: Filtered image + Strongest-gradient features","COIN-LIO ablation: Intensity image + Strongest-gradient features","COIN-LIO ablation: Reflectivity image + Strongest-gradient features",[2385],{"slug":12012,"sourceId":2385,"sourceLabel":11972,"sourceYear":2267,"table":1182,"note":12013,"datasets":12014,"metrics":12015,"families":12016,"methods":12017,"methodIds":12019,"rows":63,"failures":30},"coinlio2024-text-sec-iv-a","Average runtime of COIN-LIO on the Newer College Park sequence",[1378],[23,38],[40,23],[2378,12018],"COIN-LIO (photometric part only)",[2385],{"slug":12021,"sourceId":12022,"sourceLabel":12023,"sourceYear":1234,"table":1072,"note":12024,"datasets":12025,"metrics":12027,"families":12028,"methods":12029,"methodIds":12032,"rows":9098,"failures":30},"pomerleau2013comparing-table-6","pomerleau2013comparing","Pomerleau et al., 2013","35 scan pairs per data set (overlap 0.30 to 0.99) with 64 Gaussian perturbations per level (EP easy, MP medium, HP hard); errors after registration ag…",[12026],"Challenging Laser Registration (Pomerleau et al. 2012)",[23],[23],[12030,12031],"point-to-plane ICP (libpointmatcher baseline, 70% trimmed)","point-to-point ICP (libpointmatcher baseline, 75% trimmed)",[478,1944],{"slug":12034,"sourceId":12022,"sourceLabel":12023,"sourceYear":1234,"table":4139,"note":12035,"datasets":12036,"metrics":12037,"families":12038,"methods":12039,"methodIds":12040,"rows":120,"failures":30},"pomerleau2013comparing-text-sec-5-2-4","totals over the 80,640 baseline registrations (all six data sets and perturbation levels unless stated)",[12026],[23,38],[40,23],[12030,12031],[478,1944],{"slug":12042,"sourceId":7432,"sourceLabel":12043,"sourceYear":921,"table":12044,"note":12045,"datasets":12046,"metrics":12048,"families":12049,"methods":12050,"methodIds":12052,"rows":63,"failures":63},"pomerleau2014-icpmapper-text-sec-v-a","Pomerleau et al., 2014","Text Sec. V.A","Hospital visitor parking lot, nine surveys over three days; classification compared with a night-time ground-truth map (static if a ground-truth point…",[12047],"authors' ARTOR HDL-32E data (parking lot)",[23],[23],[12051],"Bayesian dynamic-point classification",[7432],{"slug":12054,"sourceId":7432,"sourceLabel":12043,"sourceYear":921,"table":12055,"note":12056,"datasets":12057,"metrics":12059,"families":12060,"methods":12061,"methodIds":12063,"rows":120,"failures":30},"pomerleau2014-icpmapper-text-sec-v-b","Text Sec. V.B","Controlled street without traffic, robot parked, one moving object at a time; target speed not stated; for the minibus the acceleration phase is inclu…",[12058],"authors' ARTOR HDL-32E data (remote street)",[23],[23],[12062],"dual non-rigid ICP velocity estimation",[7432],{"slug":12065,"sourceId":7432,"sourceLabel":12043,"sourceYear":921,"table":12066,"note":12067,"datasets":12068,"metrics":12070,"families":12071,"methods":12072,"methodIds":12076,"rows":274,"failures":154},"pomerleau2014-icpmapper-text-sec-v-d","Text Sec. V.D","Module rates with input run at recorded rate; registration downsamples points and uses wheel odometry as prior",[12069],"authors' ARTOR data",[148,38],[40],[12073,12074,12075],"global map maintenance module","registration module (libpointmatcher ICP)","velocity estimation module",[7432],{"slug":12078,"sourceId":12079,"sourceLabel":12080,"sourceYear":681,"table":12081,"note":12082,"datasets":12083,"metrics":12085,"families":12086,"methods":12087,"methodIds":12089,"rows":274,"failures":30},"pomerleau2015review-text-sec-3-4","pomerleau2015review","Pomerleau et al., 2015","Text Sec. 3.4","SmartTer laser odometry registering each scan to a global map without loop closure; drive of 3.8 km with four loops around the ETH main building and t…",[12084],"SmartTer urban drive (Zurich)",[1551,148],[40,1553],[12088],"libpointmatcher ICP (point-to-point configuration, Table 3.6)",[12022],{"slug":12091,"sourceId":12092,"sourceLabel":12093,"sourceYear":2267,"table":7969,"note":12094,"datasets":12095,"metrics":12097,"families":12098,"methods":12099,"methodIds":12101,"rows":274,"failures":154},"prieto2024mars-text-sec-4-3","prieto2024mars","Prieto et al., 2024","Stopping criterion phi = explored free pixels divided by free pixels of the teleoperated-SLAM 'ground-truth' 2D map, times 100; values quoted in text…",[12096],"NYUAD campus case study (about 80 m2)",[76],[78],[12100],"RA1 frontier exploration with area-based stopping criterion (proposed MARS)",[12092],{"slug":12103,"sourceId":12104,"sourceLabel":12105,"sourceYear":1234,"table":69,"note":12106,"datasets":12107,"metrics":12109,"families":12110,"methods":12111,"methodIds":12119,"rows":429,"failures":154},"puente2013mobilemapping-table-1","puente2013mobilemapping","Puente et al., 2013","Manufacturer post-processed navigation accuracy (RMS) with GNSS signal, PPK (RTK for DYNASCAN)",[12108],"manufacturer specifications",[23],[23],[12112,12113,12114,12115,12116,12117,12118],"DYNASCAN","IP-S2 AG58","IP-S2 AG60","MX8","ROAD SCANNER","STREET MAPPER","VMX-250\u002FLYNX",[],{"slug":12121,"sourceId":12104,"sourceLabel":12105,"sourceYear":1234,"table":108,"note":12122,"datasets":12123,"metrics":12124,"families":12125,"methods":12126,"methodIds":12127,"rows":429,"failures":618},"puente2013mobilemapping-table-2","Manufacturer post-processed navigation accuracy (RMS) after a 1 min GPS outage, PPK",[12108],[23],[23],[12112,12113,12114,12115,12116,12117,12118],[],{"slug":12129,"sourceId":12130,"sourceLabel":12131,"sourceYear":698,"table":279,"note":12132,"datasets":12133,"metrics":12135,"families":12136,"methods":12137,"methodIds":12139,"rows":224,"failures":30},"rflio2021-table-iii","rflio2021","Qian et al., 2021","Residual moving-object points counted in the maps of LIO-SAM and RF-LIO (same feature extraction); removal rate relative to LIO-SAM",[12134],"self-collected datasets",[23],[23],[334,12138],"RF-LIO",[338,12130],{"slug":12141,"sourceId":12130,"sourceLabel":12131,"sourceYear":698,"table":731,"note":12142,"datasets":12143,"metrics":12144,"families":12145,"methods":12146,"methodIds":12150,"rows":641,"failures":154},"rflio2021-table-iv","Low and medium dynamic self-collected datasets; LiDAR and IMU only, GPS as ground truth; RF-LIO and LIO-SAM share feature extraction and loop closure",[12134],[568],[25],[334,461,12147,12148,12149],"RF-LIO (After)","RF-LIO (FA)","RF-LIO (First)",[338,2118,12130],{"slug":12152,"sourceId":12130,"sourceLabel":12131,"sourceYear":698,"table":818,"note":12153,"datasets":12154,"metrics":12155,"families":12156,"methods":12157,"methodIds":12158,"rows":578,"failures":30},"rflio2021-table-v","High dynamic UrbanLoco sequences with many moving objects; LiDAR and IMU only, GPS as ground truth",[8748],[568],[25],[334,461,12147,12148,12149],[338,2118,12130],{"slug":12160,"sourceId":12130,"sourceLabel":12131,"sourceYear":698,"table":827,"note":12161,"datasets":12162,"metrics":12164,"families":12165,"methods":12166,"methodIds":12167,"rows":641,"failures":30},"rflio2021-table-vi","Runtime of RF-LIO variants for processing one scan",[12163],"self-collected datasets and UrbanLoco",[38],[40],[12147,12148,12149],[12130],{"slug":12169,"sourceId":12170,"sourceLabel":12171,"sourceYear":68,"table":69,"note":12172,"datasets":12173,"metrics":12175,"families":12176,"methods":12177,"methodIds":12179,"rows":274,"failures":30},"qian2026-tunnel2dgs-table-1","qian2026_tunnel2dgs","Qian et al., 2026","Accuracy under threshold of 2DGS (mask) rendered depth against valid (non-missing) MVS depths over all 210 camera viewpoints; MVS depth is the compari…",[12174],"Zhejiang shield tunnel UAV video (authors)",[23],[23],[12178],"2DGS (mask)",[12170],{"slug":12181,"sourceId":12170,"sourceLabel":12171,"sourceYear":68,"table":17,"note":12182,"datasets":12183,"metrics":12184,"families":12185,"methods":12186,"methodIds":12190,"rows":224,"failures":30},"qian2026-tunnel2dgs-table-3","Modeling time per pipeline stage for the three mesh models on the same workstation; MVS dense stage run with GPU acceleration; face counts of Nos. 2 a…",[12174],[23],[23],[12187,12188,12189],"2DGS (mask) + TSDF (No. 3)","2DGS (no mask) + TSDF (No. 2)","MVS + surface reconstruction (No. 1)",[6084,12170],{"slug":12192,"sourceId":12170,"sourceLabel":12171,"sourceYear":68,"table":12193,"note":12194,"datasets":12195,"metrics":12196,"families":12197,"methods":12198,"methodIds":12199,"rows":274,"failures":30},"qian2026-tunnel2dgs-text-sec-geometric-accuracy-evaluation","Text Sec. Geometric Accuracy Evaluation","Mean point-to-point distance from uniformly sampled mesh points of each 2DGS+TSDF model to the MVS+surface-reconstruction mesh (No. 1, designated refe…",[12174],[3417,23],[78,23],[12187,12188],[6084,12170],{"slug":12201,"sourceId":251,"sourceLabel":12202,"sourceYear":107,"table":91,"note":12203,"datasets":12204,"metrics":12205,"families":12206,"methods":12207,"methodIds":12211,"rows":2388,"failures":30},"vinsmono2018-table-i","Qin et al., 2018","EuRoC, left camera only; RMSE of the absolute trajectory error as defined in ref. [43]; alignment not stated; OKVIS = OKVIS with the monocular camera;…",[1482],[568],[25],[12208,12209,12210],"OKVIS (monocular)","VINS (VIO only)","VINS_loop (with loop closure)",[1510,251],{"slug":12213,"sourceId":251,"sourceLabel":12202,"sourceYear":107,"table":325,"note":12214,"datasets":12215,"metrics":12217,"families":12218,"methods":12219,"methodIds":12220,"rows":618,"failures":30},"vinsmono2018-table-ii","Timing statistics per thread on the 5.62 km HKUST campus dataset (hand-held VI-Sensor, 25 Hz images, 200 Hz IMU, 1 h 34 min)",[12216],"HKUST campus (own)",[148,23,38],[40,23],[1606],[251],{"slug":12222,"sourceId":251,"sourceLabel":12202,"sourceYear":107,"table":12223,"note":12224,"datasets":12225,"metrics":12226,"families":12227,"methods":12228,"methodIds":12230,"rows":154,"failures":30},"vinsmono2018-text-sec-ix-a-2","Text Sec. IX-A-2","Five EuRoC MH sequences merged one by one into one global pose graph (first frame fixed); whole trajectory compared with ground truth, about 500 m in…",[1482],[568],[25],[12229],"VINS-Mono with map merging",[251],{"slug":12232,"sourceId":251,"sourceLabel":12202,"sourceYear":107,"table":12233,"note":12234,"datasets":12235,"metrics":12237,"families":12238,"methods":12239,"methodIds":12241,"rows":154,"failures":30},"vinsmono2018-text-sec-ix-c-1","Text Sec. IX-C-1","Closed-loop autonomous flight of a self-developed quadrotor tracking a figure-eight four times with loop closure disabled; OptiTrack ground truth; tot…",[12236],"own MAV flight",[1551],[1553],[12240],"VINS-Mono (loop closure disabled)",[251],{"slug":12243,"sourceId":251,"sourceLabel":12202,"sourceYear":107,"table":12244,"note":12245,"datasets":12246,"metrics":12247,"families":12248,"methods":12249,"methodIds":12251,"rows":154,"failures":30},"vinsmono2018-text-sec-vi-e","Text Sec. VI-E","Motion-only visual-inertial optimization for camera-rate (30 Hz) output; full tightly coupled VIO may take more than 50 ms on embedded computers",[3980],[38],[40],[12250],"VINS-Mono motion-only optimization",[251],{"slug":12253,"sourceId":1513,"sourceLabel":12254,"sourceYear":16,"table":91,"note":12255,"datasets":12256,"metrics":12257,"families":12258,"methods":12259,"methodIds":12264,"rows":1079,"failures":63},"vinsfusion2019-table-i","Qin et al., 2019","EuRoC; RMSE of ATE; trajectories aligned with Horn's method (degrees of freedom not stated); x = stereo-only tracking failed because motion was too ag…",[1482],[568],[25],[12260,12261,12262,12263],"OKVIS (stereo+imu)","Proposed (mono+imu)","Proposed (stereo)","Proposed (stereo+imu)",[1510,1513],{"slug":12266,"sourceId":1513,"sourceLabel":12254,"sourceYear":16,"table":325,"note":12267,"datasets":12268,"metrics":12270,"families":12271,"methods":12272,"methodIds":12273,"rows":52,"failures":30},"vinsfusion2019-table-ii","Hand-held self-built sensor suite walked outdoors; GPS positions of the DJI A3 receiver treated as ground truth; RMSE in metres; alignment not stated…",[12269],"own outdoor sequences",[568],[25],[12261,12262,12263],[1513],{"slug":12275,"sourceId":4783,"sourceLabel":12276,"sourceYear":213,"table":91,"note":12277,"datasets":12278,"metrics":12280,"families":12281,"methods":12282,"methodIds":12291,"rows":2262,"failures":30},"lins2020-table-i","Qin et al., 2020","Relative drift = gap between estimated position and GPS ground truth divided by distance travelled; MRO = map-refined odometry, PO = pure odometry; LI…",[12279],"Own LINS datasets",[23],[23],[12283,12284,12285,12286,12287,12288,12289,12290],"LINS-MRO","LINS-PO","LIOM-MRO","LIOM-PO","LOAM-MRO","LOAM-PO","LeGO-MRO","LeGO-PO",[395,4783,2117,2118],{"slug":12293,"sourceId":4783,"sourceLabel":12276,"sourceYear":213,"table":325,"note":12294,"datasets":12295,"metrics":12296,"families":12297,"methods":12298,"methodIds":12299,"rows":578,"failures":30},"lins2020-table-ii","Mean runtime of the lidar-inertial odometry module per scan",[12279],[38],[40],[4769,4770],[4783,2117],{"slug":12301,"sourceId":12302,"sourceLabel":12303,"sourceYear":374,"table":108,"note":12304,"datasets":12305,"metrics":12308,"families":12309,"methods":12310,"methodIds":12328,"rows":6348,"failures":30},"qin2023geotransformer-table-2","qin2023geotransformer","Qin et al., 2023","3DMatch (overlap above 30%) and 3DLoMatch (10% to 30%) test pairs; registration recall = share of pairs with transformation RMSE below 0.2 m; model ti…",[12306,2578,12307],"3DLoMatch","3DMatch and 3DLoMatch",[38,1933],[40,1935],[12311,12312,12313,12314,12315,12316,12317,12318,12319,12320,12321,12322,12323,12324,12325,12326,12327],"CoFiNet + LGR (all samples)","CoFiNet + RANSAC-50k (5000 samples)","CoFiNet + weighted SVD (250 samples)","D3Feat + RANSAC-50k (5000 samples)","D3Feat + weighted SVD (250 samples)","FCGF + RANSAC-50k (5000 samples)","FCGF + weighted SVD (250 samples)","GeoTransformer (ours) + LGR (all samples)","GeoTransformer (ours) + RANSAC-50k (5000 samples)","GeoTransformer (ours) + weighted SVD (250 samples)","GeoTransformer lite (ours, shared geometric self-attention) + LGR (all samples)","GeoTransformer lite (ours, shared geometric self-attention) + RANSAC-50k (5000 samples)","GeoTransformer lite (ours, shared geometric self-attention) + weighted SVD (250 samples)","Predator + RANSAC-50k (5000 samples)","Predator + weighted SVD (250 samples)","SpinNet + RANSAC-50k (5000 samples)","SpinNet + weighted SVD (250 samples)",[2574,12302],{"slug":12330,"sourceId":12302,"sourceLabel":12303,"sourceYear":374,"table":17,"note":12331,"datasets":12332,"metrics":12333,"families":12334,"methods":12335,"methodIds":12342,"rows":2388,"failures":274},"qin2023geotransformer-table-3","KITTI odometry sequences 8 to 10 for testing, pairs at least 10 m apart, ground truth refined with ICP; RR = share of pairs with RRE below 5 deg and R…",[469],[23,1933],[23,1935],[8999,12336,9000,12337,2631,12338,12339,12340,12341,9012,9017],"CoFiNet","DGR","FMR","GeoTransformer (ours, LGR)","GeoTransformer (ours, RANSAC-50k)","HRegNet",[2574,12302],{"slug":12344,"sourceId":12302,"sourceLabel":12303,"sourceYear":374,"table":244,"note":12345,"datasets":12346,"metrics":12347,"families":12348,"methods":12349,"methodIds":12352,"rows":3819,"failures":30},"qin2023geotransformer-table-5","Augmented ICL-NUIM multiway registration: fragments fused from 50 consecutive RGB-D frames, pairwise registration then global pose-graph optimisation;…",[2939],[568],[25],[12337,8947,12350,12351,4204],"GeoTransformer (ours)","PointDSC",[4183,12302,8957],{"slug":12354,"sourceId":12355,"sourceLabel":12356,"sourceYear":213,"table":4401,"note":12357,"datasets":12358,"metrics":12359,"families":12360,"methods":12361,"methodIds":12363,"rows":356,"failures":30},"ramezani2020newercollege-text-sec-v","ramezani2020newercollege","Ramezani et al., 2020","Standard deviation of ICP ground-truth positions (scan-to-prior-map registration) over the first 10 s while the handheld device was stationary; accura…",[3253],[23],[23],[12362],"ICP localization of Ouster scans against Leica BLK360 prior map (ground-truth generation)",[],{"slug":12365,"sourceId":799,"sourceLabel":12366,"sourceYear":306,"table":325,"note":12367,"datasets":12368,"metrics":12370,"families":12371,"methods":12372,"methodIds":12376,"rows":608,"failures":29},"wildcat2022-table-ii","Ramezani et al., 2022","QCAT surveyed targets (63 in total): point-to-point error between manually picked target centres in each map and surveyed positions after MSAC robust…",[12369],"QCAT (in-house)",[75,23],[78,23],[12373,12374,12375],"FAST-LIO2 [7]","LIO-SAM [3]","Wildcat (ours)",[321,338,799],{"slug":12378,"sourceId":799,"sourceLabel":12366,"sourceYear":306,"table":9112,"note":12379,"datasets":12380,"metrics":12381,"families":12382,"methods":12383,"methodIds":12386,"rows":416,"failures":154},"wildcat2022-text-sec-vi-b","DARPA SubT Final Event prize run, four robots with SpinningPacks: Wildcat map voxelised at 40 cm compared point-wise with the 1 cm DARPA survey map af…",[3702],[3417,76,23],[78,23],[12384,12385],"Wildcat multi-agent map","Wildcat multi-agent map (DARPA scoring)",[799],{"slug":12388,"sourceId":799,"sourceLabel":12366,"sourceYear":306,"table":12389,"note":12390,"datasets":12391,"metrics":12392,"families":12393,"methods":12394,"methodIds":12397,"rows":120,"failures":30},"wildcat2022-text-sec-vi-c","Text Sec.VI-C","MulRan DCC03 (about 5 km urban drive, OS1-64): average RPE of odometry (loop closure disabled) over segment lengths 50 to 500 m computed with evo; APE…",[671],[2411,330],[332],[1437,12395,12396],"LIO-SAM odometry (loop closure disabled)","Wildcat odometry",[321,338,799],{"slug":12399,"sourceId":799,"sourceLabel":12366,"sourceYear":306,"table":870,"note":12400,"datasets":12401,"metrics":12402,"families":12403,"methods":12404,"methodIds":12406,"rows":654,"failures":356},"wildcat2022-text-sec-vi-e","Runtime and memory of Wildcat running online on QCAT SpinningPack",[12369,8984],[148,219,23,38],[40,23],[12405,12396],"Wildcat PGO",[799],{"slug":12408,"sourceId":12409,"sourceLabel":12410,"sourceYear":562,"table":108,"note":12411,"datasets":12412,"metrics":12415,"families":12416,"methods":12417,"methodIds":12424,"rows":2119,"failures":416},"rauch2025rohbau3d-table-2","rauch2025rohbau3d","Rauch & Braml, 2025","Unoriented normal estimation angle RMSE (Eq. 6) on PCPNet (noise levels and density variations) and SceneNN; values partly copied from cited sources (…",[12413,12414],"PCPNet","SceneNN",[23],[23],[12418,12419,12420,12421,12422,12423],"GraphFit [44] (source [35])","HSurf-Net [43] (source [45])","MSECNet [45] (source [45])","PCA (Open3D, authors' own result)","PCPNet [37] (source [45])","SHS-Net [35] (source [35])",[],{"slug":12426,"sourceId":12409,"sourceLabel":12410,"sourceYear":562,"table":17,"note":12427,"datasets":12428,"metrics":12429,"families":12430,"methods":12431,"methodIds":12434,"rows":429,"failures":30},"rauch2025rohbau3d-table-3","Oriented normal estimation angle RMSE (Eq. 6) on PCPNet; values from source [35] except PCA (*) by the authors with Open3D tangent-plane orientation (…",[12413],[23],[23],[12432,12421,12433,12423],"HSurf-Net + ODP [43] (source [35])","PCPNet [37] (source [35])",[],{"slug":12436,"sourceId":12409,"sourceLabel":12410,"sourceYear":562,"table":12437,"note":12438,"datasets":12439,"metrics":12441,"families":12442,"methods":12443,"methodIds":12445,"rows":274,"failures":30},"rauch2025rohbau3d-text-methods","Text Methods","Dataset statistics over all 504 FARO Focus M70 scans; densities from nearest neighbours in a 10 cm radius sphere.",[12440],"Rohbau3D",[23],[23],[12444],"FARO Focus M70 single-station TLS scans",[],{"slug":12447,"sourceId":12448,"sourceLabel":12449,"sourceYear":681,"table":279,"note":12450,"datasets":12451,"metrics":12453,"families":12454,"methods":12455,"methodIds":12460,"rows":2119,"failures":618},"razlaw2015evaluation-table-iii","razlaw2015evaluation","Razlaw et al., 2015","Final evaluation after Hyperopt parameter optimization on the MoCap training set; all methods use incremental registration against a multiresolution s…",[12452],"Razlaw et al. MAV laser datasets",[568,3417,23,38],[25,40,78,23],[12456,522,12457,11699,12458,12459],"GICP","Mesh","Surfel","VO",[478,3292],{"slug":12462,"sourceId":12463,"sourceLabel":12464,"sourceYear":345,"table":108,"note":12465,"datasets":12466,"metrics":12468,"families":12469,"methods":12470,"methodIds":12472,"rows":416,"failures":30},"rebolj2017pcqualityscanvsbim-table-2","rebolj2017pcqualityscanvsbim","Rebolj et al., 2017","Criteria for correct Scan-vs-BIM identification from 108 simulated clouds; Dc is the worst-case (maximum) border density per class",[12467],"108 simulated point clouds of a 100-element experimental BIM (HeliOS)",[23],[23],[12471],"Proposed point cloud quality criteria",[12463],{"slug":12474,"sourceId":12463,"sourceLabel":12464,"sourceYear":345,"table":17,"note":12475,"datasets":12476,"metrics":12478,"families":12479,"methods":12480,"methodIds":12482,"rows":29,"failures":30},"rebolj2017pcqualityscanvsbim-table-3","Kinect 2 distances and minimum element side meeting Dc and Ac; clear view, camera perpendicular to element centre; primed values corrected to the 3 m…",[12477],"Kinect 2 specifications",[23],[23],[12481],"Kinect 2 (proposed relations, Sec. 5.1)",[12463],{"slug":12484,"sourceId":12463,"sourceLabel":12464,"sourceYear":345,"table":3048,"note":12485,"datasets":12486,"metrics":12488,"families":12489,"methods":12490,"methodIds":12492,"rows":416,"failures":63},"rebolj2017pcqualityscanvsbim-text-sec-4-1","Photogrammetry validation: 146 synthetic 1280x720 images of the simulated 4D AB BIM, VisualSFM without calibration corrections",[12487],"simulated 4D AB BIM (synthetic images)",[23],[23],[12491],"Photogrammetry (VisualSFM)",[],{"slug":12494,"sourceId":12463,"sourceLabel":12464,"sourceYear":345,"table":3060,"note":12495,"datasets":12496,"metrics":12497,"families":12498,"methods":12499,"methodIds":12501,"rows":578,"failures":416},"rebolj2017pcqualityscanvsbim-text-sec-4-2","Videogrammetry validation: 241 synthetic 1280x960 frames of the simulated 4D AB BIM, VisualSFM with calibration and distortion compensation",[12487],[23],[23],[12500],"Videogrammetry (VisualSFM)",[],{"slug":12503,"sourceId":12463,"sourceLabel":12464,"sourceYear":345,"table":7969,"note":12504,"datasets":12505,"metrics":12507,"families":12508,"methods":12509,"methodIds":12511,"rows":654,"failures":356},"rebolj2017pcqualityscanvsbim-text-sec-4-3","Range-image validation: Kinect 2 scan of part of a real building, 18-element BIM (3 L, 6 M, 5 S, 4 XS; 1 M, 2 S, 2 XS missing), three partial sequence…",[12506],"real building part scanned with Kinect 2",[23],[23],[12510],"Kinect 2 range imaging",[],{"slug":12513,"sourceId":12463,"sourceLabel":12464,"sourceYear":345,"table":5342,"note":12514,"datasets":12515,"metrics":12517,"families":12518,"methods":12519,"methodIds":12520,"rows":154,"failures":154},"rebolj2017pcqualityscanvsbim-text-sec-4-4","Across the three validation experiments",[12516],"photogrammetry, videogrammetry and Kinect 2 validation clouds",[23],[23],[12471],[12463],{"slug":12522,"sourceId":12523,"sourceLabel":12524,"sourceYear":4828,"table":12525,"note":12526,"datasets":12527,"metrics":12529,"families":12530,"methods":12531,"methodIds":12537,"rows":578,"failures":30},"rehder2016spatiotemporal-fig-9-table","rehder2016spatiotemporal","Rehder et al., 2016","Fig. 9 table","Camera-IMU temporal offset vs exposure time, 40 hand-guided runs in 4 exposure series (Setup I, hardware sync); slope of best-fit line (theory 0.5) an…",[12528],"Setup I, 40 runs",[23],[23],[12532,12533,12534,12535,12536],"A (accelerometer only)","G (gyroscopes only)","J (joint estimation)","S (separated estimation, Mair et al.)","T (TD-ICP, Kelly et al.)",[12523],{"slug":12539,"sourceId":12523,"sourceLabel":12524,"sourceYear":4828,"table":279,"note":12540,"datasets":12541,"metrics":12543,"families":12544,"methods":12545,"methodIds":12549,"rows":598,"failures":30},"rehder2016spatiotemporal-table-iii","LRF spatiotemporal calibration over 30 one-minute Setup II runs with simulated offsets -5, 0, +5 ms; hand-measured reference displacement [69, -42, -6…",[12542],"Setup II, 30 runs",[23],[23],[12546,12547,12548],"estimator C, software synchronized","estimator L, hardware synchronized","estimator L, software synchronized",[12523],{"slug":12551,"sourceId":12523,"sourceLabel":12524,"sourceYear":4828,"table":1278,"note":12552,"datasets":12553,"metrics":12554,"families":12555,"methods":12556,"methodIds":12558,"rows":224,"failures":30},"rehder2016spatiotemporal-text-sec-iv-c","Simulation of estimator J: 500 runs of 90 s, delays -8 to 8 ms; true displacement [103, -15, -10] mm and 180 deg rotation about the optical axis",[4430],[23],[23],[12557],"estimator J",[12523],{"slug":12560,"sourceId":12523,"sourceLabel":12524,"sourceYear":4828,"table":5442,"note":12561,"datasets":12562,"metrics":12563,"families":12564,"methods":12565,"methodIds":12566,"rows":120,"failures":30},"rehder2016spatiotemporal-text-sec-iv-d","Spread of camera-IMU spatial calibration over all 40 Setup I runs (hardware sync); means [74.54, -8.68, 12.39] mm and [180.753, 0.178, -0.165] deg not…",[12528],[23],[23],[12557],[12523],{"slug":12568,"sourceId":12523,"sourceLabel":12524,"sourceYear":4828,"table":5451,"note":12569,"datasets":12570,"metrics":12571,"families":12572,"methods":12573,"methodIds":12576,"rows":63,"failures":30},"rehder2016spatiotemporal-text-sec-iv-e","Repeatability of LRF orientation: square root of variance w.r.t. the Frechet expectation over all Setup II runs",[12542],[23],[23],[12574,12575],"estimator C","estimator L",[12523],{"slug":12578,"sourceId":12523,"sourceLabel":12524,"sourceYear":4828,"table":5461,"note":12579,"datasets":12580,"metrics":12582,"families":12583,"methods":12584,"methodIds":12585,"rows":63,"failures":30},"rehder2016spatiotemporal-text-sec-iv-f","Convergence study: 100 perturbed 30-s chunks of Setup II data; J success if offset error \u003C 100 us, 5 mm, 0.5 deg; L success if \u003C 2.0 ms, 10 mm, 1 deg…",[12581],"Setup II",[1933],[1935],[12557,12575],[12523],{"slug":12587,"sourceId":12523,"sourceLabel":12524,"sourceYear":4828,"table":12588,"note":12589,"datasets":12590,"metrics":12591,"families":12592,"methods":12593,"methodIds":12595,"rows":654,"failures":30},"rehder2016spatiotemporal-text-sec-iv-g","Text Sec. IV-G","Estimator L repeated on the Setup II dataset with range timestamps assigned on arrival (no jitter correction)",[12542],[23],[23],[12594],"estimator L, timestamps on arrival",[12523],{"slug":12597,"sourceId":12598,"sourceLabel":12599,"sourceYear":213,"table":91,"note":12600,"datasets":12601,"metrics":12603,"families":12604,"methods":12605,"methodIds":12609,"rows":2882,"failures":356},"voxgraph2020-table-i","voxgraph2020","Reijgwart et al., 2020","Four MAV flights (about 400 m each) at the Wangen an der Aare search and rescue training site; RTK-GNSS ground truth; each system run 10 times and ave…",[12602],"Voxgraph MAV field dataset (this paper)",[568,113],[25,40],[12606,12607,12608,629],"Loam","ROVIO (odometry input to Voxgraph)","Vins-Mono",[450,251,12598],{"slug":12611,"sourceId":12598,"sourceLabel":12599,"sourceYear":213,"table":12612,"note":12613,"datasets":12614,"metrics":12615,"families":12616,"methods":12617,"methodIds":12619,"rows":63,"failures":154},"voxgraph2020-text-sec-viii-b1","Text Sec.VIII-B1","Global optimization time over 10 trials of each of 4 trajectories; text gives only the maximum",[12602],[113,23],[40,23],[629,12618],"Voxgraph (global optimization only)",[12598],{"slug":12621,"sourceId":12622,"sourceLabel":12623,"sourceYear":306,"table":325,"note":12624,"datasets":12625,"metrics":12627,"families":12628,"methods":12629,"methodIds":12634,"rows":618,"failures":30},"locus2-2022-table-ii","locus2_2022","Reinke et al., 2022","Relative memory and CPU change of sliding-window map structures versus the LOCUS 1.0 static octree with 0.001 m leaf (baseline), 50 m map window, GICP…",[12626],"NeBula odometry dataset (DARPA SubT, Team CoSTAR)",[113,219],[40],[12630,12631,12632,12633],"LOCUS 2.0 with ikd-tree","LOCUS 2.0 with mto 0.001 (multi-threaded octree, leaf 0.001 m)","LOCUS 2.0 with mto 0.01","LOCUS 2.0 with mto 0.1",[12622],{"slug":12636,"sourceId":12622,"sourceLabel":12623,"sourceYear":306,"table":279,"note":12637,"datasets":12638,"metrics":12639,"families":12640,"methods":12641,"methodIds":12643,"rows":339,"failures":356},"locus2-2022-table-iii","Underground datasets A, C, F, H, I, J (Table I); LOCUS 2.0 versus FAST-LIO and LINS; column labels reproduced as printed (APE max [m], APE mean [%], C…",[12626],[329,113,219],[25,40],[12642,4769,6435],"FAST-LIO",[1476,4783,12622],{"slug":12645,"sourceId":12622,"sourceLabel":12623,"sourceYear":306,"table":12646,"note":12647,"datasets":12648,"metrics":12649,"families":12650,"methods":12651,"methodIds":12653,"rows":356,"failures":30},"locus2-2022-text-sec-iv-c1","Text Sec. IV-C1","GICP from normals versus standard GICP inside LOCUS 2.0, averaged over datasets A-J and 5 runs each; percentage changes stated in text",[12626],[329,148,23],[25,40,23],[12652],"LOCUS 2.0, GICP from normals vs GICP",[12622],{"slug":12655,"sourceId":12622,"sourceLabel":12623,"sourceYear":306,"table":12656,"note":12657,"datasets":12658,"metrics":12659,"families":12660,"methods":12661,"methodIds":12662,"rows":63,"failures":30},"locus2-2022-text-sec-iv-d1","Text Sec. IV-D1","Map structure operation timing relative to the octree",[12626],[23],[23],[12630],[12622],{"slug":12664,"sourceId":12665,"sourceLabel":12666,"sourceYear":213,"table":91,"note":12667,"datasets":12668,"metrics":12670,"families":12671,"methods":12672,"methodIds":12678,"rows":12679,"failures":849},"rogers2020subttunnel-table-i","rogers2020subttunnel","Rogers et al., 2020","Artifact-based absolute mapping score on SubT-Tunnel: SLAM map aligned to the surveyed darpa frame by Umeyama on >=3 surveyed AprilTags (stereo depth)…",[12669],"SubT-Tunnel",[75,1933],[78,1935],[4759,12673,12674,12675,12676,12677],"Cartographer 2D (artifacts and detections projected to X-Y plane)","ORB SLAM2","ORB SLAM2+ (modified recovery: continue from last pose when tracking is lost)","Odometry (wheel odometry with IMU orientation, no mapping)","OmniMapper",[2116,1511],75,{"slug":12681,"sourceId":1508,"sourceLabel":12682,"sourceYear":213,"table":325,"note":12683,"datasets":12684,"metrics":12685,"families":12686,"methods":12687,"methodIds":12693,"rows":12694,"failures":274},"kimera2020-table-ii","Rosinol et al., 2020","EuRoC ATE RMSE grouped as fixed-lag smoothing, full smoothing and PGO with loop closure; comparator values taken from Delmerico and Scaramuzza [77] (S…",[743],[568],[25],[12688,12689,12690,4734,2803,2805,12691,12692,1606],"Kimera-RPGO (loop closure)","Kimera-VIO (fixed-lag smoothing)","Kimera-VIO (full smoothing)","SVO-GTSAM (full smoothing)","VINS-LC (loop closure)",[1508,1509,1510,251],99,{"slug":12696,"sourceId":1508,"sourceLabel":12682,"sourceYear":213,"table":279,"note":12697,"datasets":12698,"metrics":12699,"families":12700,"methods":12701,"methodIds":12704,"rows":578,"failures":30},"kimera2020-table-iii","EuRoC V1_01 ATE RMSE versus DBoW2 loop-closure threshold alpha; smaller alpha gives more but less conservative loop closures",[743],[568],[25],[12702,12703],"Kimera ablation: PGO w\u002Fo PCM","Kimera-RPGO",[1508],{"slug":12706,"sourceId":1508,"sourceLabel":12682,"sourceYear":213,"table":731,"note":12707,"datasets":12708,"metrics":12710,"families":12711,"methods":12712,"methodIds":12715,"rows":224,"failures":30},"kimera2020-table-iv","Mesh evaluated against the EuRoC ground-truth point cloud: mesh sampled at 10^3 points per m2, registered by rigid ICP in CloudCompare (ICP threshold…",[12709],"EuRoC MAV (V1, V2 ground-truth point cloud)",[76],[78],[12713,12714],"Kimera-Mesher multi-frame mesh","Kimera-Semantics global TSDF mesh",[1508],{"slug":12717,"sourceId":1508,"sourceLabel":12682,"sourceYear":213,"table":818,"note":12718,"datasets":12719,"metrics":12721,"families":12722,"methods":12723,"methodIds":12727,"rows":224,"failures":30},"kimera2020-table-v","Unity-based photo-realistic simulator (MIT Lincoln Lab) with ground-truth 2D semantics; mesh registered to ground truth and point RMSE computed as in…",[12720],"photo-realistic simulator (MIT Lincoln Lab)",[329,75,23],[25,78,23],[12724,12725,12726],"Kimera-Semantics with GT depth and GT poses","Kimera-Semantics with GT depth and Kimera-VIO poses","Kimera-Semantics with dense stereo and Kimera-VIO poses",[1508],{"slug":12729,"sourceId":1508,"sourceLabel":12682,"sourceYear":213,"table":10792,"note":12730,"datasets":12731,"metrics":12733,"families":12734,"methods":12735,"methodIds":12742,"rows":618,"failures":63},"kimera2020-text-sec-iii-d","Module timings stated in Sec. III-D text (Fig. 5 shows distributions); the text ties only the Kimera-RPGO average to EuRoC and the Kimera-Semantics ti…",[743,12732,12720],"not_reported (Fig. 5 runtime breakdown)",[38],[40],[12713,12736,12703,12737,12738,12739,12740,12741],"Kimera-Mesher per-frame mesh","Kimera-Semantics global mesh update per keyframe (720x480 depth from simulator)","Kimera-VIO IMU front-end (preintegration)","Kimera-VIO back-end factor-graph optimisation","Kimera-VIO vision front-end at keyframes (detection, stereo matching, verification)","Kimera-VIO vision front-end, feature tracking per frame",[1508],{"slug":12744,"sourceId":12745,"sourceLabel":12746,"sourceYear":374,"table":91,"note":12747,"datasets":12748,"metrics":12749,"families":12750,"methods":12751,"methodIds":12758,"rows":6820,"failures":30},"nerfslam2023-table-i","nerfslam2023","Rosinol et al., 2023","Replica rendered sequences (2000 frames per scene from iMAP); iMAP* and NICE-SLAM (GT depth) use rendered ground-truth depth; TSDF-Fusion, sigma-Fusio…",[3223],[23],[23],[12752,12753,12754,12755,12756,12757],"Nice-SLAM [28] (GT depth)","Nice-SLAM [28] (no depth)","Ours (our depth)","TSDF-Fusion Res. = 256 (our depth)","iMAP* [27] (GT depth)","sigma-Fusion [15] Res. = 256 (our depth)",[6378,3184],{"slug":12760,"sourceId":12745,"sourceLabel":12746,"sourceYear":374,"table":5451,"note":12761,"datasets":12762,"metrics":12763,"families":12764,"methods":12765,"methodIds":12767,"rows":356,"failures":30},"nerfslam2023-text-sec-iv-e","Real-time performance at 640x480 on one RTX 2080 Ti shared by tracking and mapping; the section states both 12 FPS and 10 FPS for the pipeline",[2070],[148],[40],[12766],"Ours (NeRF-SLAM)",[],{"slug":12769,"sourceId":12745,"sourceLabel":12746,"sourceYear":374,"table":4401,"note":12770,"datasets":12771,"metrics":12772,"families":12773,"methods":12774,"methodIds":12775,"rows":154,"failures":30},"nerfslam2023-text-sec-v","GPU memory needed to operate (dense correlation volumes plus hierarchical grids)",[2070],[219],[40],[12766],[],{"slug":12777,"sourceId":12778,"sourceLabel":12779,"sourceYear":213,"table":91,"note":12780,"datasets":12781,"metrics":12782,"families":12783,"methods":12784,"methodIds":12789,"rows":721,"failures":30},"lol2020-table-i","lol2020","Rozenberszki & Majdik, 2020","Numbers of filtered-out, true-positive and false-positive SegMap matches for minimum cluster sizes 2-5 on three KITTI raw drives; validity judged agai…",[4342],[23],[23],[12785,12786,12787,12788],"LOL filtering, minimum cluster size 2","LOL filtering, minimum cluster size 3","LOL filtering, minimum cluster size 4","LOL filtering, minimum cluster size 5",[12778],{"slug":12791,"sourceId":12778,"sourceLabel":12779,"sourceYear":213,"table":454,"note":12792,"datasets":12793,"metrics":12794,"families":12795,"methods":12796,"methodIds":12802,"rows":416,"failures":30},"lol2020-text-sec-iv","Mean (standard deviation) processing time of each module",[4342],[38],[40],[12797,12798,12799,12800,12801],"LOL, ICP alignment","LOL, additional RANSAC geometric filtering","LOL, description","LOL, match recognition in the reduced candidate pool","LOL, segmentation",[12778],{"slug":12804,"sourceId":11635,"sourceLabel":12805,"sourceYear":374,"table":91,"note":12806,"datasets":12807,"metrics":12809,"families":12810,"methods":12811,"methodIds":12815,"rows":52,"failures":30},"ruan2023slamesh-table-i","Ruan et al., 2023","Mai City (CARLA simulation, simulated HDL-64E, 99 m, 100 frames); each pipeline used its own estimated poses (A-LOAM for Voxblox); meshes densely samp…",[12808],"Mai City",[74,23],[78,23],[12812,12813,12814],"Puma","SLAMesh (Ours)","Voxblox+A-LOAM",[11635,3264],{"slug":12817,"sourceId":11635,"sourceLabel":12805,"sourceYear":374,"table":325,"note":12818,"datasets":12819,"metrics":12820,"families":12821,"methods":12822,"methodIds":12829,"rows":9622,"failures":154},"ruan2023slamesh-table-ii","KITTI odometry 00-10, relative translation error; baseline values imported from their published papers (not rerun); SLAMesh without loop closure; w\u002Fo…",[469],[438,439],[441],[4330,461,12823,12812,12824,12825,12826,12827,12828],"Litamin2","SLAMesh (Ours) Full","SLAMesh w\u002Fo Comb.","SLAMesh w\u002Fo P2Mesh","Suma","Suma++",[392,2118,11635,432,1968,3264],{"slug":12831,"sourceId":11635,"sourceLabel":12805,"sourceYear":374,"table":5442,"note":12832,"datasets":12833,"metrics":12834,"families":12835,"methods":12836,"methodIds":12839,"rows":274,"failures":30},"ruan2023slamesh-text-sec-iv-d","KITTI 07 (10 Hz LiDAR), per-frame time stated in text (Fig. 7 plot not digitised)",[469],[148,38],[40],[12837,12812,12838],"A-LOAM+Voxblox","SLAMesh",[11635,3264],{"slug":12841,"sourceId":11635,"sourceLabel":12805,"sourceYear":374,"table":5451,"note":12842,"datasets":12843,"metrics":12844,"families":12845,"methods":12846,"methodIds":12849,"rows":356,"failures":30},"ruan2023slamesh-text-sec-iv-e","Ablation of efficiency, per-scan processing time stated in text; the text does not name the dataset for these statements (Fig. 8 and the cell-size rem…",[2070],[438,148,38],[40,441],[12838,12847,12848],"SLAMesh (cell 3 m)","SLAMesh w\u002Fo Thread.",[11635],{"slug":12851,"sourceId":3310,"sourceLabel":12852,"sourceYear":12853,"table":12854,"note":12855,"datasets":12856,"metrics":12859,"families":12860,"methods":12861,"methodIds":12866,"rows":356,"failures":356},"rusinkiewicz2001variants-text-sec-3-2-and-4","Rusinkiewicz & Levoy, 2001",2001,"Text Sec. 3.2 and 4","timings stated in the text for the synthetic fractal scene and a real scan pair; C++ on a 550 MHz Pentium III Xeon",[12857,12858],"elephant figurine scans (prototype structured-light scanner)","synthetic fractal landscape meshes",[23,38],[40,23],[12862,12863,12864,12865],"baseline ICP (after Pulli 99)","closest-point and normal-shooting matching variants","optimised high-speed ICP","optimised high-speed ICP (projection matching, point-to-plane, random sampling, constant weights, distance threshold)",[3310],{"slug":12868,"sourceId":12869,"sourceLabel":12870,"sourceYear":16,"table":12871,"note":12872,"datasets":12873,"metrics":12875,"families":12876,"methods":12877,"methodIds":12886,"rows":1991,"failures":30},"rusinkiewicz2019symmetric-fig-5","rusinkiewicz2019symmetric","Rusinkiewicz, 2019","Fig. 5","Numerals printed in Fig. 5 heatmap cells: % of 1000 random initial transforms (given rotation about a random axis, translation as fraction of mesh siz…",[12874],"bunny range scans (Turk and Levoy 1994)",[1933],[1935],[12878,12879,12880,12881,12882,12883,12884,12885],"LM-Point-to-plane (Fitzgibbon 2001)","LM-Symmetric","Point-to-plane","Point-to-point","Quadratic (Mitra et al. 2004, on-demand)","Symmetric","Symmetric-RN","Two-plane",[478,1944,12869],{"slug":12888,"sourceId":1788,"sourceLabel":12889,"sourceYear":1157,"table":91,"note":12890,"datasets":12891,"metrics":12893,"families":12894,"methods":12895,"methodIds":12899,"rows":52,"failures":63},"rusu2009fpfh-table-i","Rusu et al., 2009","Initial alignment of two overlapping Ljubljana urban outdoor datasets (about 45% overlap); greedy initial alignment (GIA) runs on downsampled data; SA…",[12892],"Ljubljana outdoor dataset (from ref. [2])",[23,38],[40,23],[12896,12897,12898],"GIA - run 1 (Greedy Initial Alignment)","GIA - run 2 (Greedy Initial Alignment)","SAC-IA",[1788],{"slug":12901,"sourceId":12902,"sourceLabel":12903,"sourceYear":213,"table":108,"note":12904,"datasets":12905,"metrics":12907,"families":12908,"methods":12909,"methodIds":12913,"rows":274,"failures":30},"salgues2020mmsindoor-table-2","salgues2020mmsindoor","Salgues et al., 2020","Standard deviation of points around a plane fitted in Trimble RealWorks to a smooth wall portion of about 1 x 2 m in each cloud (INSA laboratory).",[12906],"Strasbourg dataset 3",[1830],[78],[12910,12911,12912],"FARO Focus 3D (TLS reference)","GeoSLAM ZEB-REVO RT","GreenValley LiBackPack C50",[],{"slug":12915,"sourceId":12902,"sourceLabel":12903,"sourceYear":213,"table":6714,"note":12916,"datasets":12917,"metrics":12919,"families":12920,"methods":12921,"methodIds":12922,"rows":654,"failures":30},"salgues2020mmsindoor-text-sec-5-1","Tower of Ponts Couverts, whole edifice; M3C2 (CloudCompare) between ZEB-REVO RT and FARO TLS after 1 cm resampling, manual point picking and ICP; abou…",[12918],"Strasbourg dataset 1",[3919,23],[78,23],[12911],[],{"slug":12924,"sourceId":12902,"sourceLabel":12903,"sourceYear":213,"table":2391,"note":12925,"datasets":12926,"metrics":12928,"families":12929,"methods":12930,"methodIds":12931,"rows":52,"failures":30},"salgues2020mmsindoor-text-sec-5-2","Zoological Museum; M3C2 between ZEB-REVO RT and FARO TLS after manual point picking and ICP; shares computed after outlier removal except where stated…",[12927],"Strasbourg dataset 2",[23],[23],[12911],[],{"slug":12933,"sourceId":12902,"sourceLabel":12903,"sourceYear":213,"table":1143,"note":12934,"datasets":12935,"metrics":12936,"families":12937,"methods":12938,"methodIds":12939,"rows":224,"failures":30},"salgues2020mmsindoor-text-sec-5-3","INSA topography laboratory; comparisons restricted to a pillar portion of about 1 m3 because low MMS density prevented a proper floor comparison; M3C2…",[12906],[3919,23],[78,23],[12911,12912],[],{"slug":12941,"sourceId":12902,"sourceLabel":12903,"sourceYear":213,"table":2984,"note":12942,"datasets":12943,"metrics":12944,"families":12945,"methods":12946,"methodIds":12947,"rows":274,"failures":30},"salgues2020mmsindoor-text-sec-6-2","Correlation rate between X and Y coordinates of points on the segmented wall face after orthogonal regression; higher means less scatter.",[12906],[23],[23],[12910,12911,12912],[],{"slug":12949,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":12952,"note":12953,"datasets":12954,"metrics":12956,"families":12957,"methods":12958,"methodIds":12959,"rows":416,"failures":30},"sammartano2018zeb-table-12","sammartano2018zeb","Sammartano & Spanò, 2018","Table 12","Courtyard (C): optimised 12 x 7 m wall segment, ZEB1 (450,000 points) vs TLS LiDAR DSM (5,600,000 points). Second bin printed as '0.10 \u003C error \u003C 0.05…",[12955],"Valperga castle",[3417,23],[78,23],[10193],[1180],{"slug":12961,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":12962,"note":12963,"datasets":12964,"metrics":12965,"families":12966,"methods":12967,"methodIds":12969,"rows":618,"failures":30},"sammartano2018zeb-table-13","Table 13","Raw cloud-to-cloud alignment of roundtrip ZEB scans started from the courtyard onto the TLS LiDAR DSM, before per-cloud optimisation.",[12955],[3417],[78],[12968,10193],"ZEB (device not stated for this volume)",[1180],{"slug":12971,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":12972,"note":12973,"datasets":12974,"metrics":12976,"families":12977,"methods":12978,"methodIds":12980,"rows":120,"failures":30},"sammartano2018zeb-table-16","Table 16","Fortified village (E), 2016 circular loop (about 660 m, about 6000 m2) vs UAV DSM; strategy I = ICP-like alignment of the whole cloud; strategy II = l…",[12975],"San Silvestro archaeomining park",[3417,23],[78,23],[12979],"ZEB-REVO",[],{"slug":12982,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":12983,"note":12984,"datasets":12985,"metrics":12986,"families":12987,"methods":12988,"methodIds":12989,"rows":274,"failures":30},"sammartano2018zeb-table-17","Table 17","Church area of the 2016 village loop vs UAV DSM; strategy I = ICP-like whole cloud; strategy II = local segment aligned by matching points.",[12975],[3417,23],[78,23],[12979],[],{"slug":12991,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":12992,"note":12993,"datasets":12994,"metrics":12995,"families":12996,"methods":12997,"methodIds":12999,"rows":356,"failures":30},"sammartano2018zeb-table-18","Table 18","Lower-village sample from the 2017 roundtrip dataset vs UAV DSM and vs TLS.",[12975],[3417],[78],[12998],"ZEB (2017 dataset; device not stated)",[],{"slug":13001,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":13002,"note":13003,"datasets":13004,"metrics":13005,"families":13006,"methods":13007,"methodIds":13009,"rows":356,"failures":30},"sammartano2018zeb-table-19","Table 19","Climbing footpath with steep steps (2017 dataset) vs UAV DSM, before and after optimisation.",[12975],[3417],[78],[12998,13008],"ZEB (2017 dataset; device not stated), optimised",[],{"slug":13011,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":33,"note":13012,"datasets":13013,"metrics":13014,"families":13015,"methods":13016,"methodIds":13018,"rows":356,"failures":30},"sammartano2018zeb-table-4","Tower (A), outward vs return ZEB1 surfaces separated by trajectory time; raw vs optimised (segmentation, outlier cleaning, noise filtering).",[12955],[3417],[78],[10193,13017],"ZEB1 (optimised)",[1180],{"slug":13020,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":244,"note":13021,"datasets":13022,"metrics":13023,"families":13024,"methods":13025,"methodIds":13026,"rows":578,"failures":30},"sammartano2018zeb-table-5","Tower (A), ZEB1 surfaces vs CRP reference model (about 1 cm accuracy), cloud-to-cloud best-fitting alignment.",[12955],[3417],[78],[10193,13017],[1180],{"slug":13028,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":1072,"note":13029,"datasets":13030,"metrics":13031,"families":13032,"methods":13033,"methodIds":13034,"rows":356,"failures":30},"sammartano2018zeb-table-6","Ice house (B), outward vs return ZEB1 surfaces (roundtrip from the courtyard); raw vs optimised.",[12955],[3417],[78],[10193,13017],[1180],{"slug":13036,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":621,"note":13037,"datasets":13038,"metrics":13039,"families":13040,"methods":13041,"methodIds":13042,"rows":618,"failures":30},"sammartano2018zeb-table-7","Ice house (B), share of outward vs return deviation errors per range; raw vs optimised.",[12955],[23],[23],[10193,13017],[1180],{"slug":13044,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":633,"note":13045,"datasets":13046,"metrics":13047,"families":13048,"methods":13049,"methodIds":13053,"rows":618,"failures":30},"sammartano2018zeb-table-8","Mining cave (D) Buca della Faina, outward vs return ZEB-REVO surfaces through successive optimisation steps (cleaning in\u002Fout, filtering in\u002Fout, cave i…",[12975],[3417],[78],[12979,13050,13051,13052],"ZEB-REVO (cleaned)","ZEB-REVO (filtered)","ZEB-REVO (filtered, interior only)",[],{"slug":13055,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":644,"note":13056,"datasets":13057,"metrics":13058,"families":13059,"methods":13060,"methodIds":13061,"rows":52,"failures":30},"sammartano2018zeb-table-9","Mining cave (D), share of outward vs return deviation errors per range across optimisation steps.",[12975],[23],[23],[12979,13051,13052],[],{"slug":13063,"sourceId":12950,"sourceLabel":12951,"sourceYear":107,"table":13064,"note":13065,"datasets":13066,"metrics":13067,"families":13068,"methods":13069,"methodIds":13070,"rows":654,"failures":30},"sammartano2018zeb-text-sec-metric-validation","Text Sec.Metric validation","Values stated in the text of the validation sections.",[12975,12955],[1551,23],[1553,23],[12998,12979,10193,13017],[1180],{"slug":13072,"sourceId":3185,"sourceLabel":13073,"sourceYear":374,"table":13074,"note":13075,"datasets":13076,"metrics":13077,"families":13078,"methods":13079,"methodIds":13084,"rows":800,"failures":30},"pointslam2023-fig-3a","Sandström et al., 2023","Fig. 3a","Replica reconstruction (table inside Fig. 3a), average over 8 scenes and 3 runs; meshes from TSDF fusion of rendered depth at 1 cm voxels, aligned to…",[3223],[74,23],[78,23],[13080,13081,13082,13083],"ESLAM [29]","NICE-SLAM [81]","Point-SLAM (ours)","Vox-Fusion* [71] (re-run)",[3182,3184,3185],{"slug":13086,"sourceId":3185,"sourceLabel":13073,"sourceYear":374,"table":69,"note":13087,"datasets":13088,"metrics":13089,"families":13090,"methods":13091,"methodIds":13093,"rows":3547,"failures":30},"pointslam2023-table-1","Replica tracking, ATE RMSE in cm after Horn closed-form alignment (App. D); average of 3 runs except Vox-Fusion paper values (grayed, single run the a…",[3223],[568],[25],[13080,13081,13082,13092,13083],"Vox-Fusion [71]",[3182,3184,3185],{"slug":13095,"sourceId":3185,"sourceLabel":13073,"sourceYear":374,"table":17,"note":13096,"datasets":13097,"metrics":13098,"families":13099,"methods":13100,"methodIds":13106,"rows":1069,"failures":120},"pointslam2023-table-3","TUM-RGBD tracking, ATE RMSE in cm; values in parentheses are averages over successful runs only; N\u002FA not available; ground truth from an external moti…",[6663],[568],[25],[13101,13102,13103,13104,13081,13105,13082,13083],"BAD-SLAM [50]","DI-Fusion [19]","ElasticFusion [67]","Kintinuous [68]","ORB-SLAM2 [35]",[9325,2915,2448,3184,1511,3185],{"slug":13108,"sourceId":3185,"sourceLabel":13073,"sourceYear":374,"table":33,"note":13109,"datasets":13110,"metrics":13111,"families":13112,"methods":13113,"methodIds":13114,"rows":1339,"failures":63},"pointslam2023-table-4","ScanNet tracking on trajectory 00 of each scene, ATE RMSE in cm; NICE-SLAM values taken from ESLAM; one Vox-Fusion* run failed on scene 0000, parenthe…",[2963],[568],[25],[13102,13081,13082,13092,13083],[3184,3185],{"slug":13116,"sourceId":3185,"sourceLabel":13073,"sourceYear":374,"table":1072,"note":13117,"datasets":13118,"metrics":13119,"families":13120,"methods":13121,"methodIds":13122,"rows":102,"failures":30},"pointslam2023-table-6","Runtime and memory on Replica office 0; tracking and mapping times per iteration and per frame; runtimes profiled on a single RTX 2080 Ti except Vox-F…",[3223],[219,23,38],[40,23],[13081,13082,13092],[3184,3185],{"slug":13124,"sourceId":13125,"sourceLabel":13126,"sourceYear":306,"table":7969,"note":13127,"datasets":13128,"metrics":13131,"families":13132,"methods":13133,"methodIds":13135,"rows":618,"failures":30},"schaub2022pc2bim-text-sec-4-3","schaub2022pc2bim","Schaub et al., 2022","Localization error = registration-derived initial sensor position vs manually measured initial position in the BIM frame, evaluated at every second ke…",[13129,13130],"TU Wien hallway (test environment 1)","TU Wien library, 6th floor (test environment 2)",[23,1933],[23,1935],[13134],"Kudan SLAM + point cloud to BIM registration (proposed)",[13125],{"slug":13137,"sourceId":13125,"sourceLabel":13126,"sourceYear":306,"table":952,"note":13138,"datasets":13139,"metrics":13140,"families":13141,"methods":13142,"methodIds":13143,"rows":154,"failures":30},"schaub2022pc2bim-text-sec-5","Best interval in env. 2: after keyframe 20 (about 25 m) and before keyframe 38 (about 60 m); average values.",[13130],[23],[23],[13134],[13125],{"slug":13145,"sourceId":8912,"sourceLabel":13146,"sourceYear":107,"table":91,"note":13147,"datasets":13148,"metrics":13155,"families":13156,"methods":13157,"methodIds":13160,"rows":608,"failures":356},"schauer2018peopleremover-table-i","Schauer & Nuchter, 2018","Best-parameter F1 and full-pipeline single-threaded runtime; Underwood et al. run on all scan pairs; no clustering or sub-voxel step; sim: 387,838 poi…",[13149,13150,13151,13152,13153,13154],"campus (own, Riegl VZ-400)","carpark (Underwood et al.)","lab (Underwood et al.)","lecturehall (own, Riegl VZ-400)","sim (Underwood et al.)","wrzburg (own, Riegl VZ-400)",[23],[23],[13158,13159],"3dtk (peopleremover)","underwood",[8912],{"slug":13162,"sourceId":8912,"sourceLabel":13146,"sourceYear":107,"table":13163,"note":13164,"datasets":13165,"metrics":13166,"families":13167,"methods":13168,"methodIds":13170,"rows":63,"failures":30},"schauer2018peopleremover-text-sec-ix","Text Sec. IX","Quality-runtime trade-off on lecturehall with a 17.5 cm voxel instead of 10 cm",[13152],[23],[23],[13169],"peopleremover, voxel 17.5 cm",[8912],{"slug":13172,"sourceId":13173,"sourceLabel":13174,"sourceYear":562,"table":108,"note":13175,"datasets":13176,"metrics":13178,"families":13179,"methods":13180,"methodIds":13182,"rows":102,"failures":30},"schillberg2025quadrupedasbuilt-table-2","schillberg2025quadrupedasbuilt","Schillberg et al., 2025","RMS APE and RPE of each SLAM trajectory vs the Leica TS30 prism trajectory; time-synchronised; Umeyama alignment (evo) using only the first 1.5 m of t…",[13177],"own recordings (test building not named; data availability not stated)",[568,330],[25,332],[1437,571,13181],"LIO-SAM (LIO-SAM-6AXIS variant)",[321,577],{"slug":13184,"sourceId":13173,"sourceLabel":13174,"sourceYear":562,"table":17,"note":13185,"datasets":13186,"metrics":13187,"families":13188,"methods":13189,"methodIds":13190,"rows":102,"failures":30},"schillberg2025quadrupedasbuilt-table-3","Mean nearest-neighbour cloud-to-cloud distance to RIEGL VZ-400i\u002FVZ-600i TLS reference after manual point-pair alignment plus ICP (CloudCompare); max d…",[13177],[3417,23],[78,23],[1437,571,13181],[321,577],{"slug":13192,"sourceId":3525,"sourceLabel":13193,"sourceYear":374,"table":91,"note":13194,"datasets":13195,"metrics":13197,"families":13198,"methods":13199,"methodIds":13208,"rows":3547,"failures":416},"dynablox2023-table-i","Schmid et al., 2023","DOALS (OS1 64 at 10 Hz, 8 sequences in 4 environments): IoU (%) between detected and annotated dynamic points, mean over the 10 annotated frames per s…",[13196],"DOALS",[23],[23],[13200,13201,13202,13203,13204,13205,13206,81,13207],"4DMOS [14]","DOALS-3DMiniNet [10,28]","LC Free Space [22] (20m)","LMNet [8] (Original)","LMNet [8] (Refit)","MotionSeg3D [9]","Occupancy [10] (Offline)","Ours (20m)",[3525],{"slug":13210,"sourceId":3525,"sourceLabel":13193,"sourceYear":374,"table":325,"note":13211,"datasets":13212,"metrics":13213,"families":13214,"methods":13215,"methodIds":13222,"rows":654,"failures":30},"dynablox2023-table-ii","Ablation of the modelled components at 20 m range on DOALS (IoU %, all sequences)",[13196],[23],[23],[13216,13217,13218,13219,13220,13221,81],"Dynablox (w\u002Fo Cluster Filter τc)","Dynablox (w\u002Fo Occupancy Cue)","Dynablox (w\u002Fo Sparsity Comp. τs)","Dynablox (w\u002Fo Spatial Margin N)","Dynablox (w\u002Fo TSDF Cue)","Dynablox (w\u002Fo temporal window τw)",[3525],{"slug":13224,"sourceId":3525,"sourceLabel":13193,"sourceYear":374,"table":8279,"note":13225,"datasets":13226,"metrics":13228,"families":13229,"methods":13230,"methodIds":13231,"rows":154,"failures":30},"dynablox2023-text-sec-vii-c","Robustness to drift: 3 random drift rollouts per sequence and intensity from a drift simulator applied to DOALS; recall stated in text for the worst c…",[13227],"DOALS with simulated drift",[23],[23],[81],[3525],{"slug":13233,"sourceId":3525,"sourceLabel":13193,"sourceYear":374,"table":13234,"note":13235,"datasets":13236,"metrics":13238,"families":13239,"methods":13240,"methodIds":13242,"rows":356,"failures":30},"dynablox2023-text-sec-vii-d","Text Sec.VII-D","Computation cost on a NUC with laptop-grade AMD 4800U CPU; integration distance limited to 20 m unless stated",[13237],"not named for the timing (Fig. 5 'different datasets'; the DOALS Station environment is named in Sec. VII-D)",[148,23,38],[40,23],[81,13241],"Ours (pre-processing component)",[3525],{"slug":13244,"sourceId":9325,"sourceLabel":13245,"sourceYear":16,"table":108,"note":13246,"datasets":13247,"metrics":13248,"families":13249,"methods":13250,"methodIds":13254,"rows":2388,"failures":154},"badslam2019-table-2","Schöps et al., 2019","TUM RGB-D ATE RMSE in cm (rank column omitted); values of other methods copied by the authors from BundleFusion, PSM SLAM and ORB-SLAM2 papers; 'fixed…",[2872],[568],[25],[13251,13252,5252,2907,5233,2909,2910,6668,13253,2911,2913],"BAD SLAM (Ours)","BAD SLAM ablation: Ours (fixed intr.)","PSM SLAM",[9325,2897,2915,2448,1511,2916],{"slug":13256,"sourceId":9325,"sourceLabel":13245,"sourceYear":16,"table":17,"note":13257,"datasets":13258,"metrics":13260,"families":13261,"methods":13262,"methodIds":13263,"rows":2262,"failures":30},"badslam2019-table-3","Synthetic renders of dense TUM RGB-D reconstructions along the original trajectories; each value aggregates ATE RMSE over seven synthetic datasets per…",[13259],"synthetic TUM RGB-D renders (7 datasets per category)",[568],[25],[13251,5252,2907,5233,6668],[9325,2897,2915,1511],{"slug":13265,"sourceId":13266,"sourceLabel":13267,"sourceYear":213,"table":69,"note":13268,"datasets":13269,"metrics":13270,"families":13271,"methods":13272,"methodIds":13276,"rows":102,"failures":30},"surfelmeshing2020-table-1","surfelmeshing2020","Schöps et al., 2020","Mesh quality on TUM RGB-D reconstructions (Kinect v1 data; the trajectories used for Table 1 are not specified); truncated extract of Table 1: full me…",[2872],[23],[23],[13273,13274,13275],"FastFusion [27]","InfiniTAM [29]","Ours (regularization, blending and remeshing enabled)",[7643,13266],{"slug":13278,"sourceId":13266,"sourceLabel":13267,"sourceYear":213,"table":13279,"note":13280,"datasets":13281,"metrics":13282,"families":13283,"methods":13284,"methodIds":13290,"rows":6014,"failures":30},"surfelmeshing2020-table-2-ground-truth-trajectories","Table 2 (ground-truth trajectories)","ICL-NUIM living room with simulated depth noise; ground-truth trajectories used and loop-closure handling disabled for all methods; reconstructions al…",[2870],[75,76,23],[78,23],[13285,13286,13273,13287,13274,13288,13289],"ElasticFusion [17]","ElasticFusion [17] - smoothed","FastFusion [27] - smoothed","InfiniTAM [29] - smoothed","SurfelMeshing (Ours)",[2915,7643,13266],{"slug":13292,"sourceId":13266,"sourceLabel":13267,"sourceYear":213,"table":13293,"note":13294,"datasets":13295,"metrics":13296,"families":13297,"methods":13298,"methodIds":13299,"rows":849,"failures":30},"surfelmeshing2020-table-2-loop-closure-trajectories","Table 2 (loop-closure trajectories)","ICL-NUIM living room with simulated depth noise; trajectories estimated with ElasticFusion including loop closures (kt3 omitted because ElasticFusion…",[2870],[75,76,23],[78,23],[13285,13286,13289],[2915,13266],{"slug":13301,"sourceId":13266,"sourceLabel":13267,"sourceYear":213,"table":6714,"note":13302,"datasets":13303,"metrics":13304,"families":13305,"methods":13306,"methodIds":13308,"rows":154,"failures":154},"surfelmeshing2020-text-sec-5-1","Batch meshing of the final surfel cloud from scratch, for comparison with incremental remeshing (dataset of Fig. 1, identified as fr3\u002Flong office hous…",[2872],[23],[23],[13307],"SurfelMeshing batch meshing (ablation)",[13266],{"slug":13310,"sourceId":13266,"sourceLabel":13267,"sourceYear":213,"table":13311,"note":13312,"datasets":13313,"metrics":13314,"families":13315,"methods":13316,"methodIds":13317,"rows":63,"failures":154},"surfelmeshing2020-text-sec-5-4","Text Sec.5.4","Performance on the TUM fr3\u002Flong office household sequence at 640x480; remeshing statistics averaged over iterations",[2872],[23],[23],[13289],[13266],{"slug":13319,"sourceId":11469,"sourceLabel":13320,"sourceYear":107,"table":91,"note":13321,"datasets":13322,"metrics":13324,"families":13325,"methods":13326,"methodIds":13331,"rows":2813,"failures":578},"staticfusion2018-table-i","Scona et al., 2018","TUM (Freiburg) RGB-D sequences grouped as static (fr1), low dynamic (fr3\u002Fsit) and high dynamic (fr3\u002Fwalk) environments; StaticFusion and VO-SF at QVGA…",[13323],"TUM RGB-D (Freiburg)",[2411,330],[332],[13327,13328,13329,11467,13330],"BaMVO (Kim et al.)","CF (Co-Fusion)","EF (ElasticFusion)","VO-SF (Jaimez et al. joint visual odometry and scene flow)",[2915,11469],{"slug":13333,"sourceId":11469,"sourceLabel":13320,"sourceYear":107,"table":325,"note":13321,"datasets":13334,"metrics":13335,"families":13336,"methods":13337,"methodIds":13338,"rows":1079,"failures":30},"staticfusion2018-table-ii",[13323],[568],[25],[13328,13329,11467,13330],[2915,11469],{"slug":13340,"sourceId":11469,"sourceLabel":13320,"sourceYear":107,"table":13341,"note":13342,"datasets":13343,"metrics":13345,"families":13346,"methods":13347,"methodIds":13348,"rows":274,"failures":30},"staticfusion2018-text-sec-vii-b","Text Sec.VII-B","Authors' hand-held 'selfie' sequence that starts and ends at the same camera position; estimated trajectory length 9.5 m; drift = final position error",[13344],"authors' hand-held selfie sequence",[1551],[1553],[13328,13329,11467],[2915,11469],{"slug":13350,"sourceId":11469,"sourceLabel":13320,"sourceYear":107,"table":550,"note":13351,"datasets":13352,"metrics":13353,"families":13354,"methods":13355,"methodIds":13356,"rows":154,"failures":154},"staticfusion2018-text-sec-viii","Runtime stated in the introduction and conclusion",[2070],[38],[40],[11467],[11469],{"slug":13358,"sourceId":395,"sourceLabel":13359,"sourceYear":107,"table":731,"note":13360,"datasets":13361,"metrics":13363,"families":13364,"methods":13365,"methodIds":13366,"rows":1069,"failures":120},"legoloam2018-table-iv","Shan & Englot, 2018","Runtime of each module for processing one scan, averaged over 10 real-time trials; LOAM has no segmentation module",[13362],"Own Jackal UGV datasets",[38],[40],[461,2197],[395,2118],{"slug":13368,"sourceId":395,"sourceLabel":13359,"sourceYear":107,"table":818,"note":13369,"datasets":13370,"metrics":13371,"families":13372,"methods":13373,"methodIds":13374,"rows":608,"failures":30},"legoloam2018-table-v","Relative pose error when returning to start (final vs initial pose), averaged over 10 real-time trials; both methods fed the same IMU initial guess; p…",[13362],[1551,23],[1553,23],[461,2197],[395,2118],{"slug":13376,"sourceId":338,"sourceLabel":13377,"sourceYear":213,"table":325,"note":13378,"datasets":13379,"metrics":13381,"families":13382,"methods":13383,"methodIds":13386,"rows":641,"failures":356},"liosam2020-table-ii","Shan et al., 2020","End-to-end translation error when returning to the start; LOAM and LIO-SAM forced to real time, LIOM given unlimited time",[13380],"Own LIO-SAM datasets",[1551],[1553],[13384,334,13385,4770,461],"LIO-GPS (no loop factors)","LIO-odom (no GPS or loop factors)",[2117,338,450],{"slug":13388,"sourceId":338,"sourceLabel":13377,"sourceYear":213,"table":279,"note":13389,"datasets":13390,"metrics":13391,"families":13392,"methods":13393,"methodIds":13394,"rows":416,"failures":30},"liosam2020-table-iii","RMSE translation error w.r.t. the GPS measurement history (x-y only, z ignored); GPS partially used as input by LIO-GPS and LIO-SAM",[13380],[568],[25],[13384,334,13385,4770,461],[2117,338,450],{"slug":13396,"sourceId":338,"sourceLabel":13377,"sourceYear":213,"table":731,"note":13397,"datasets":13398,"metrics":13399,"families":13400,"methods":13401,"methodIds":13402,"rows":1042,"failures":356},"liosam2020-table-iv","Average mapping runtime to register one lidar frame; frames dropped above 100 ms for LOAM and LIO-SAM",[13380],[23,38],[40,23],[334,4770,461],[2117,338,450],{"slug":13404,"sourceId":2386,"sourceLabel":13405,"sourceYear":698,"table":91,"note":13406,"datasets":13407,"metrics":13409,"families":13410,"methods":13411,"methodIds":13418,"rows":224,"failures":30},"lvisam2021-table-i","Shan et al., 2021","Ablation on the Urban dataset (handheld, start and end at the same position, GPS-denied under dense vegetation); end-to-end errors",[13408],"Urban (authors' data)",[1551],[1553],[13412,13413,13414,13415,13416,13417],"LVI-SAM ablation A1 (w\u002F depth): VIS only with LiDAR feature depth","LVI-SAM ablation A1 (w\u002Fo depth): VIS only (LiDAR scan-matching disabled), no LiDAR feature depth","LVI-SAM ablation A2: LIS only (VIS disabled)","LVI-SAM ablation A3 (w\u002F depth): VIS + LIS with feature depth, loop closure disabled","LVI-SAM ablation A3 (w\u002Fo depth): VIS + LIS without feature depth, loop closure disabled","LVI-SAM ablation A4: full system with visual loop closure",[2386],{"slug":13420,"sourceId":2386,"sourceLabel":13405,"sourceYear":698,"table":325,"note":13421,"datasets":13422,"metrics":13425,"families":13426,"methods":13427,"methodIds":13434,"rows":2119,"failures":641},"lvisam2021-table-ii","Comparison on the Jackal and Handheld datasets (start and end at the same position); RMSE computed against GPS positions treated as ground truth (Reac…",[13423,13424],"Handheld (authors' data)","Jackal (authors' data)",[568,1551],[25,1553],[13428,13429,9827,9828,13430,461,13431,9856,13432,13433],"LINS (w\u002F loop)","LINS (w\u002Fo loop)","LIO-mapping","LVI-SAM (w\u002F loop)","VINS (w\u002F loop)","VINS (w\u002Fo loop)",[4783,2117,338,450,2386,251],{"slug":13436,"sourceId":13437,"sourceLabel":13438,"sourceYear":107,"table":13439,"note":13440,"datasets":13441,"metrics":13443,"families":13444,"methods":13445,"methodIds":13448,"rows":274,"failures":30},"shang2018-uav-vslam-text-accuracy-evaluation","shang2018_uav_vslam","Shang & Shen, 2018","Text Accuracy Evaluation","Average unit error of edge lengths between eight manually selected corner points of the foundation, SLAM model versus post-processed photogrammetry; m…",[13442],"own UAV flight",[1819,23],[23,1821],[13446,13447],"Post-processing photogrammetry","Visual SLAM (RTAB-Map with RealSense R200 on UAV)",[241],{"slug":13450,"sourceId":13437,"sourceLabel":13438,"sourceYear":107,"table":13451,"note":13452,"datasets":13453,"metrics":13455,"families":13456,"methods":13457,"methodIds":13459,"rows":154,"failures":154},"shang2018-uav-vslam-text-site-asset-tracking","Text Site Asset Tracking","Map size after continuously mapping over 5000 frames, reported as 'over 3.6 GB'; performance of an onboard computer with a memory chip of about 4 GB d…",[13454],"own experimental study (platform and site not specified)",[219],[40],[13458],"Visual SLAM (RTAB-Map) continuous mapping",[241],{"slug":13461,"sourceId":13462,"sourceLabel":13463,"sourceYear":16,"table":91,"note":13464,"datasets":13465,"metrics":13467,"families":13468,"methods":13469,"methodIds":13471,"rows":1042,"failures":654},"vilslam2019-table-i","vilslam2019","Shao et al., 2019","Author-collected sequences that start and end at the same point; FDE = final drift error of LiDAR mapping odometry (no loop closure) as % of distance;…",[13466],"VIL-SLAM custom datasets",[3417,1551],[1553,78],[461,13470],"VIL-SLAM",[2118,13462],{"slug":13473,"sourceId":13462,"sourceLabel":13463,"sourceYear":16,"table":1610,"note":13474,"datasets":13475,"metrics":13476,"families":13477,"methods":13478,"methodIds":13480,"rows":63,"failures":30},"vilslam2019-text-sec-viii-b","Final drift error after loop closure, stated in the text",[13466],[1551],[1553],[13479],"VIL-SLAM (with loop closure)",[13462],{"slug":13482,"sourceId":13483,"sourceLabel":13484,"sourceYear":68,"table":91,"note":13485,"datasets":13486,"metrics":13489,"families":13490,"methods":13491,"methodIds":13494,"rows":9098,"failures":30},"litgs2026-table-i","litgs2026","Shi et al., 2026","Thermal Gaussian-splatting reconstruction quality; private scenes captured with the authors' Livox Avia + thermal rig at different times of day (Fig.…",[13487,13488],"M2DGR (public)","authors' private thermal dataset",[23],[23],[13492,81,13493],"LIV-GaussMap","Thermal3D-GS",[13483,5807],{"slug":13496,"sourceId":13483,"sourceLabel":13484,"sourceYear":68,"table":1208,"note":13497,"datasets":13498,"metrics":13499,"families":13500,"methods":13501,"methodIds":13503,"rows":63,"failures":30},"litgs2026-text-sec-iv-c","Offline training and rendering cost",[13488],[23,38],[40,23],[13502],"LIT-GS",[13483],{"slug":13505,"sourceId":8562,"sourceLabel":13506,"sourceYear":1032,"table":13507,"note":13508,"datasets":13509,"metrics":13510,"families":13511,"methods":13512,"methodIds":13515,"rows":63,"failures":30},"sibley2010swf-text-fig-14-caption","Sibley et al., 2010","Text Fig. 14 caption","Simulation averaged over 20 runs, 76 landmarks tracked, about 20 features per frame, feature tracks about 10 frames, 0.5 px image noise; run-time curv…",[4430],[23],[23],[13513,13514],"10-frame SWF","20-frame SWF",[8562],{"slug":13517,"sourceId":8562,"sourceLabel":13506,"sourceYear":1032,"table":13518,"note":13519,"datasets":13520,"metrics":13522,"families":13523,"methods":13524,"methodIds":13526,"rows":63,"failures":30},"sibley2010swf-text-sec-4-effect-of-marginalization","Text Sec.4 Effect of Marginalization","SWF versus visual odometry (VO, equivalent to deleting instead of marginalizing with k = 1) over 10 frames; the data set behind this comparison is not…",[13521],"not stated in the text (Sec. 4 Effect of Marginalization; the paragraph refers to Fig. 12 and to Newman et al. 2009)",[23],[23],[13525],"SWF",[8562],{"slug":13528,"sourceId":8562,"sourceLabel":13506,"sourceYear":1032,"table":13529,"note":13530,"datasets":13531,"metrics":13533,"families":13534,"methods":13535,"methodIds":13537,"rows":154,"failures":30},"sibley2010swf-text-sec-4-static-convergence","Text Sec.4 Static Convergence","Ground-truth check: plane fitted to the batch solution over all frames using fiducials; used to justify a planar ground-truth wall model",[13532],"authors' laboratory wall sequence with fiducials",[1830],[78],[13536],"batch solution over all frames using fiducials (ground-truth wall model)",[],{"slug":13539,"sourceId":13540,"sourceLabel":13541,"sourceYear":13542,"table":13543,"note":13544,"datasets":13545,"metrics":13547,"families":13548,"methods":13549,"methodIds":13551,"rows":154,"failures":154},"smith-cheeseman1986-text-sec-6-3","smith_cheeseman1986","Smith & Cheeseman, 1986",1986,"Text Sec.6.3","first-order AT estimates compared with an independent Monte Carlo simulation of a three-degree-of-freedom robot with Gaussian errors in the given rela…",[13546],"Monte Carlo simulation (authors)",[23],[23],[13550],"compounding of approximate transformations (first-order estimate)",[13540],{"slug":13553,"sourceId":13554,"sourceLabel":13555,"sourceYear":4540,"table":3060,"note":13556,"datasets":13557,"metrics":13559,"families":13560,"methods":13561,"methodIds":13563,"rows":154,"failures":154},"soudarissanane2011scanninggeometry-text-sec-4-2","soudarissanane2011scanninggeometry","Soudarissanane et al., 2011","Board scanned at 5 m and 10 to 50 m, rotated 0° to 80° in 10° steps (54 scans)",[13558],"reference board experiment 2",[23],[23],[13562],"Leica HDS6000 acquisition",[],{"slug":13565,"sourceId":13554,"sourceLabel":13555,"sourceYear":4540,"table":7991,"note":13566,"datasets":13567,"metrics":13569,"families":13570,"methods":13571,"methodIds":13575,"rows":356,"failures":154},"soudarissanane2011scanninggeometry-text-sec-5-2-3","Room scanned from a centre and a corner viewpoint; statistics over 5°x5° spherical patches",[13568],"standard room scans",[23,1830],[78,23],[13572,13573,13574],"incidence-angle corrected residuals (proposed model)","incidence-angle model","raw TLS residuals (incidence-angle effect included)",[],{"slug":13577,"sourceId":10043,"sourceLabel":13578,"sourceYear":1111,"table":13579,"note":13580,"datasets":13581,"metrics":13583,"families":13584,"methods":13585,"methodIds":13588,"rows":63,"failures":63},"stoyanov2012d2dndt-text-sec-6-1","Stoyanov et al., 2012","Text Sec. 6.1","Initial orientation estimation on simulated scan pairs (20 positions per environment, 10 scans 15 deg apart, 8 pairs 30 deg apart per position, 160 pa…",[13582],"simulated Willow and Terrain data sets (ROS\u002FGazebo)",[38],[40],[13586,13587],"3D-NDT histogram initialization","FPFH (ROS implementation)",[1788,10043],{"slug":13590,"sourceId":13591,"sourceLabel":13592,"sourceYear":16,"table":91,"note":13593,"datasets":13594,"metrics":13598,"families":13599,"methods":13600,"methodIds":13601,"rows":578,"failures":356},"straub2019replica-table-i","straub2019replica","Straub et al., 2019","Dataset comparison; colour and geometry resolution estimated as pixels and mesh primitives per m2 on the semantically annotated meshes, median values.",[13595,13596,3223,2963,13597],"Gibson","Matterport 3D (MP3D)","Stanford 2D-3D-S",[23],[23],[13595,13596,3223,2963,13597],[2958,13591],{"slug":13603,"sourceId":5050,"sourceLabel":13604,"sourceYear":1111,"table":3370,"note":13605,"datasets":13606,"metrics":13607,"families":13608,"methods":13609,"methodIds":13611,"rows":154,"failures":30},"sturm2012tum-text-sec-vi-a","Sturm et al., 2012","Motion-capture calibration check with a ~2 m rod carrying markers at both ends, measured across the capture area; reference-system quality, not a SLAM…",[2872],[1819],[1821],[13610],"TUM RGB-D ground truth (MotionAnalysis motion capture with Kinect calibration)",[],{"slug":13613,"sourceId":5050,"sourceLabel":13604,"sourceYear":1111,"table":3381,"note":13614,"datasets":13615,"metrics":13616,"families":13617,"methods":13618,"methodIds":13620,"rows":63,"failures":30},"sturm2012tum-text-sec-vi-b","Kinect depth noise versus distance to a checkerboard (stated in text from Fig. 4b); sensor characteristic.",[2872],[23],[23],[13619],"Microsoft Kinect depth",[],{"slug":13622,"sourceId":5050,"sourceLabel":13604,"sourceYear":1111,"table":3393,"note":13623,"datasets":13624,"metrics":13625,"families":13626,"methods":13627,"methodIds":13628,"rows":224,"failures":356},"sturm2012tum-text-sec-vi-c","Extrinsic calibration and validation of the ground truth; reference-system quality, not a SLAM result.",[2872],[23],[23],[13610],[],{"slug":13630,"sourceId":5050,"sourceLabel":13604,"sourceYear":1111,"table":4996,"note":13631,"datasets":13632,"metrics":13633,"families":13634,"methods":13635,"methodIds":13637,"rows":63,"failures":63},"sturm2012tum-text-sec-vi-d","Time synchronization findings.",[2872],[23],[23],[13636,13610],"Microsoft Kinect colour and depth streams",[],{"slug":13639,"sourceId":13640,"sourceLabel":13641,"sourceYear":921,"table":69,"note":13642,"datasets":13643,"metrics":13644,"families":13645,"methods":13646,"methodIds":13652,"rows":2813,"failures":30},"mrsmap2014-table-1","mrsmap2014","Stückler & Behnke, 2014","Incremental (frame-to-frame) registration on TUM Freiburg sequences; median translational relative pose error in mm (maximum values in brackets in the…",[13323],[330],[332],[13647,13648,13649,13650,13651],"3D-NDT [7]","Fovis [12]","GICP [5]","Ours (MRSMap)","Warp [17] (OpenCV)",[6025,13640,3292,10043],{"slug":13654,"sourceId":13640,"sourceLabel":13641,"sourceYear":921,"table":108,"note":13655,"datasets":13656,"metrics":13657,"families":13658,"methods":13659,"methodIds":13660,"rows":578,"failures":30},"mrsmap2014-table-2","Average runtime per incremental registration in ms (standard deviations in the table not extracted)",[13323],[38],[40],[13647,13648,13649,13650,13651],[6025,13640,3292,10043],{"slug":13662,"sourceId":13640,"sourceLabel":13641,"sourceYear":921,"table":13663,"note":13664,"datasets":13665,"metrics":13666,"families":13667,"methods":13668,"methodIds":13672,"rows":2388,"failures":30},"mrsmap2014-table-3-corrigendum","Table 3 (corrigendum)","SLAM on TUM Freiburg sequences: RMSE of relative pose error averaged over all frame differences, in m; values from the 2015 corrigendum that replaces…",[13323],[330],[332],[13669,13670,13671],"Ours all frames (MRSMap SLAM)","Ours real-time (MRSMap SLAM, frames dropped, 0.05 m max. resolution)","RGB-D SLAM [24], [34] (Endres et al.)",[13640],{"slug":13674,"sourceId":13640,"sourceLabel":13641,"sourceYear":921,"table":2984,"note":13675,"datasets":13676,"metrics":13677,"families":13678,"methods":13679,"methodIds":13681,"rows":63,"failures":30},"mrsmap2014-text-sec-6-2","Time of one pose-graph optimization iteration on freiburg2_desk (up to 64 key views and 138 edges)",[13323],[38],[40],[13680],"Ours (MRSMap SLAM, graph optimization)",[13640],{"slug":13683,"sourceId":13684,"sourceLabel":13685,"sourceYear":562,"table":244,"note":13686,"datasets":13687,"metrics":13688,"families":13689,"methods":13690,"methodIds":13692,"rows":2882,"failures":30},"stuhrenberg2025liobim-table-5","stuhrenberg2025liobim","Stührenberg & Smarsly, 2025","APE of keyframe trajectories vs ConSLAM ground truth; trajectories aligned with Umeyama alignment (evo), scale handling not stated; ConSLAM sequence 1…",[8315],[329,568,23],[25,23],[13691,334],"LIO-BIM",[338,13684],{"slug":13694,"sourceId":13684,"sourceLabel":13685,"sourceYear":562,"table":1072,"note":13695,"datasets":13696,"metrics":13697,"families":13698,"methods":13699,"methodIds":13700,"rows":2882,"failures":30},"stuhrenberg2025liobim-table-6","APE vs the ground-truth trajectories of SLAM2REF [17]; same runs as Table 5; alignment procedure for this comparison not separately stated",[8315],[329,568,23],[25,23],[13691,334],[338,13684],{"slug":13702,"sourceId":13684,"sourceLabel":13685,"sourceYear":562,"table":621,"note":13703,"datasets":13704,"metrics":13706,"families":13707,"methods":13708,"methodIds":13709,"rows":1042,"failures":30},"stuhrenberg2025liobim-table-7","Inlier RMSE (distance threshold 0.3 m) and fitness score of the SLAM point cloud vs TLS (Faro Focus S 70 for the office, Leica RTC360 scans of ConSLAM…",[8315,13705],"own recording (IDOG quadruped)",[3417,23],[78,23],[13691,334],[338,13684],{"slug":13711,"sourceId":13684,"sourceLabel":13685,"sourceYear":562,"table":633,"note":13712,"datasets":13713,"metrics":13714,"families":13715,"methods":13716,"methodIds":13717,"rows":598,"failures":30},"stuhrenberg2025liobim-table-8","Processing times recorded on the same Intel NUC11TNKV7 for both tests (office rosbag played back to LIO-BIM; ConSLAM sequences processed on the same c…",[8315,13705],[23,38],[40,23],[13691],[13684],{"slug":13719,"sourceId":13684,"sourceLabel":13685,"sourceYear":562,"table":644,"note":13712,"datasets":13720,"metrics":13721,"families":13722,"methods":13723,"methodIds":13724,"rows":598,"failures":30},"stuhrenberg2025liobim-table-9",[8315,13705],[23,38],[40,23],[13691],[13684],{"slug":13726,"sourceId":6378,"sourceLabel":13727,"sourceYear":698,"table":69,"note":13728,"datasets":13729,"metrics":13730,"families":13731,"methods":13732,"methodIds":13735,"rows":2607,"failures":30},"imap2021-table-1","Sucar et al., 2021","Replica reconstruction from 200,000 points sampled on GT and reconstructed meshes; each scene reported at its highest reached completion ratio with th…",[3223],[75,76],[78],[13733,13734],"TSDF Fusion (with iMAP tracking)","iMAP",[6378],{"slug":13737,"sourceId":6378,"sourceLabel":13727,"sourceYear":698,"table":108,"note":13738,"datasets":13739,"metrics":13740,"families":13741,"methods":13742,"methodIds":13749,"rows":120,"failures":30},"imap2021-table-2","Memory of the map representation: iMAP as a function of MLP width (default 256) and TSDF fusion as a function of voxel resolution (default 256^3); non…",[8984],[219],[40],[13743,13744,13745,13746,13747,13748],"TSDF Fusion, resolution 128","TSDF Fusion, resolution 256","TSDF Fusion, resolution 512","iMAP, width 128 (variant)","iMAP, width 256 (default)","iMAP, width 512 (variant)",[6378],{"slug":13751,"sourceId":6378,"sourceLabel":13727,"sourceYear":698,"table":17,"note":13752,"datasets":13753,"metrics":13754,"families":13755,"methods":13756,"methodIds":13760,"rows":224,"failures":30},"imap2021-table-3","Tracking ATE RMSE on three TUM RGB-D sequences; alignment not stated",[2872],[568],[25],[13757,13758,13759,13734],"BAD-SLAM [24]","Kintinuous [38] (2012 workshop version cited)","ORB-SLAM2 [16]",[9325,6378,2448,1511],{"slug":13762,"sourceId":6378,"sourceLabel":13727,"sourceYear":698,"table":33,"note":13763,"datasets":13764,"metrics":13765,"families":13766,"methods":13767,"methodIds":13775,"rows":1315,"failures":30},"imap2021-table-4","Ablation on Replica office-2 (three seeds): tracking time for 6 iterations and joint optimization time for 10 iterations running concurrently on the s…",[3223],[76,23,38],[40,78,23],[13768,13769,13770,13771,13772,13773,13774],"iMAP 100 pixels (variant)","iMAP 400 pixels (variant)","iMAP default (width 256, W = 5, 200 px)","iMAP width 128 (variant)","iMAP width 512 (variant)","iMAP window 10 (variant)","iMAP window 3 (variant)",[6378],{"slug":13777,"sourceId":6378,"sourceLabel":13727,"sourceYear":698,"table":244,"note":13778,"datasets":13779,"metrics":13780,"families":13781,"methods":13782,"methodIds":13787,"rows":356,"failures":30},"imap2021-table-5","Completion ratio vs keyframe selection threshold tP on Replica office-2 (keyframes: 8, 10, 14, 24); tP = 0.65 is the default",[3223],[76],[78],[13783,13784,13785,13786],"iMAP tP = 0.55 (8 keyframes) (variant)","iMAP tP = 0.65 (10 keyframes) default","iMAP tP = 0.75 (14 keyframes) (variant)","iMAP tP = 0.85 (24 keyframes) (variant)",[6378],{"slug":13789,"sourceId":13790,"sourceLabel":13791,"sourceYear":16,"table":13792,"note":13793,"datasets":13794,"metrics":13795,"families":13796,"methods":13797,"methodIds":13800,"rows":356,"failures":30},"openvslam2019-fig-4-table","openvslam2019","Sumikura et al., 2019","Fig. 4 table","Tracking time per frame on EuRoC MH_02 (monocular); laptop Core i7-7820HK (2.90 GHz, 4C8T), 32 GB RAM",[743],[38],[40],[13798,13799],"ORB-SLAM (ORB-SLAM2)","OpenVSLAM",[1511],{"slug":13802,"sourceId":13790,"sourceLabel":13791,"sourceYear":16,"table":13803,"note":13804,"datasets":13805,"metrics":13806,"families":13807,"methods":13808,"methodIds":13809,"rows":356,"failures":30},"openvslam2019-fig-6-table","Fig. 6 table","Tracking time per frame on KITTI Odometry sequence 05 (stereo); same laptop",[4494],[38],[40],[13798,13799],[1511],{"slug":13811,"sourceId":13812,"sourceLabel":13813,"sourceYear":107,"table":1182,"note":13814,"datasets":13815,"metrics":13816,"families":13817,"methods":13818,"methodIds":13821,"rows":274,"failures":154},"smsckf2018-text-sec-iv-a","smsckf2018","Sun et al., 2018","EuRoC MAV; CPU measured on NUC6i7KYK (quad-core i7-6770HQ); five runs per sequence; parameters from each repository",[743],[568,113,23],[25,40,23],[4741,13819,13820],"S-MSCKF filter back end","S-MSCKF front end",[],{"slug":13823,"sourceId":13812,"sourceLabel":13813,"sourceYear":107,"table":1208,"note":13824,"datasets":13825,"metrics":13827,"families":13828,"methods":13829,"methodIds":13830,"rows":63,"failures":30},"smsckf2018-text-sec-iv-c","Fully autonomous flight through a wooded area and a warehouse and back; stereo cameras and IMU only for estimation; laser used for mapping only",[13826],"own field flight (woods, warehouse, runway)",[1551,23],[1553,23],[4741],[],{"slug":13832,"sourceId":13833,"sourceLabel":13834,"sourceYear":562,"table":244,"note":13835,"datasets":13836,"metrics":13838,"families":13839,"methods":13840,"methodIds":13842,"rows":339,"failures":30},"sun2025nss-table-5","sun2025nss","Sun et al., 2025","Pairwise spatiotemporal registration on NSS; success = RRE \u003C 10 deg and RTE \u003C 0.2 m; methods retrained per split following original protocols. TE and…",[13837],"Nothing Stands Still (NSS)",[23,1933],[23,1935],[9000,2631,9030,13841,9012],"GeoTransformer",[2574,12302,1788],{"slug":13844,"sourceId":13833,"sourceLabel":13834,"sourceYear":562,"table":1072,"note":13845,"datasets":13846,"metrics":13850,"families":13851,"methods":13852,"methodIds":13853,"rows":608,"failures":30},"sun2025nss-table-6","Registration recall of the three best methods on NSS (original split) versus 3DMatch and 3DLoMatch; benchmark values for 3DMatch and 3DLoMatch as list…",[12306,2578,13847,13848,13849],"NSS (all), original split","NSS (different-stage only), original split","NSS (same-stage only), original split",[1933],[1935],[9000,13841,9012],[12302],{"slug":13855,"sourceId":13833,"sourceLabel":13834,"sourceYear":562,"table":644,"note":13856,"datasets":13857,"metrics":13858,"families":13859,"methods":13860,"methodIds":13867,"rows":598,"failures":30},"sun2025nss-table-9","Multi-way spatiotemporal registration: pose graphs over all stages of an area, edges initialised from pairwise Predator or GeoTransformer results; glo…",[13837],[23,1933],[23,1935],[13861,13862,13863,13864,13865,13866],"Choi et al. [124] pose-graph optimisation initialised with GeoTransformer","Choi et al. [124] pose-graph optimisation initialised with Predator","GeoTransformer pairwise outputs (no multi-way step)","PoseGraphNet [135] initialised with GeoTransformer","PoseGraphNet [135] initialised with Predator","Predator pairwise outputs (no multi-way step)",[12302],{"slug":13869,"sourceId":13870,"sourceLabel":13871,"sourceYear":538,"table":69,"note":13872,"datasets":13873,"metrics":13875,"families":13876,"methods":13877,"methodIds":13882,"rows":618,"failures":154},"surmann2003-kurt3d-table-1","surmann2003_kurt3d","Surmann et al., 2003","Computing time for matching two 3D scans of the GMD Robobench (46,336 points; 4,910 reduced points) on a Pentium-III-800, odometry initialization",[13874],"GMD Robobench (two scans)",[23],[23],[13878,13879,13880,13881],"All points and brute force search","All points and kD-tree","Reduced points and brute force search","Reduced points and kD-tree",[13870],{"slug":13884,"sourceId":13870,"sourceLabel":13871,"sourceYear":538,"table":4863,"note":13885,"datasets":13886,"metrics":13887,"families":13888,"methods":13889,"methodIds":13891,"rows":154,"failures":154},"surmann2003-kurt3d-text-sec-4-3","Whole next-best-view planning algorithm on scenes of 20 m x 30 m",[2070],[23],[23],[13890],"next best view planner",[13870],{"slug":13893,"sourceId":2489,"sourceLabel":13894,"sourceYear":1111,"table":325,"note":13895,"datasets":13896,"metrics":13900,"families":13901,"methods":13902,"methodIds":13904,"rows":3819,"failures":30},"sunderhauf2012switchable-table-ii","Sünderhauf & Protzel, 2012","Robust back-end (switchable constraints in g2o, Xi = 1) on pose graphs with 0 to 1000 added false loop closures under four outlier policies, 500 trial…",[13897,6552,13898,13899,6555],"City10000","Manhattan (Olson original)","Manhattan (g2o version)",[330,23,1933],[23,332,1935],[13903],"switchable constraints (robust back-end)",[2489],{"slug":13906,"sourceId":2489,"sourceLabel":13894,"sourceYear":1111,"table":1193,"note":13907,"datasets":13908,"metrics":13910,"families":13911,"methods":13912,"methodIds":13913,"rows":63,"failures":154},"sunderhauf2012switchable-text-sec-iv-b","Precision-recall of deactivating added false loop closures, emulated by thresholding switch values; all tested datasets together",[13909],"Manhattan (both versions), City10000, Sphere2500, Intel",[23],[23],[13903],[2489],{"slug":13915,"sourceId":2489,"sourceLabel":13894,"sourceYear":1111,"table":13916,"note":13917,"datasets":13918,"metrics":13920,"families":13921,"methods":13922,"methodIds":13923,"rows":154,"failures":154},"sunderhauf2012switchable-text-sec-iv-d2","Text Sec.IV-D2","Parking Garage (real 3D, 1661 poses, 4615 loop closures, four decks joined by two odometry strands) excluded from Table II",[13919],"Parking Garage",[23],[23],[13903],[2489],{"slug":13925,"sourceId":13926,"sourceLabel":13927,"sourceYear":698,"table":69,"note":13928,"datasets":13929,"metrics":13931,"families":13932,"methods":13933,"methodIds":13937,"rows":1069,"failures":30},"lion2021-table-1","lion2021","Tagliabue et al., 2021","DARPA SubT Tunnel Circuit (NIOSH experimental mines, Pittsburgh), one robot; LAMP output used as ground truth; LiDAR odometry 10 Hz; LION sliding wind…",[13930],"DARPA SubT Tunnel Circuit runs",[568,23],[25,23],[13934,461,13935,13936],"LION","Scan-To-Scan","Wheel-Inertial",[13926,2118],{"slug":13939,"sourceId":13926,"sourceLabel":13927,"sourceYear":698,"table":3087,"note":13940,"datasets":13941,"metrics":13943,"families":13944,"methods":13945,"methodIds":13948,"rows":63,"failures":30},"lion2021-text-sec-3-2","Office-like environment with a featureless corridor section; total translation error when the robot returns to the start, before loop closure; values…",[13942],"JPL office-like environment",[1551],[1553],[13946,13947],"LION with observability module (HeRO switches to WIO in the corridor)","LION without observability module",[13926],{"slug":13950,"sourceId":13951,"sourceLabel":13952,"sourceYear":4540,"table":244,"note":13953,"datasets":13954,"metrics":13956,"families":13957,"methods":13958,"methodIds":13967,"rows":641,"failures":30},"tang2011flatness-table-5","tang2011flatness","Tang et al., 2011","Number of false-positive defect regions for the best parameter setting of each algorithm (ranked by detection, then localization, then FP) when scanni…",[13955],"own test bed (clay defects on flat boards)",[23],[23],[13959,13960,13961,13962,13963,13964,13965,13966],"DF (sigma 1 px)","DF (sigma 2 px)","DF (sigma 3 px)","RF (sigma 1 px)","RF (sigma 2 px)","SW (window 3 cm)","SW (window 4 cm)","SW (window 6 cm)",[],{"slug":13969,"sourceId":13951,"sourceLabel":13952,"sourceYear":4540,"table":1072,"note":13953,"datasets":13970,"metrics":13971,"families":13972,"methods":13973,"methodIds":13974,"rows":641,"failures":30},"tang2011flatness-table-6",[13955],[23],[23],[13959,13960,13962,13964],[],{"slug":13976,"sourceId":13951,"sourceLabel":13952,"sourceYear":4540,"table":621,"note":13953,"datasets":13977,"metrics":13978,"families":13979,"methods":13980,"methodIds":13981,"rows":120,"failures":30},"tang2011flatness-table-7",[13955],[23],[23],[13959,13962,13964],[],{"slug":13983,"sourceId":13951,"sourceLabel":13952,"sourceYear":4540,"table":13984,"note":13985,"datasets":13986,"metrics":13988,"families":13989,"methods":13990,"methodIds":13994,"rows":274,"failures":30},"tang2011flatness-text-influence-of-angular-resolution","Text Influence of Angular Resolution","Localization rate of Scanner 1 at 10 m after increasing angular resolution from 0.036 to 0.018 deg (rates were 0% at 0.036 deg)",[13987],"own test bed",[1933],[1935],[13991,13992,13993],"DF","RF","SW",[],{"slug":13996,"sourceId":13951,"sourceLabel":13952,"sourceYear":4540,"table":13997,"note":13998,"datasets":13999,"metrics":14000,"families":14001,"methods":14002,"methodIds":14006,"rows":274,"failures":30},"tang2011flatness-text-inspection-rates","Text Inspection rates","Illustrative inspection rate R = A \u002F (T_scan + T_setup) for controlling defects thicker than 1 mm with the SW algorithm; setup time assumed 120 s (hyp…",[13987],[23],[23],[14003,14004,14005],"SW algorithm with Scanner 1 (AMCW)","SW algorithm with Scanner 2 (TOF)","SW algorithm with Scanner 3 (TOF)",[],{"slug":14008,"sourceId":14009,"sourceLabel":14010,"sourceYear":374,"table":91,"note":14011,"datasets":14012,"metrics":14014,"families":14015,"methods":14016,"methodIds":14019,"rows":224,"failures":30},"fflins2023-table-i","fflins2023","Tang et al., 2023","LiLi-OM dataset without ground truth: error of the estimated starting-ending distance relative to meter-level GPS start and end fixes; LiLi-OM and LIO…",[14013],"LiLi-OM dataset",[23],[23],[14017,14018,334,335],"FAST_LIO2","FF-LINS",[321,14009,337,338],{"slug":14021,"sourceId":14009,"sourceLabel":14010,"sourceYear":374,"table":325,"note":14022,"datasets":14023,"metrics":14024,"families":14025,"methods":14026,"methodIds":14028,"rows":224,"failures":154},"fflins2023-table-ii","R3LIVE dataset (Livox AVIA) end-to-end errors; LiLi-OM could not be run; FF-LINS-WO disables online extrinsic and time-delay calibration",[8050],[1551],[1553],[1437,14018,14027,334],"FF-LINS-WO (without online calibration)",[321,14009,338],{"slug":14030,"sourceId":14009,"sourceLabel":14010,"sourceYear":374,"table":279,"note":14031,"datasets":14032,"metrics":14034,"families":14035,"methods":14036,"methodIds":14037,"rows":608,"failures":30},"fflins2023-table-iii","Private Robot dataset (Livox Mid-70, ADIS16465) with post-processed GNSS-RTK\u002FINS ground truth; ARE and ATE; LiLi-OM and LIO-SAM could not be run",[14033],"FF-LINS Robot dataset",[329,23],[25,23],[1437,14018,14027],[321,14009],{"slug":14039,"sourceId":14009,"sourceLabel":14010,"sourceYear":374,"table":731,"note":14040,"datasets":14041,"metrics":14042,"families":14043,"methods":14044,"methodIds":14045,"rows":224,"failures":30},"fflins2023-table-iv","Average running times of FF-LINS on the Robot dataset; frame preprocessing about 0.6 ms per frame (text)",[14033],[23,38],[40,23],[14018],[14009],{"slug":14047,"sourceId":14048,"sourceLabel":14049,"sourceYear":68,"table":325,"note":14050,"datasets":14051,"metrics":14053,"families":14054,"methods":14055,"methodIds":14059,"rows":6567,"failures":63},"palvio2026-table-ii","palvio2026","Tang et al., 2026","Absolute translation error (RMSE, m), all systems in real-time mode on the desktop PC; 'x' = system totally failed. Ablation columns (Ours VIO, LIO, w…",[5857,14052],"i2Nav-Robot",[568],[25],[1437,14056,14018,14057,14058,2381],"FAST-LIVO2","LE-VINS","Ours (PA-LVIO)",[321,797,14048,2387],{"slug":14061,"sourceId":14048,"sourceLabel":14049,"sourceYear":68,"table":279,"note":14062,"datasets":14063,"metrics":14064,"families":14065,"methods":14066,"methodIds":14067,"rows":2252,"failures":30},"palvio2026-table-iii","End-to-end errors (m) on the public R3LIVE handheld dataset, computed by subtracting start positions from end positions; ablation columns omitted; Ave…",[8050],[1551],[1553],[1437,14056,14018,14057,14058,2381],[321,797,14048,2387],{"slug":14069,"sourceId":14048,"sourceLabel":14049,"sourceYear":68,"table":731,"note":14070,"datasets":14071,"metrics":14072,"families":14073,"methods":14074,"methodIds":14077,"rows":274,"failures":30},"palvio2026-table-iv","Average over the 10 i2Nav-Robot sequences of per-keyframe FGO time for tightly vs loosely coupled F2M factors, and of the F2M pose optimization",[14052],[38],[40],[14075,14076],"Loosely coupled (PA-LVIO)","PA-LVIO with tightly coupled F2M factor",[14048],{"slug":14079,"sourceId":14048,"sourceLabel":14049,"sourceYear":68,"table":827,"note":14080,"datasets":14081,"metrics":14083,"families":14084,"methods":14085,"methodIds":14087,"rows":120,"failures":30},"palvio2026-table-vi","Average processing time on the three private HandNav sequences (whu-building, whu-gateway, whu-library); LiDAR and visual include preprocessing and da…",[14082],"HandNav (private)",[38],[40],[14086],"PA-LVIO",[14048],{"slug":14089,"sourceId":14048,"sourceLabel":14049,"sourceYear":68,"table":838,"note":14090,"datasets":14091,"metrics":14092,"families":14093,"methods":14094,"methodIds":14095,"rows":63,"failures":30},"palvio2026-table-vii","Equivalent FPS = sequence length divided by total running time, multiplied by the frame rate; averaged over the three HandNav sequences",[14082],[148],[40],[14086],[14048],{"slug":14097,"sourceId":14098,"sourceLabel":14099,"sourceYear":562,"table":17,"note":14100,"datasets":14101,"metrics":14102,"families":14103,"methods":14104,"methodIds":14110,"rows":5121,"failures":618},"tao2025oxfordspires-table-3","tao2025oxfordspires","Tao et al., 2025","ATE RMS (m) against LiDAR-to-TLS ground truth after SE(3) Umeyama alignment; online: VILENS-SLAM, Fast-LIO-SLAM, SC-LIO-SAM, ImMesh, Fast-LIVO2; offli…",[72],[568],[25],[7327,14105,14106,14107,80,14108,14109],"Fast-LIO-SLAM","Fast-LIVO2","HBA","SC-LIO-SAM","VILENS-SLAM",[797,9453,84],{"slug":14112,"sourceId":14098,"sourceLabel":14099,"sourceYear":562,"table":33,"note":14113,"datasets":14114,"metrics":14115,"families":14116,"methods":14117,"methodIds":14121,"rows":1069,"failures":30},"tao2025oxfordspires-table-4","3D reconstruction against the Leica RTC360 TLS model; points outside the ground-truth region filtered and sky removed for Nerfacto; F-score at 5 cm an…",[72],[74,75,76],[78],[14118,14119,14120],"Nerfacto (Nerfstudio 1.1.4, point cloud from expected depth, offline)","OpenMVS (MVS on COLMAP input, offline)","VILENS-SLAM (merged LiDAR clouds with SLAM poses, online)",[],{"slug":14123,"sourceId":14124,"sourceLabel":14125,"sourceYear":345,"table":69,"note":14126,"datasets":14127,"metrics":14128,"families":14129,"methods":14130,"methodIds":14137,"rows":2813,"failures":30},"cnnslam2017-table-1","cnnslam2017","Tateno et al., 2017","Absolute trajectory error (RMSE of camera translation, TUM methodology) on ICL-NUIM and TUM sequences; CNN trained on NYU Depth v2 only; monocular bas…",[2870,2871,2872],[568,23],[25,23],[14131,14132,14133,14134,14135,14136],"CNN-SLAM (Our Method)","LSD [4] (LSD-SLAM)","LSD-BS [4] (LSD-SLAM bootstrapped with ground-truth depth)","Laina [16] (CNN depth fed to point-based fusion)","ORB [20] (ORB-SLAM)","Remode [23] (REMODE, poses from LSD-BS)",[2881,119],{"slug":14139,"sourceId":6178,"sourceLabel":14140,"sourceYear":698,"table":13792,"note":14141,"datasets":14142,"metrics":14144,"families":14145,"methods":14146,"methodIds":14149,"rows":340,"failures":30},"droidslam2021-fig-4-table","Teed & Deng, 2021","ETH3D-SLAM RGB-D leaderboard AUC (accounts for error and catastrophic failures); network trained only on TartanAir",[14143],"ETH3D SLAM benchmark",[23],[23],[11926,5252,14147,3215,5233,6668,14148],"DROID-SLAM (Ours)","RFusion (Whelan et al. 2015, IJRR)",[9325,2897,6178,2915,2448,1511],{"slug":14151,"sourceId":6178,"sourceLabel":14140,"sourceYear":698,"table":69,"note":14152,"datasets":14153,"metrics":14154,"families":14155,"methods":14156,"methodIds":14158,"rows":356,"failures":154},"droidslam2021-table-1","TartanAir monocular benchmark, official test split, 'Hard' sequences MH000 to MH007; unit not printed",[9273],[23],[23],[14147,9906,10574,14157],"TartanVO",[6178,119],{"slug":14160,"sourceId":6178,"sourceLabel":14140,"sourceYear":698,"table":108,"note":14161,"datasets":14162,"metrics":14164,"families":14165,"methods":14166,"methodIds":14169,"rows":618,"failures":30},"droidslam2021-table-2","ECCV 2020 TartanAir SLAM competition score computed from normalized relative pose error over sub-trajectories of 5 to 40 m",[14163],"TartanAir test set",[23],[23],[14147,4738,14167,14168],"SuperGlue + SuperPoint + COLMAP","VOLDOR + COLMAP",[6178],{"slug":14171,"sourceId":6178,"sourceLabel":14140,"sourceYear":698,"table":17,"note":14172,"datasets":14173,"metrics":14174,"families":14175,"methods":14176,"methodIds":14181,"rows":14182,"failures":416},"droidslam2021-table-3","EuRoC monocular, ATE[m]; dagger rows (DeepV2D, TartanVO, D3VO, DSO, SVO, Ours odometry only) are visual odometry; '-' average not computed because of…",[743],[23],[23],[14177,14147,14178,14179,4728,2877,9906,14180,10574,892,4745,14157],"D3VO + DSO","DROID-SLAM (Ours, odometry only)","DSM","DeepV2D (TartanAir)",[6178,1507,119,763,1512],34,{"slug":14184,"sourceId":6178,"sourceLabel":14140,"sourceYear":698,"table":33,"note":14185,"datasets":14186,"metrics":14187,"families":14188,"methods":14189,"methodIds":14192,"rows":6179,"failures":800},"droidslam2021-table-4","TUM-RGBD freiburg1, all methods given monocular video except DeepTAM (RGB-D); X marks failure; unit not printed (EuRoC tables use m)",[2872],[23],[23],[14147,2877,14190,9906,14180,6668,892,14191],"DeepTAM (uses RGB-D)","TartanVO (uses ground truth to scale relative pose)",[6178,1511,763],{"slug":14194,"sourceId":6178,"sourceLabel":14140,"sourceYear":698,"table":244,"note":14195,"datasets":14196,"metrics":14197,"families":14198,"methods":14199,"methodIds":14200,"rows":2337,"failures":274},"droidslam2021-table-5","EuRoC stereo, ATE[m]; network trained on monocular synthetic video only",[743],[23],[23],[14177,14147,6668,892,4745,2382],[6178,1511,763,1512,1513],{"slug":14202,"sourceId":6178,"sourceLabel":14140,"sourceYear":698,"table":2713,"note":14203,"datasets":14204,"metrics":14205,"families":14206,"methods":14207,"methodIds":14208,"rows":274,"failures":30},"droidslam2021-text-sec-4","Average processing rate with two 3090 GPUs; downsampled to 320x512, every other frame skipped",[743,2872,9273],[148],[40],[14147],[6178],{"slug":14210,"sourceId":14211,"sourceLabel":14212,"sourceYear":374,"table":69,"note":14213,"datasets":14214,"metrics":14215,"families":14216,"methods":14217,"methodIds":14225,"rows":654,"failures":30},"dpvo2023-table-1","dpvo2023","Teed et al., 2023","TartanAir monocular test split (ECCV 2020 SLAM competition, 16 sequences), ATE with scale alignment; * = uses global optimization or loop closure; onl…",[9273],[329],[25],[14218,14219,14220,14221,14222,14223,14224],"COLMAP* [ 31 ]","DROID-SLAM* [ 37 ]","DROID-VO","DSO [ 12 ]","ORB-SLAM3* [ 4 ]","Ours (Default)","Ours (Fast)",[6178,1507,763],{"slug":14227,"sourceId":14211,"sourceLabel":14212,"sourceYear":374,"table":108,"note":14228,"datasets":14229,"metrics":14230,"families":14231,"methods":14232,"methodIds":14236,"rows":415,"failures":154},"dpvo2023-table-2","EuRoC MAV monocular VO, ATE[m] after similarity alignment with EVO (Appendix C); median of 5 runs for DPVO; every other frame skipped",[1482],[329],[25],[14233,14221,14223,14224,14234,14235],"DROID-VO [ 37 ]","SVO [ 15 ]","TartanVO [ 43 ]",[6178,1507,1512],{"slug":14238,"sourceId":14211,"sourceLabel":14212,"sourceYear":374,"table":17,"note":14239,"datasets":14240,"metrics":14241,"families":14242,"methods":14243,"methodIds":14246,"rows":2119,"failures":2420},"dpvo2023-table-3","TUM RGB-D freiburg1 monocular VO, ATE; x = method failed and output no trajectory; '-' = average not computed; median of 5 trials",[2872],[329],[25],[14233,14221,14244,14245,14223,14224],"DSO-Realtime [ 12 ]","ORB-SLAM3 [ 27 ]",[6178,1507,763],{"slug":14248,"sourceId":14211,"sourceLabel":14212,"sourceYear":374,"table":14249,"note":14250,"datasets":14251,"metrics":14253,"families":14254,"methods":14255,"methodIds":14256,"rows":120,"failures":30},"dpvo2023-text-sec-1-and-fig-10","Text Sec. 1 and Fig. 10","Average frame rate on RTX-3090 (DPVO Default 60 FPS, Fast 120 FPS; DROID-VO averages 40 FPS in VO mode)",[1482,14252],"EuRoC (memory); frame rate stated as average",[148,219],[40],[14220,14223,14224],[6178],{"slug":14258,"sourceId":14259,"sourceLabel":14260,"sourceYear":1234,"table":69,"note":14261,"datasets":14262,"metrics":14264,"families":14265,"methods":14266,"methodIds":14268,"rows":416,"failures":30},"thomson2013mlsindoor-table-1","thomson2013mlsindoor","Thomson et al., 2013","Mobile cloud registered to the Faro Focus3D reference by ICP in CloudCompare after manual removal of people and glass artefacts; residual cloud-to-clo…",[14263],"own survey, UCL South Cloisters ground-floor corridor",[3417],[78],[14267],"i-MMS (Viametris, trolley, 2D SLAM)",[],{"slug":14270,"sourceId":14259,"sourceLabel":14260,"sourceYear":1234,"table":108,"note":14261,"datasets":14271,"metrics":14272,"families":14273,"methods":14274,"methodIds":14276,"rows":416,"failures":30},"thomson2013mlsindoor-table-2",[14263],[3417],[78],[14275],"ZEB1 (3D Laser Mapping\u002FCSIRO, handheld)",[1180],{"slug":14278,"sourceId":14259,"sourceLabel":14260,"sourceYear":1234,"table":17,"note":14279,"datasets":14280,"metrics":14281,"families":14282,"methods":14283,"methodIds":14289,"rows":1042,"failures":30},"thomson2013mlsindoor-table-3","Width and height differences of doors and windows between parametric Revit 2014 models built from each point cloud and from the Focus3D reference or t…",[14263],[1819],[1821],[14284,14285,14286,14287,14288],"Leica TS15 survey vs Focus3D model (reference modelling uncertainty)","ZEB1 model vs Focus3D model","ZEB1 model vs Leica TS15 survey","i-MMS model vs Focus3D model","i-MMS model vs Leica TS15 survey",[1180],{"slug":14291,"sourceId":14259,"sourceLabel":14260,"sourceYear":1234,"table":33,"note":14279,"datasets":14292,"metrics":14293,"families":14294,"methods":14295,"methodIds":14296,"rows":1042,"failures":30},"thomson2013mlsindoor-table-4",[14263],[1819],[1821],[14284,14285,14286,14287,14288],[1180],{"slug":14298,"sourceId":6213,"sourceLabel":14299,"sourceYear":11940,"table":14300,"note":14301,"datasets":14302,"metrics":14305,"families":14306,"methods":14307,"methodIds":14311,"rows":274,"failures":154},"thrun2000-3dmapping-text-sec-3-3-3-5","Thrun et al., 2000","Text Sec. 3.3, 3.5","3D model of a cyclic corridor map about 60 m long; polygon count before simplification",[14303,14304],"DARPA demonstration run (Urban Robot)","authors' dual-laser Pioneer data",[23],[23],[14308,14309,14310],"proposed incremental mapping","raw polygon model","simplified polygonal model",[6213],{"slug":14313,"sourceId":14314,"sourceLabel":14315,"sourceYear":306,"table":91,"note":14316,"datasets":14317,"metrics":14319,"families":14320,"methods":14321,"methodIds":14329,"rows":2252,"failures":30},"kimeramulti2022-table-i","kimeramulti2022","Tian et al., 2022","ATE in meters against ground truth for distributed trajectory estimators on Kimera-VIO odometry plus putative loops; fixed isotropic covariance (0.01…",[14318,1482],"DCIST simulation",[329],[25],[14322,14323,14324,14325,14326,14327,14328],"Centralized GNC","D-GNC","D-GNC (ES, early stopping)","D-GNC (NI, naive initialization)","L2 (least squares, RBCD)","PCM","PCM + D-GNC",[14314,14330],"yang2020gnc",{"slug":14332,"sourceId":14314,"sourceLabel":14315,"sourceYear":306,"table":325,"note":14333,"datasets":14334,"metrics":14335,"families":14336,"methods":14337,"methodIds":14343,"rows":721,"failures":30},"kimeramulti2022-table-ii","Communication usage (total of place recognition, geometric verification and distributed PGO) versus centralized baselines transmitting images or keypo…",[14318,1482],[23],[23],[14338,14339,14322,14340,14341,14342],"Centralized (Images)","Centralized (Keypoints)","D-GNC distributed","D-GNC distributed (ES)","Kimera-Multi total communication",[14314,14330],{"slug":14345,"sourceId":14314,"sourceLabel":14315,"sourceYear":306,"table":279,"note":14346,"datasets":14347,"metrics":14348,"families":14349,"methods":14350,"methodIds":14353,"rows":29,"failures":30},"kimeramulti2022-table-iii","Semantic label accuracy of meshes against the simulator's ground-truth labels after ICP registration (Open3D) of meshes sampled at 10^3 points per m^2…",[14318],[23],[23],[14351,14352],"Kimera-Semantics","LMO (Kimera-Multi local mesh optimization)",[1508,14314],{"slug":14355,"sourceId":14314,"sourceLabel":14315,"sourceYear":306,"table":818,"note":14356,"datasets":14357,"metrics":14360,"families":14361,"methods":14362,"methodIds":14366,"rows":102,"failures":30},"kimeramulti2022-table-v","Outdoor datasets without ground truth: each robot starts and ends at the same place; end-to-end position error; Kimera-Multi uses D-GNC (Stata with fu…",[14358,14359],"Medfield outdoor dataset (authors' own)","Stata outdoor dataset (authors' own)",[1551],[1553],[14363,14364,14365],"Centralized","Kimera-Multi","Kimera-VIO",[1508,14314,14330],{"slug":14368,"sourceId":14369,"sourceLabel":14370,"sourceYear":374,"table":69,"note":14371,"datasets":14372,"metrics":14374,"families":14375,"methods":14376,"methodIds":14381,"rows":1069,"failures":356},"vegatorres2023ogm2pgbm-table-1","vegatorres2023ogm2pgbm","Torres et al., 2023","Pose tracking in Gazebo with a Robotnik SUMMIT-XL and Hokuyo UST-10LX; maps from an as-designed IFC model; three scenarios with increasing Scan-BIM de…",[14373],"Gazebo simulation from an as-designed IFC model, three scenarios (building not named in the paper)",[568,23],[25,23],[14377,14378,14379,14380],"AMCL","Cartographer (pure localization with .pbstream from OGM2PGBM)","GMCL","SLAM Toolbox (localization with prior .posegraph from OGM2PGBM)",[2116,14369],{"slug":14383,"sourceId":14384,"sourceLabel":14385,"sourceYear":68,"table":11621,"note":14386,"datasets":14387,"metrics":14388,"families":14389,"methods":14390,"methodIds":14397,"rows":2882,"failures":30},"tosi2026survey-table-xi","tosi2026survey","Tosi et al., 2026","Authors' own benchmark of methods with public code on a single NVIDIA RTX 3090: peak GPU memory and average FPS (total sequence time divided by number…",[1997,3223],[148,219],[40],[14391,3166,14392,3168,9907,3174,8107,14393,14394,4778,14395,3175,3177,14396,3179,13734],"ADFP","DIM-SLAM","Nerf-LOAM","Orbeez-SLAM","Plenoxel-SLAM","UncLe-SLAM",[3181,3182,6378,3248,3184,4785,3185,3186],{"slug":14399,"sourceId":14400,"sourceLabel":14401,"sourceYear":11940,"table":14402,"note":14403,"datasets":14404,"metrics":14406,"families":14407,"methods":14408,"methodIds":14413,"rows":618,"failures":30},"triggs2000ba-text-sec-7-2-fig-5-legend","triggs2000ba","Triggs et al., 2000","Text Sec.7.2 (Fig. 5 legend)","Small but ill-conditioned synthetic bundle problem; convergence near the minimum; step and flop counts printed in the figure legend",[14405],"small but ill-conditioned bundle problem (whether synthetic or real is not stated)",[23],[23],[14409,14410,14411,14412],"Diag. Precond. Conjugate Gradient","Gauss-Newton","Resect-Intersect with line search","Resect-Intersect without line search",[],{"slug":14415,"sourceId":14400,"sourceLabel":14401,"sourceYear":11940,"table":7688,"note":14416,"datasets":14417,"metrics":14419,"families":14420,"methods":14421,"methodIds":14423,"rows":154,"failures":154},"triggs2000ba-text-sec-7-4","Weak 'strip' geometry with 16 or more images",[14418],"synthetic strip (weak geometry)",[23],[23],[14422],"Resect-Intersect (resection-intersection)",[],{"slug":14425,"sourceId":14400,"sourceLabel":14401,"sourceYear":11940,"table":14426,"note":14427,"datasets":14428,"metrics":14430,"families":14431,"methods":14432,"methodIds":14437,"rows":654,"failures":30},"triggs2000ba-text-sec-7-4-fig-6-legend","Text Sec.7.4 (Fig. 6 legend)","Synthetic projective bundle problems with points increasing in proportion to images; strong 'spherical cloud' geometry (all points in all images, stro…",[14429,14418],"synthetic spherical cloud (strong geometry)",[23],[23],[14433,14434,14435,14436],"Dense Gauss-Newton","Diag. Conj. Gradient","Resect-Intersect","Sparse Gauss-Newton",[],{"slug":14439,"sourceId":14440,"sourceLabel":14441,"sourceYear":374,"table":108,"note":14442,"datasets":14443,"metrics":14445,"families":14446,"methods":14447,"methodIds":14451,"rows":641,"failures":30},"trybala2023lowcosttunnel-table-2","trybala2023lowcosttunnel","Trybała et al., 2023","Standard deviations vs the RIEGL VZ-400i reference: M3C2 signed distances for the whole cloud after rigid ICP registration; ICP fit of two cross-secti…",[14444],"own survey, Gontowa adit (Owl Mountains, Poland)",[3919,23],[78,23],[14448,14449,14450],"Actuated Velodyne (A-LOAM odometry + Scan Context++ + GTSAM in SC-LiDAR-SLAM)","GeoSLAM ZEB Horizon (proprietary SLAM)","Livox Horizon (FAST-LIO odometry + Scan Context++ + GTSAM in SC-LiDAR-SLAM)",[],{"slug":14453,"sourceId":14440,"sourceLabel":14441,"sourceYear":374,"table":17,"note":14454,"datasets":14455,"metrics":14456,"families":14457,"methods":14458,"methodIds":14459,"rows":102,"failures":30},"trybala2023lowcosttunnel-table-3","Voxel occupancy vs the reference voxel model after the proposed sequential voxel-wise ICP drift compensation (non-rigid, evaluation only); TP occupied…",[14444],[23],[23],[14448,14449,14450],[],{"slug":14461,"sourceId":14462,"sourceLabel":14463,"sourceYear":374,"table":108,"note":14464,"datasets":14465,"metrics":14466,"families":14467,"methods":14468,"methodIds":14470,"rows":641,"failures":30},"trzeciak2023conslam-table-2","trzeciak2023conslam","Trzeciak et al., 2023","Quality of the ground-truth TLS registration: distance-constrained RMSE (RMSEd, d = 1 cm) between overlapping registered TLS scans after 5 mm downsamp…",[8315],[3417],[78],[14469],"TLS multi-view registration by land surveyors (Leica RTC360, bundle adjustment, partly georeferenced)",[],{"slug":14472,"sourceId":14462,"sourceLabel":14463,"sourceYear":374,"table":14473,"note":14474,"datasets":14475,"metrics":14476,"families":14477,"methods":14478,"methodIds":14480,"rows":63,"failures":63},"trzeciak2023conslam-text-data-collection-system","Text Data collection system","Software time-synchronisation quality of the PointPix sensor streams.",[8315],[23],[23],[14479],"ROS approximate-time synchronisation (\u003C10 ms matching threshold)",[],{"slug":14482,"sourceId":14462,"sourceLabel":14463,"sourceYear":374,"table":14483,"note":14484,"datasets":14485,"metrics":14486,"families":14487,"methods":14488,"methodIds":14491,"rows":63,"failures":30},"trzeciak2023conslam-text-ground-truth-trajectories","Text Ground-truth trajectories","Share of key LiDAR scans registered to the TLS ground truth by the edge-feature ICP pipeline.",[8315],[1933],[1935],[14489,14490],"edge-feature ICP of key LiDAR scans to TLS scans, initialised from SLAM key poses (ground-truth trajectory recovery)","ground-truth trajectory recovery in the ECCV 2022 workshop version",[],{"slug":14493,"sourceId":14462,"sourceLabel":14463,"sourceYear":374,"table":14494,"note":14495,"datasets":14496,"metrics":14497,"families":14498,"methods":14499,"methodIds":14500,"rows":63,"failures":63},"trzeciak2023conslam-text-practical-application","Text Practical application","Demonstration on sequence 5; SLAM key poses re-expressed relative to their first pose (first-pose alignment); value stated qualitatively in text about…",[8315],[23],[23],[4330,334],[392,338],{"slug":14502,"sourceId":8379,"sourceLabel":14503,"sourceYear":2267,"table":91,"note":14504,"datasets":14505,"metrics":14507,"families":14508,"methods":14509,"methodIds":14513,"rows":849,"failures":30},"tuna2024xicp-table-i","Tuna et al., 2024","Seemuhle underground mine, ANYmal with VLP-16, 521.8 m; APE via EVO against Leica RTC 360 ground truth, mu (sigma); 'first 15 m' = trajectory aligned…",[14506],"Seemuhle underground mine (authors' data)",[329,1551],[25,1553],[14510,14511,14512],"Hinduja et al. [17]","X-ICP (Proposed)","Zhang et al. [12]",[5430,8379,5431],{"slug":14515,"sourceId":8379,"sourceLabel":14503,"sourceYear":2267,"table":325,"note":14516,"datasets":14517,"metrics":14518,"families":14519,"methods":14520,"methodIds":14521,"rows":224,"failures":30},"tuna2024xicp-table-ii","RPE per 10 m traversed distance, Seemuhle mine with VLP-16, mu (sigma)",[14506],[2411,330],[332],[14510,14511,14512],[5430,8379,5431],{"slug":14523,"sourceId":8379,"sourceLabel":14503,"sourceYear":2267,"table":279,"note":14524,"datasets":14525,"metrics":14526,"families":14527,"methods":14528,"methodIds":14530,"rows":102,"failures":30},"tuna2024xicp-table-iii","Ablation, Seemuhle VLP-16 APE (same protocol as Table I); Xs-ICP drops the partial-localizability category and categorizes only in the first ICP itera…",[14506],[329,1551],[25,1553],[14511,14529],"Xs-ICP (Proposed)",[8379],{"slug":14532,"sourceId":8379,"sourceLabel":14503,"sourceYear":2267,"table":731,"note":14533,"datasets":14534,"metrics":14535,"families":14536,"methods":14537,"methodIds":14538,"rows":618,"failures":30},"tuna2024xicp-table-iv","Ablation, RPE per 10 m traversed distance, Seemuhle VLP-16, mu (sigma)",[14506],[2411,330],[332],[14511,14529],[8379],{"slug":14540,"sourceId":8379,"sourceLabel":14503,"sourceYear":2267,"table":818,"note":14541,"datasets":14542,"metrics":14544,"families":14545,"methods":14546,"methodIds":14550,"rows":224,"failures":30},"tuna2024xicp-table-v","Scan-to-map registration time per scan on the Rumlang construction-site data, single-threaded, mu (sigma); baseline statistics computed only until 150…",[14543],"Rumlang construction site (authors' data)",[38],[40],[14547,14548,14549],"Baseline (scan-to-map ICP without localizability awareness)","X-ICP","Xs-ICP",[8379],{"slug":14552,"sourceId":14553,"sourceLabel":14554,"sourceYear":562,"table":17,"note":14555,"datasets":14556,"metrics":14558,"families":14559,"methods":14560,"methodIds":14572,"rows":1069,"failures":30},"tuna2025informed-table-3","tuna2025informed","Tuna et al., 2025","Dynamic ANYmal simulation with Open3D SLAM in the loop; prior noise σt 0.05 m, σr 0.01 rad; EVO metrics",[14557],"ANYmal simulation",[329,2411,330],[25,332],[286,14561,14562,14563,14564,14565,14566,14567,14568,14569,14570,14571],"Eq. Con.","Gelfand et al.","InEq. Con.","L-Reg.","NL-Reg.","NL-Solver","P2Plane","Prior Only","RMS","TSVD","Zhang et al.",[277,4594,8379,5431],{"slug":14574,"sourceId":14553,"sourceLabel":14554,"sourceYear":562,"table":33,"note":14575,"datasets":14576,"metrics":14578,"families":14579,"methods":14580,"methodIds":14585,"rows":721,"failures":30},"tuna2025informed-table-4","ANYmal forest to open-field run (107 m before revisit), leg-odometry prior, GNSS position ground truth; ICP-loop cost",[14577],"ANYmal forest experiment",[22,330,38],[25,40,332],[286,14561,14562,14581,14582,14583,14566,14584,14568,14569,14570,14571],"Ineq. Con.","L-Reg","NL-Reg","P2plane",[277,4594,8379,5431],{"slug":14587,"sourceId":14553,"sourceLabel":14554,"sourceYear":562,"table":244,"note":14588,"datasets":14589,"metrics":14591,"families":14592,"methods":14593,"methodIds":14594,"rows":721,"failures":30},"tuna2025informed-table-5","Ulmberg bicycle tunnel, handheld payload, COIN-LIO prior, one-directional degeneracy over more than 80% of the run; ICP registration cost",[14590],"ENWIDE (Ulmberg bicycle tunnel)",[22,330,38],[25,40,332],[286,14561,14562,14581,14582,14583,14566,14584,14568,14569,14570,14571],[277,4594,8379,5431],{"slug":14596,"sourceId":14553,"sourceLabel":14554,"sourceYear":562,"table":1072,"note":14597,"datasets":14598,"metrics":14600,"families":14601,"methods":14602,"methodIds":14603,"rows":224,"failures":224},"tuna2025informed-table-6","Qualitative map outcome, HEAP Excavator column only (no visible drift, minimal drift, broken map)",[14599],"HEAP excavator experiment",[23],[23],[286,14561,14562,14581,14582,14583,14566,14584,14568,14569,14570,14571],[277,4594,8379,5431],{"slug":14605,"sourceId":14606,"sourceLabel":14607,"sourceYear":68,"table":69,"note":14608,"datasets":14609,"metrics":14612,"families":14613,"methods":14614,"methodIds":14616,"rows":1810,"failures":30},"tuomisto2026quadrupedbim-table-1","tuomisto2026quadrupedbim","Tuomisto et al., 2026","Task-level metrics; simulated experiments are 20 trials each in an Unreal Engine 5 digital twin (continuous metrics: mean with t-distribution 95% CI;…",[14610,14611],"Unreal Engine 5 digital twin","own real-world trial",[23,1933],[23,1935],[14615],"proposed framework (Ours)",[14606],{"slug":14618,"sourceId":14606,"sourceLabel":14607,"sourceYear":68,"table":108,"note":14619,"datasets":14620,"metrics":14621,"families":14622,"methods":14623,"methodIds":14626,"rows":608,"failures":30},"tuomisto2026quadrupedbim-table-2","Simulated comparison with ablated variants: Non-Semantic (standard binary voxel map, no BIM-aware update and cost logic) and No NBV Retries (no viewpo…",[14610],[23,1933],[23,1935],[14624,14625,14615],"No NBV Retries","Non-Semantic",[14606],{"slug":14628,"sourceId":14606,"sourceLabel":14607,"sourceYear":68,"table":17,"note":14629,"datasets":14630,"metrics":14631,"families":14632,"methods":14633,"methodIds":14634,"rows":608,"failures":30},"tuomisto2026quadrupedbim-table-3","Per-object VLM accuracy (%) with Wilson 95% CI, pooled over the 20 trials of each simulated experiment; images that passed the qualitative success cri…",[14610],[23],[23],[14615],[14606],{"slug":14636,"sourceId":14637,"sourceLabel":14638,"sourceYear":14639,"table":1158,"note":14640,"datasets":14641,"metrics":14643,"families":14644,"methods":14645,"methodIds":14648,"rows":63,"failures":63},"umeyama1991least-text-sec-iii","umeyama1991least","Umeyama, 1991",1991,"Numerical example with three 2D point pairs (Fig. 1); least mean squared error of the returned similarity transform.",[14642],"numerical example (Fig. 1)",[23],[23],[14646,14647],"Arun and Horn's method (equivalent to S = I)","proposed closed-form solution (Theorem, Eq. 40-43)",[14637],{"slug":14650,"sourceId":14651,"sourceLabel":14652,"sourceYear":213,"table":91,"note":14653,"datasets":14654,"metrics":14655,"families":14656,"methods":14657,"methodIds":14671,"rows":4709,"failures":274},"basalt2020-table-i","basalt2020","Usenko et al., 2020","EuRoC MAV; RMS ATE (m) after alignment with ground truth; upper part VIO methods (pose per frame), lower part mapping methods on keyframes (KF); X = f…",[743],[568],[25],[14658,14659,14660,14661,14662,14663,14664,14665,14666,14667,14668,14669,14670],"BA + Identity Factors, stereo, KF (ablation)","IS VIO stereo","OKVIS mono","OKVIS stereo","Proposed VI Mapping, stereo, KF","Proposed VIO, stereo","Pure BA, stereo, KF (ablation)","VI DSO, mono","VI ORB-SLAM mono, KF","VI SLAM (Kasyanov et al.) mono, KF","VI SLAM (Kasyanov et al.) stereo, KF","VINS FUSION mono","VINS FUSION stereo",[1510,1513],{"slug":14673,"sourceId":14651,"sourceLabel":14652,"sourceYear":213,"table":325,"note":14674,"datasets":14675,"metrics":14676,"families":14677,"methods":14678,"methodIds":14684,"rows":416,"failures":30},"basalt2020-table-ii","Mean processing time of the mapping subsystem on EuRoC normalised by the number of keyframes; Intel E5-1620 (4 cores, 8 virtual cores)",[743],[38],[40],[14679,14680,14681,14682,14683],"Proposed VI Mapping (Factor Extraction)","Proposed VI Mapping (Keypoint detection)","Proposed VI Mapping (Matching and Triangulation)","Proposed VI Mapping (Optimization (10 iterations))","Proposed VI Mapping (Total)",[],{"slug":14686,"sourceId":14651,"sourceLabel":14652,"sourceYear":213,"table":14687,"note":14688,"datasets":14689,"metrics":14690,"families":14691,"methods":14692,"methodIds":14694,"rows":416,"failures":30},"basalt2020-text-sec-vi-d","Text Sec.VI-d","VIO timing on EuRoC",[743],[23,38],[40,23],[14693,14663],"Proposed VI Mapping, stereo",[],{"slug":14696,"sourceId":7177,"sourceLabel":14697,"sourceYear":107,"table":108,"note":14698,"datasets":14699,"metrics":14704,"families":14705,"methods":14706,"methodIds":14710,"rows":224,"failures":30},"pointnetvlad2018-table-2","Uy & Lee, 2018","Baseline networks trained on Oxford only; average recall at top 1%; success if retrieved submap within 25 m",[14700,14701,14702,14703],"In-house B.D.","In-house R.A.","In-house U.S.","Oxford RobotCar benchmark",[23],[23],[14707,14708,14709],"PN_MAX (PointNet + maxpool + FC)","PN_STD (PointNet trained on ModelNet)","PN_VLAD (PointNetVLAD)",[7177],{"slug":14712,"sourceId":7177,"sourceLabel":14697,"sourceYear":107,"table":17,"note":14713,"datasets":14714,"metrics":14715,"families":14716,"methods":14717,"methodIds":14724,"rows":608,"failures":30},"pointnetvlad2018-table-3","Output dimensionality D of the global descriptor; trained on Oxford; average recall at top 1%",[14700,14701,14702,14703],[23],[23],[14718,14719,14720,14721,14722,14723],"PN_MAX (PointNet + maxpool + FC) D-128","PN_MAX (PointNet + maxpool + FC) D-256","PN_MAX (PointNet + maxpool + FC) D-512","PN_VLAD (PointNetVLAD) D-128","PN_VLAD (PointNetVLAD) D-256","PN_VLAD (PointNetVLAD) D-512",[7177],{"slug":14726,"sourceId":7177,"sourceLabel":14697,"sourceYear":107,"table":33,"note":14727,"datasets":14728,"metrics":14729,"families":14730,"methods":14731,"methodIds":14736,"rows":356,"failures":30},"pointnetvlad2018-table-4","PN_VLAD trained and tested on Oxford with different losses; average recall at top 1%",[14703],[23],[23],[14732,14733,14734,14735],"PN_VLAD with Lazy Quadruplet Loss","PN_VLAD with Lazy Triplet Loss","PN_VLAD with Quadruplet Loss","PN_VLAD with Triplet Loss",[7177],{"slug":14738,"sourceId":7177,"sourceLabel":14697,"sourceYear":107,"table":244,"note":14739,"datasets":14740,"metrics":14741,"families":14742,"methods":14743,"methodIds":14744,"rows":608,"failures":30},"pointnetvlad2018-table-5","Refined networks trained on Oxford, U.S. and R.A. (B.D. unseen); average recall at top 1% and at top 1",[14700,14701,14702,14703],[23],[23],[14707,14708,14709],[7177],{"slug":14746,"sourceId":7177,"sourceLabel":14697,"sourceYear":107,"table":2391,"note":14747,"datasets":14748,"metrics":14749,"families":14750,"methods":14751,"methodIds":14752,"rows":154,"failures":30},"pointnetvlad2018-text-sec-5-2","Inference time of the network (TensorFlow); retrieval stated as O(log n)",[2070],[38],[40],[14709],[7177],{"slug":14754,"sourceId":14755,"sourceLabel":14756,"sourceYear":374,"table":69,"note":14757,"datasets":14758,"metrics":14760,"families":14761,"methods":14762,"methodIds":14765,"rows":618,"failures":30},"bimslam2023-table-1","bimslam2023","Vega Torres et al., 2023","Single simulated Gazebo sequence (Robotnik SUMMIT XL with Velodyne VLP-16) in the BIM-derived environment; translation error (cm) and rotation error (…",[14759],"Gazebo simulated sequence",[23],[23],[14763,14764],"BIM-SLAM","SC-A-LOAM",[14755],{"slug":14767,"sourceId":14768,"sourceLabel":14769,"sourceYear":2267,"table":69,"note":14770,"datasets":14771,"metrics":14772,"families":14773,"methods":14774,"methodIds":14778,"rows":2262,"failures":30},"vegatorres2024slam2ref-table-1","vegatorres2024slam2ref","Vega-Torres et al., 2024","ConSLAM S2-S5 aligned to the per-sequence TLS point cloud; APE RMSE against the SLAM2REF final-ICP poses, which the authors use as ground truth; evo,…",[8315],[329,23],[25,23],[14775,1436,14776,14777],"ConSLAM (ground-truth poses supplied with the dataset)","KNN (after KNN loops and optimization; SLAM2REF intermediate stage)","SC (DLIO after Indoor Scan Context loop detection and optimization; SLAM2REF intermediate stage)",[1392,14462,14768],{"slug":14780,"sourceId":14768,"sourceLabel":14769,"sourceYear":2267,"table":6743,"note":14781,"datasets":14782,"metrics":14783,"families":14784,"methods":14785,"methodIds":14787,"rows":63,"failures":30},"vegatorres2024slam2ref-text-sec-6","ConSLAM S2 aligned to a BIM modelled from the S2 TLS cloud; APE RMSE after the final ICP against the TLS-derived reference poses",[8315],[329,23],[25,23],[14786],"SLAM2REF with BIM reference (after final ICP)",[14768],{"slug":14789,"sourceId":14790,"sourceLabel":14791,"sourceYear":107,"table":91,"note":14792,"datasets":14793,"metrics":14794,"families":14795,"methods":14796,"methodIds":14800,"rows":598,"failures":120},"supereight2018-table-i","supereight2018","Vespa et al., 2018","ATE RMSE (Euclidean distance between ground-truth and estimated positions) on ICL-NUIM living room and TUM RGB-D; 1 cm finest voxels, depth-only track…",[2870,2872],[568],[25],[14797,14798,14799],"InfiniTAM [16] (default depth-only tracker)","OFusion (octree occupancy fusion, ours)","TSDF (octree TSDF fusion, ours)",[7643,14790],{"slug":14802,"sourceId":14790,"sourceLabel":14791,"sourceYear":107,"table":325,"note":14803,"datasets":14804,"metrics":14805,"families":14806,"methods":14807,"methodIds":14808,"rows":29,"failures":30},"supereight2018-table-ii","Memory of the octree maps relative to a statically pre-allocated grid covering the same area at the same resolution (as in KinectFusion)",[2870,2872],[219],[40],[14798,14799],[14790],{"slug":14810,"sourceId":14790,"sourceLabel":14791,"sourceYear":107,"table":279,"note":14811,"datasets":14812,"metrics":14814,"families":14815,"methods":14816,"methodIds":14819,"rows":120,"failures":30},"supereight2018-table-iii","Time to find the first feasible straight-line path with Informed RRT* (OMPL) for an obstructed 2.83 m start-goal distance, 1 cm map, averaged over 10,…",[14813],"not_reported (map built by the SLAM system)",[23],[23],[14817,14818],"OFusion (octree occupancy map, ours)","Octomap",[3527,14790],{"slug":14821,"sourceId":14790,"sourceLabel":14791,"sourceYear":107,"table":731,"note":14822,"datasets":14823,"metrics":14824,"families":14825,"methods":14826,"methodIds":14827,"rows":120,"failures":30},"supereight2018-table-iv","Time for linear optimization of a collision-free polynomial trajectory from the RRT* plan, averaged over 1,000 executions",[14813],[23],[23],[14817,14818],[3527,14790],{"slug":14829,"sourceId":14790,"sourceLabel":14791,"sourceYear":107,"table":1430,"note":14830,"datasets":14831,"metrics":14832,"families":14833,"methods":14834,"methodIds":14836,"rows":63,"failures":154},"supereight2018-text-sec-v-b","OctoMap mapping time per frame with 5 cm voxels on the same test sequences (omitted from Fig. 6)",[2872],[38],[40],[14835],"Octomap (5 cm voxels)",[3527],{"slug":14838,"sourceId":3264,"sourceLabel":14839,"sourceYear":698,"table":91,"note":14840,"datasets":14841,"metrics":14842,"families":14843,"methods":14844,"methodIds":14847,"rows":224,"failures":30},"vizzo2021puma-table-i","Vizzo et al., 2021","Mai City synthetic urban scans; all methods use ground-truth poses; maps densely sampled at 1,000 points per cm2; metrics after Knapitsch et al.; thre…",[12808],[2343,74,75,76],[78],[81,14845,14846],"Surfels [1]","TSDF [21]",[432,3264],{"slug":14849,"sourceId":3264,"sourceLabel":14839,"sourceYear":698,"table":325,"note":14850,"datasets":14851,"metrics":14852,"families":14853,"methods":14854,"methodIds":14864,"rows":9622,"failures":30},"vizzo2021puma-table-ii","KITTI odometry training sequences 00-10; relative errors averaged over 100-800 m segments; all methods share the range-image normals and Huber loss; M…",[4494],[438,439],[441],[14855,14856,14857,14858,14859,14860,14861,14862,14863],"GICP [33] (map: None, DA: NN)","GICP [33] (map: Point cloud, DA: NN)","Ours (Δtree = 10) (map: Mesh, DA: NN)","Ours (Δtree = 10) (map: Mesh, DA: RC)","SuMa [1] (map: None, DA: Proj.)","point-to-plane ICP [32] (map: None, DA: NN)","point-to-plane ICP [32] (map: Point cloud, DA: NN)","point-to-point ICP [3] (map: None, DA: NN)","point-to-point ICP [3] (map: Point cloud, DA: NN)",[478,3310,3292,432,3264],{"slug":14866,"sourceId":3264,"sourceLabel":14839,"sourceYear":698,"table":279,"note":14867,"datasets":14868,"metrics":14869,"families":14870,"methods":14871,"methodIds":14874,"rows":102,"failures":30},"vizzo2021puma-table-iii","Registration of every scan to the local mesh on the full KITTI training sequences; Mesh vertex-sampling = standard point-to-plane ICP on sampled mesh…",[4494],[438,439,38],[40,441],[14872,14873],"Mesh ray-casting (proposed)","Mesh vertex-sampling (point-to-plane ICP on sampled mesh vertices, nearest neighbours)",[3264],{"slug":14876,"sourceId":3264,"sourceLabel":14839,"sourceYear":698,"table":14877,"note":14878,"datasets":14879,"metrics":14880,"families":14881,"methods":14882,"methodIds":14884,"rows":63,"failures":30},"vizzo2021puma-text-sec-iv-c-fig-4-labels","Text Sec.IV-C (Fig. 4 labels)","Final size of the PUMA triangle-mesh map printed in Fig. 4 for two KITTI sequences (raw geometry only); comparison curves for point clouds, surfels an…",[4494],[219],[40],[14883],"Ours (triangle mesh map)",[3264],{"slug":14886,"sourceId":3264,"sourceLabel":14839,"sourceYear":698,"table":14887,"note":14888,"datasets":14889,"metrics":14890,"families":14891,"methods":14892,"methodIds":14894,"rows":274,"failures":30},"vizzo2021puma-text-sec-iv-f","Text Sec.IV-F","Per-scan runtime of the full PUMA pipeline as stated in text; dataset not named in this paragraph",[2070],[38],[40],[14893],"Ours (PUMA)",[3264],{"slug":14896,"sourceId":3265,"sourceLabel":14897,"sourceYear":306,"table":108,"note":14898,"datasets":14899,"metrics":14901,"families":14902,"methods":14903,"methodIds":14909,"rows":578,"failures":30},"vizzo2022vdbfusion-table-2","Vizzo et al., 2022","Integration rate averaged over the whole sequence, timers stopped during data loading and conversion; all methods single-threaded C++; KITTI 07 at 10…",[14900,4494],"Cow and Lady",[148],[40],[14904,14905,14906,14907,14908],"Octomap (with space carving)","VDBFusion (with space carving)","VDBFusion (without space carving)","Voxblox (with space carving)","Voxblox (without space carving)",[3527,85,3265],{"slug":14911,"sourceId":3265,"sourceLabel":14897,"sourceYear":306,"table":17,"note":14912,"datasets":14913,"metrics":14914,"families":14915,"methods":14916,"methodIds":14919,"rows":356,"failures":30},"vizzo2022vdbfusion-table-3","VDBFusion Python API versus C++ API integration rate, same data and settings as Table 2 (without space carving)",[14900,4494],[148],[40],[14917,14918],"VDBFusion C++","VDBFusion Python",[3265],{"slug":14921,"sourceId":3265,"sourceLabel":14897,"sourceYear":306,"table":33,"note":14922,"datasets":14923,"metrics":14924,"families":14925,"methods":14926,"methodIds":14930,"rows":578,"failures":63},"vizzo2022vdbfusion-table-4","RAM of the internal map after integrating the whole sequence; point cloud and dense voxel grid values are computed analytically (3 floats per point; d…",[14900,4494],[219],[40],[14927,14818,14928,14929,82],"Dense Voxel Grid","Point Cloud","VDBFusion",[3527,85,3265],{"slug":14932,"sourceId":3265,"sourceLabel":14897,"sourceYear":306,"table":244,"note":14933,"datasets":14934,"metrics":14935,"families":14936,"methods":14937,"methodIds":14942,"rows":578,"failures":30},"vizzo2022vdbfusion-table-5","Disk size of the serialized map; point cloud = all scans aggregated and saved in binary with Open3D; Octomap = 0\u002F1 maximum-likelihood output; Voxblox…",[14900,4494],[23],[23],[14938,14928,14939,14940,14941],"Octomap 0\u002F1 Output","VDBFusion Mesh Export","VDBFusion TSDF Volume","Voxblox Mesh Export",[3527,85,3265],{"slug":14944,"sourceId":3265,"sourceLabel":14897,"sourceYear":306,"table":1072,"note":14945,"datasets":14946,"metrics":14947,"families":14948,"methods":14949,"methodIds":14950,"rows":14951,"failures":154},"vizzo2022vdbfusion-table-6","Point-to-point distance (m) between densely sampled maps (KITTI 100,000,000 points, Cow and Lady 1,000,000 points) and the reference cloud; KITTI refe…",[14900,4494],[3417],[78],[14904,14905,14906,14907,14908],[3527,85,3265],19,{"slug":14953,"sourceId":3265,"sourceLabel":14897,"sourceYear":306,"table":14954,"note":14955,"datasets":14956,"metrics":14957,"families":14958,"methods":14959,"methodIds":14961,"rows":63,"failures":30},"vizzo2022vdbfusion-text-sec-2","Text Sec.2","Text claim in Related Work: fusion rate under the same constraints and on the same dataset [45] (Newer College) as the SuperEight LiDAR extension; Sup…",[3253],[148],[40],[14960,14929],"SuperEight LiDAR extension [34]",[3265],{"slug":14963,"sourceId":577,"sourceLabel":14964,"sourceYear":374,"table":325,"note":14965,"datasets":14966,"metrics":14967,"families":14968,"methods":14969,"methodIds":14977,"rows":29,"failures":154},"kissicp2023-table-ii","Vizzo et al., 2023","KITTI benchmark with already motion-compensated scans; deskewing disabled for KISS-ICP and CT-ICP; average relative translational error; Seq. 11-21 fr…",[469],[438],[441],[14970,14971,11530,14972,14973,14974,14975,14976],"CT-ICP [10] (SLAM)","F-LOAM [33]","MULLS [21]","MULLS [21] (SLAM)","Ours (KISS-ICP)","SuMa [1]","SuMa++ [1] (SLAM)",[596,393,577,597,432,1968],{"slug":14979,"sourceId":577,"sourceLabel":14964,"sourceYear":374,"table":279,"note":14980,"datasets":14981,"metrics":14982,"families":14983,"methods":14984,"methodIds":14985,"rows":2119,"failures":30},"kissicp2023-table-iii","MulRan; values are averages over the three runs per sequence; CT-ICP not evaluated because it lacks MulRan support",[671],[329,438,23],[25,441,23],[14971,14972,14974,14975],[393,577,597,432],{"slug":14987,"sourceId":577,"sourceLabel":14964,"sourceYear":374,"table":731,"note":14988,"datasets":14989,"metrics":14990,"families":14991,"methods":14992,"methodIds":14994,"rows":224,"failures":274},"kissicp2023-table-iv","Relative translational error (%); CT-ICP NCLT value copied from its paper because the authors could not reproduce it; authors flag NCLT ground-truth e…",[310,3253],[438],[441],[14993,14971,14972,14974],"CT-ICP [10]",[596,393,577,597],{"slug":14996,"sourceId":577,"sourceLabel":14964,"sourceYear":374,"table":818,"note":14997,"datasets":14998,"metrics":14999,"families":15000,"methods":15001,"methodIds":15005,"rows":641,"failures":30},"kissicp2023-table-v","KITTI-raw without motion compensation, sequences matching the odometry benchmark; average frequency reported without hardware",[4342],[438,148,23],[40,441,23],[14993,11530,14972,15002,15003,15004],"Ours + Deskewing (CV)","Ours + Deskewing (IMU)","Ours without deskewing",[596,577,597],{"slug":15007,"sourceId":577,"sourceLabel":14964,"sourceYear":374,"table":827,"note":15008,"datasets":15009,"metrics":15010,"families":15011,"methods":15012,"methodIds":15018,"rows":641,"failures":30},"kissicp2023-table-vi","Ablation of data-association threshold; all runs use the robust kernel",[469],[438],[441],[15013,15014,15015,15016,15017],"KISS-ICP with Ours (adaptive threshold)","KISS-ICP with fixed tau = 0.3 m","KISS-ICP with fixed tau = 0.5 m","KISS-ICP with fixed tau = 1.0 m","KISS-ICP with fixed tau = 2.0 m",[577],{"slug":15020,"sourceId":577,"sourceLabel":14964,"sourceYear":374,"table":15021,"note":15022,"datasets":15023,"metrics":15024,"families":15025,"methods":15026,"methodIds":15028,"rows":63,"failures":30},"kissicp2023-text-sec-iv-d2","Text Sec. IV-D2","KISS-ICP without robust kernel, averaged over KITTI sequences (stated in text)",[469],[438,23],[441,23],[15027],"KISS-ICP without robust kernel",[],{"slug":15030,"sourceId":15031,"sourceLabel":15032,"sourceYear":345,"table":325,"note":15033,"datasets":15034,"metrics":15035,"families":15036,"methods":15037,"methodIds":15041,"rows":2252,"failures":30},"deepvo2017-table-ii","deepvo2017","Wang et al., 2017","KITTI test sequences 03, 04, 05, 06, 07, 10; DeepVO trained on 00, 02, 08, 09; VISO2 M is monocular LIBVISO2 with fixed camera height, VISO2 S is ster…",[469],[438,439],[441],[15038,15039,15040],"DeepVO","VISO2 M","VISO2 S",[],{"slug":15043,"sourceId":15044,"sourceLabel":15045,"sourceYear":16,"table":91,"note":15046,"datasets":15047,"metrics":15048,"families":15049,"methods":15050,"methodIds":15053,"rows":608,"failures":30},"densesurfelmapping2019-table-i","densesurfelmapping2019","Wang et al., 2019","ICL-NUIM living room with simulated noise; reconstruction accuracy = mean difference between reconstructed model and ground-truth model; ORB-SLAM2 RGB…",[2870],[75],[78],[5252,5233,5234,15051,81,15052],"InfiniTAM [13] (Kaehler et al. ECCV 2016)","Ours w\u002Fo loop (ablation: ORB-SLAM2 loop closure disabled)",[2897,15044,2915,5224],{"slug":15055,"sourceId":15044,"sourceLabel":15045,"sourceYear":16,"table":9112,"note":15056,"datasets":15057,"metrics":15058,"families":15059,"methods":15060,"methodIds":15062,"rows":63,"failures":63},"densesurfelmapping2019-text-sec-vi-b","KITTI odometry 00 reconstructed from PSMNet stereo depth with ORB-SLAM2 stereo tracking; per-frame fusion time including superpixel extraction and sur…",[469],[38],[40],[81,15061],"Ours (surfel fusion step)",[15044],{"slug":15064,"sourceId":8724,"sourceLabel":15065,"sourceYear":213,"table":325,"note":15066,"datasets":15067,"metrics":15068,"families":15069,"methods":15070,"methodIds":15076,"rows":2551,"failures":30},"iscloam2020-table-ii","Wang et al., 2020","KITTI sequences 00, 02 (forward and reverse revisits), 05; loop-closure precision and recall (%); Scan Context, GLAROT3D and Cieslewski results copied…",[469],[23],[23],[15071,15072,15073,15074,15075],"Cieslewski [24]","GLAROT3D [17]","Galvez-Lopez [10] (DBoW2)","Kim [21] (Scan Context)","Proposed (ISC)",[8724,6940],{"slug":15078,"sourceId":8724,"sourceLabel":15065,"sourceYear":213,"table":1278,"note":15079,"datasets":15080,"metrics":15081,"families":15082,"methods":15083,"methodIds":15086,"rows":63,"failures":30},"iscloam2020-text-sec-iv-c","Retrieval time per query stated in text; binary geometry matching time stated in Sec. III-C",[469,2070],[38],[40],[15084,15085],"Proposed (ISC), binary geometry stage","Proposed (ISC), full two-stage query",[8724],{"slug":15088,"sourceId":393,"sourceLabel":15089,"sourceYear":698,"table":91,"note":15090,"datasets":15091,"metrics":15093,"families":15094,"methods":15095,"methodIds":15098,"rows":120,"failures":30},"floam2021-table-i","Wang et al., 2021a","ablation in the warehouse environment: F-LOAM without distortion compensation, with LOAM's iterative compensation added, and with the proposed two-sta…",[15092],"authors' warehouse AGV data",[23,38],[40,23],[4048,15096,15097],"LOAM (iterative distortion compensation inside F-LOAM)","No Compensation",[393],{"slug":15100,"sourceId":393,"sourceLabel":15089,"sourceYear":698,"table":1928,"note":15101,"datasets":15102,"metrics":15103,"families":15104,"methods":15105,"methodIds":15106,"rows":274,"failures":154},"floam2021-text-sec-iv-b","KITTI odometry sequences 00-10 (23,201 frames, 22 km); averages over 11 sequences with the KITTI metric definitions (Eq. 14)",[4494],[438,148,23],[40,441,23],[4048],[393],{"slug":15108,"sourceId":393,"sourceLabel":15089,"sourceYear":698,"table":15109,"note":15110,"datasets":15111,"metrics":15113,"families":15114,"methods":15115,"methodIds":15116,"rows":154,"failures":30},"floam2021-text-sec-iv-c3","Text Sec. IV-C3","indoor room with VICON motion capture; robot remotely controlled",[15112],"authors' VICON room test",[22],[25],[4048],[393],{"slug":15118,"sourceId":15119,"sourceLabel":15120,"sourceYear":698,"table":325,"note":15121,"datasets":15122,"metrics":15124,"families":15125,"methods":15126,"methodIds":15128,"rows":356,"failures":30},"sslslam2021-table-ii","sslslam2021","Wang et al., 2021b","Rotation test: L515 rotated randomly from horizontal at up to 1.57 rad\u002Fs and returned; tracking loss when final angle deviation exceeds 10 deg",[15123],"own rotation test",[23,1933],[23,1935],[4330,15127],"The proposed method",[392,15119],{"slug":15130,"sourceId":15119,"sourceLabel":15120,"sourceYear":698,"table":1928,"note":15131,"datasets":15132,"metrics":15134,"families":15135,"methods":15136,"methodIds":15139,"rows":274,"failures":154},"sslslam2021-text-sec-iv-b","Manually driven robot in a 4 m x 4 m VICON room; LOAM configured with the L515 angles and unchanged feature numbers",[15133],"own VICON room data",[23,38],[40,23],[15137,15138],"LOAM [19] (L515 settings)","proposed method (SSL_SLAM)",[2118,15119],{"slug":15141,"sourceId":15119,"sourceLabel":15120,"sourceYear":698,"table":1278,"note":15142,"datasets":15143,"metrics":15145,"families":15146,"methods":15147,"methodIds":15149,"rows":416,"failures":30},"sslslam2021-text-sec-iv-c","Warehouse AGV at up to 0.8 m\u002Fs; mapped machine size = average Euclidean distance between picked edge points; actual sizes: machine (b) 1.15 m x 1.85 m…",[15144],"own warehouse AGV data",[23,38],[40,23],[15138,15148],"proposed method (SSL_SLAM) map",[15119],{"slug":15151,"sourceId":15152,"sourceLabel":15153,"sourceYear":698,"table":69,"note":15154,"datasets":15155,"metrics":15156,"families":15157,"methods":15158,"methodIds":15169,"rows":15170,"failures":30},"pwclonet2021-table-1","pwclonet2021","Wang et al., 2021c","KITTI odometry, trained on 00-06 (marked *) and tested on 07-10; trel = average translational RMSE (%) over 100-800 m subsequences; rows other than LO…",[469],[438,439],[441],[15159,15160,15161,15162,15163,15164,15165,15166,15167,15168],"CLS [21]","DMLO [11]","Full LOAM [31]","GICP [19]","ICP-po2pl","ICP-po2po","LO-Net [10]","LOAM w\u002Fo mapping (published code run by authors)","Ours (PWCLO-Net)","Velas et al. [22]",[478,1944,2118,450,3309,15152,3292],122,{"slug":15172,"sourceId":15152,"sourceLabel":15153,"sourceYear":698,"table":108,"note":15173,"datasets":15174,"metrics":15175,"families":15176,"methods":15177,"methodIds":15179,"rows":618,"failures":30},"pwclonet2021-table-2","KITTI, trained on 00-06 and 09-10, tested on 07-08 to match LodoNet [32]",[469],[438,439],[441],[15178,15167],"LodoNet [32]",[15152],{"slug":15181,"sourceId":15152,"sourceLabel":15153,"sourceYear":698,"table":17,"note":15182,"datasets":15183,"metrics":15184,"families":15185,"methods":15186,"methodIds":15188,"rows":618,"failures":30},"pwclonet2021-table-3","KITTI, trained on 00-03 and 05-09, tested on 04 and 10 to match DeepPCO [27]",[469],[438,439],[441],[15187,15167],"DeepPCO [27]",[15152],{"slug":15190,"sourceId":15152,"sourceLabel":15153,"sourceYear":698,"table":33,"note":15191,"datasets":15192,"metrics":15193,"families":15194,"methods":15195,"methodIds":15197,"rows":618,"failures":30},"pwclonet2021-table-4","KITTI, trained on 00-08, tested on 09 and 10 to match the unsupervised method of Cho et al. [3]",[469],[438,439],[441],[15196,15167],"Cho et al. [3] (unsupervised)",[15152],{"slug":15199,"sourceId":3181,"sourceLabel":15200,"sourceYear":374,"table":2425,"note":15201,"datasets":15202,"metrics":15204,"families":15205,"methods":15206,"methodIds":15208,"rows":224,"failures":30},"coslam2023-supp-table-1","Wang et al., 2023a","Replica reconstruction evaluated with the NICE-SLAM frustum-only culling; Co-SLAM run with smooth weight 1e-3 and hash table size 14",[15203],"Replica (8 synthetic scenes)",[75,76,23],[78,23],[15207,3174,6376],"Co-SLAM (Ours)",[3181,6378,3184],{"slug":15210,"sourceId":3181,"sourceLabel":15200,"sourceYear":374,"table":2437,"note":15211,"datasets":15212,"metrics":15213,"families":15214,"methods":15215,"methodIds":15217,"rows":340,"failures":30},"coslam2023-supp-table-3","ScanNet ATE RMSE without trajectory alignment (raw trajectories in the same world frame), average of 5 runs",[2963],[568],[25],[15216,3174],"Co-SLAM (Ours, same values as Ours-dagger with alignment)",[3181,3184],{"slug":15219,"sourceId":3181,"sourceLabel":15200,"sourceYear":374,"table":69,"note":15220,"datasets":15221,"metrics":15223,"families":15224,"methods":15225,"methodIds":15227,"rows":396,"failures":30},"coslam2023-table-1","Averages over scenes; meshes culled with the authors' new culling strategy for all methods; TSDF-Fusion uses Co-SLAM poses; iMAP* is the NICE-SLAM re-…",[15203,15222],"Synthetic RGB-D of NeuralRGBD (7 scenes, simulated depth noise)",[148,75,76,23,38],[40,78,23],[15207,3174,15226,13734,6376],"TSDF-Fusion",[3181,2826,6378,3184],{"slug":15229,"sourceId":3181,"sourceLabel":15200,"sourceYear":374,"table":108,"note":15230,"datasets":15231,"metrics":15232,"families":15233,"methods":15234,"methodIds":15236,"rows":608,"failures":30},"coslam2023-table-2","Run-time as ms\u002Fiter x #iter; NICE-SLAM and iMAP* map every frame on TUM, otherwise mapping every 5 frames; Ours-dagger uses twice the tracking iterati…",[2963,2872],[148,23],[40,23],[15207,15235,3174,6376],"Co-SLAM (Ours-dagger)",[3181,6378,3184],{"slug":15238,"sourceId":3181,"sourceLabel":15200,"sourceYear":374,"table":17,"note":15239,"datasets":15240,"metrics":15241,"families":15242,"methods":15243,"methodIds":15244,"rows":429,"failures":30},"coslam2023-table-3","ATE RMSE averaged over 5 runs; ground truth from BundleFusion; rigid alignment of trajectory (Supp. Sec. 2.3)",[2963],[568],[25],[15207,15235,3174,6376],[3181,6378,3184],{"slug":15246,"sourceId":3181,"sourceLabel":15200,"sourceYear":374,"table":33,"note":15247,"datasets":15248,"metrics":15249,"families":15250,"methods":15251,"methodIds":15252,"rows":608,"failures":30},"coslam2023-table-4","ATE RMSE on TUM RGB-D; classic-method values as listed by the authors; ground truth from motion capture",[2872],[568],[25],[11926,15207,15235,2909,3174,6668,13734,6376],[9325,3181,6378,2448,3184,1511],{"slug":15254,"sourceId":15255,"sourceLabel":15256,"sourceYear":374,"table":279,"note":15257,"datasets":15258,"metrics":15259,"families":15260,"methods":15261,"methodIds":15265,"rows":2262,"failures":30},"dliom2023-table-iii","dliom2023","Wang et al., 2023b","NTU VIRAL absolute positioning errors; LIOM, LIO-SAM and Ours (H) use the horizontal LiDAR only, Ours (HV) uses horizontal and vertical LiDARs; w-avg…",[1636],[329],[25],[7503,15262,15263,15264],"LIOM [9]","Ours (H)","Ours (HV)",[15255,2117,338],{"slug":15267,"sourceId":15255,"sourceLabel":15256,"sourceYear":374,"table":731,"note":15268,"datasets":15269,"metrics":15271,"families":15272,"methods":15273,"methodIds":15275,"rows":1042,"failures":274},"dliom2023-table-iv","TONGJI self-collected handheld data; revisiting error = difference between each method's relative pose of two revisiting nodes and the relative pose f…",[15270],"TONGJI dataset",[23],[23],[15274,4760,334,4770,461],"Carto3D",[2116,15255,2117,338,2118],{"slug":15277,"sourceId":15255,"sourceLabel":15256,"sourceYear":374,"table":818,"note":15278,"datasets":15279,"metrics":15280,"families":15281,"methods":15282,"methodIds":15283,"rows":29,"failures":30},"dliom2023-table-v","Time cost per scan of the Odometry and Mapping modules for 16-line and 64-line LiDAR input; D-LIOM back-end time summed and averaged per frame",[2070],[38],[40],[4760,334,4770,461],[15255,2117,338,2118],{"slug":15285,"sourceId":15255,"sourceLabel":15256,"sourceYear":374,"table":827,"note":15286,"datasets":15287,"metrics":15288,"families":15289,"methods":15290,"methodIds":15293,"rows":224,"failures":30},"dliom2023-table-vi","Ablation on TONGJI: D-LIOM without gravity factor (WoG) and without submap-to-submap loop detection (WoL); revisiting error",[15270],[23],[23],[4760,15291,15292],"D-LIOM WoG (without gravity factor)","D-LIOM WoL (without loop detection)",[15255],{"slug":15295,"sourceId":15255,"sourceLabel":15256,"sourceYear":374,"table":838,"note":15296,"datasets":15297,"metrics":15298,"families":15299,"methods":15300,"methodIds":15302,"rows":63,"failures":30},"dliom2023-table-vii","All submaps of TJ-1 to TJ-4 matched for loop detection; precision and recall",[15270],[23],[23],[15301],"D-LIOM (S2SLD)",[15255],{"slug":15304,"sourceId":8482,"sourceLabel":15305,"sourceYear":2267,"table":15306,"note":15307,"datasets":15308,"metrics":15311,"families":15312,"methods":15313,"methodIds":15321,"rows":429,"failures":356},"dust3r2024-table-2-right","Wang et al., 2024","Table 2 (right)","Multi-view relative pose with 10 random frames per sequence (45 pairs); DUSt3R not trained on RealEstate10K",[15309,15310],"CO3Dv2","RealEstate10K",[23],[23],[15314,15315,15316,15317,15318,15319,15320],"Colmap+SPSG","DUSt3R 512 (w\u002F GA)","DUSt3R 512 (w\u002F PnP)","PixSfM","PosReg","PoseDiffusion (RealEstate10K result from CO3Dv2-trained model)","RelPose",[8482],{"slug":15323,"sourceId":8482,"sourceLabel":15305,"sourceYear":2267,"table":17,"note":15324,"datasets":15325,"metrics":15328,"families":15329,"methods":15330,"methodIds":15334,"rows":1810,"failures":30},"dust3r2024-table-3","Multi-view depth; rel is absolute relative error and tau the inlier ratio at 1.03; DUSt3R ScanNet value in parentheses (same-domain training via Habit…",[10401,6088,9543,1997,2963,15326,15327],"T&T","all Table 3 test sets",[23],[23],[15331,15332,15333],"COLMAP (classical, GT pose and intrinsics)","COLMAP Dense (classical, GT pose and intrinsics)","DUSt3R 512 (no GT pose, range or intrinsics; median alignment)",[8482],{"slug":15336,"sourceId":8482,"sourceLabel":15305,"sourceYear":2267,"table":33,"note":15337,"datasets":15338,"metrics":15339,"families":15340,"methods":15341,"methodIds":15355,"rows":1810,"failures":30},"dust3r2024-table-4","DTU MVS benchmark; baselines use GT cameras (and learning-based ones train on DTU); DUSt3R zero-shot without GT cameras, predictions aligned to the GT…",[6088],[2343,75,76],[78],[15342,15343,15344,15345,15346,15347,15348,15349,15350,15351,15352,15353,15354],"CER-MVS","CIDER","CVP-MVSNet","Camp","CasMVSNet","DUSt3R 512","Furu","GeoMVSNet","Gipuma","MVSNet","PatchmatchNet","Tola","UCS-Net",[8482],{"slug":15357,"sourceId":15358,"sourceLabel":15359,"sourceYear":562,"table":91,"note":15360,"datasets":15361,"metrics":15362,"families":15363,"methods":15364,"methodIds":15366,"rows":6820,"failures":120},"wang2025planarmesh-table-i","wang2025planarmesh","Wang et al., 2025a","Oxford Spires; each method meshes individual scans with ground-truth poses (every undistorted scan registered to the TLS map); meshes sampled to the r…",[72],[3417,74,75,76,23,38],[40,78,23],[80,5916,15365,14929],"PlanarMesh (Ours)",[3527,84,3265,15358],{"slug":15368,"sourceId":15358,"sourceLabel":15359,"sourceYear":562,"table":325,"note":15369,"datasets":15370,"metrics":15371,"families":15372,"methods":15373,"methodIds":15378,"rows":429,"failures":30},"wang2025planarmesh-table-ii","Ablation on Christ Church 03: number of scans for which seed planar-meshes are retained (0, 1, 10, All); All is the default setting; same metrics as T…",[72],[3417,74,75,76,23,38],[40,78,23],[15374,15375,15376,15377],"PlanarMesh, seed planar-meshes kept for 0 scans","PlanarMesh, seed planar-meshes kept for 1 scans","PlanarMesh, seed planar-meshes kept for 10 scans","PlanarMesh, seed planar-meshes kept for All scans",[15358],{"slug":15380,"sourceId":15358,"sourceLabel":15359,"sourceYear":562,"table":1221,"note":15381,"datasets":15382,"metrics":15383,"families":15384,"methods":15385,"methodIds":15387,"rows":63,"failures":30},"wang2025planarmesh-text-sec-iv-d","Per-scan processing time of PlanarMesh on Christ Church 03 (Fig. 7 stacked plot summarised in text); values stated in text, not read off the plot",[72],[38],[40],[15386],"PlanarMesh",[15358],{"slug":15389,"sourceId":15390,"sourceLabel":15391,"sourceYear":562,"table":69,"note":15392,"datasets":15393,"metrics":15396,"families":15397,"methods":15398,"methodIds":15409,"rows":721,"failures":154},"vggt2025-table-1","vggt2025","Wang et al., 2025b","Camera pose estimation with 10 random frames per scene, AUC@30 combining relative rotation and translation accuracy; no method trained on RealEstate10…",[15309,15394,15395],"RealEstate10K (unseen)","RealEstate10K and CO3Dv2 (single time column)",[23],[23],[15399,15314,15400,15401,15402,15403,15404,15405,15406,15317,15407,15408],"CUT3R","DUSt3R","FLARE","Fast3R","MASt3R","MV-DUSt3R","Ours (Feed-Forward)","Ours (with BA)","PoseDiff","VGGSfM v2",[8482,8462,15390],{"slug":15411,"sourceId":15390,"sourceLabel":15391,"sourceYear":562,"table":108,"note":15412,"datasets":15413,"metrics":15414,"families":15415,"methods":15416,"methodIds":15417,"rows":608,"failures":30},"vggt2025-table-2","Dense MVS estimation on DTU; method uses known GT cameras; units not stated in the paper",[6088],[2343,75,76],[78],[15343,15400,15349,15350,15403,15351,81,15352],[8482,8462,15390],{"slug":15419,"sourceId":15390,"sourceLabel":15391,"sourceYear":562,"table":17,"note":15420,"datasets":15421,"metrics":15422,"families":15423,"methods":15424,"methodIds":15427,"rows":29,"failures":30},"vggt2025-table-3","Point map estimation on ETH3D, 10 random frames per scene, predicted cloud aligned to GT with the Umeyama algorithm (similarity or rigid not stated),…",[9543],[2343,75,76,23],[78,23],[15400,15403,15425,15426],"Ours (Depth + Cam)","Ours (Point)",[8482,8462,15390],{"slug":15429,"sourceId":15390,"sourceLabel":15391,"sourceYear":562,"table":644,"note":15430,"datasets":15431,"metrics":15432,"families":15433,"methods":15434,"methodIds":15436,"rows":102,"failures":30},"vggt2025-table-9","Feature-backbone inference time and peak GPU memory versus number of input frames (arXiv v1 only; not in the CVF main paper)",[3980],[219,23],[40,23],[15435],"VGGT",[15390],{"slug":15438,"sourceId":15439,"sourceLabel":15440,"sourceYear":68,"table":279,"note":15441,"datasets":15442,"metrics":15444,"families":15445,"methods":15446,"methodIds":15449,"rows":721,"failures":30},"lemon2026-table-iii","lemon2026","Wang et al., 2026","Single-robot study on self-collected Mid360 data; spatial BA versus BALM2 (sliding window) and HBA, all on raw odometry without loop-based refinement;…",[15443],"self-collected (Mid360)",[23],[23],[15447,14107,15448],"BALM2","Ours (spatial BA)",[9393,9453,15439],{"slug":15451,"sourceId":15439,"sourceLabel":15440,"sourceYear":68,"table":731,"note":15452,"datasets":15453,"metrics":15455,"families":15456,"methods":15457,"methodIds":15460,"rows":598,"failures":578},"lemon2026-table-iv","Multi-robot localization; RMSE of ATE (m); failure (x) = any sequence with RMSE above 30 m; GEODE, MARS-LVIG and S3E sequences split into sessions wit…",[2374,5857,15454],"S3E",[568],[25],[15458,15459,81],"DCL-SLAM","LAMM",[15461,15439],"lamm2025",{"slug":15463,"sourceId":15439,"sourceLabel":15440,"sourceYear":68,"table":803,"note":15464,"datasets":15465,"metrics":15466,"families":15467,"methods":15468,"methodIds":15472,"rows":1315,"failures":30},"lemon2026-table-ix","Ablation of the map merging module: first PGO only, first PGO plus spatial BA, and full LEMON-Mapping; RMSE of ATE (m)",[2374,5857,15454],[568],[25],[15469,15470,15471],"FPGO","FPGO + BA","LEMON Full",[15439],{"slug":15474,"sourceId":15439,"sourceLabel":15440,"sourceYear":68,"table":818,"note":15475,"datasets":15476,"metrics":15477,"families":15478,"methods":15479,"methodIds":15480,"rows":608,"failures":30},"lemon2026-table-v","Mapping quality against the MARS-LVIG ground-truth map (DJI L1 LiDAR processed with DJI Terra); AWD average Wasserstein distance, CD Chamfer distance,…",[5857],[2343,75,23],[78,23],[15459,81],[15461,15439],{"slug":15482,"sourceId":15439,"sourceLabel":15440,"sourceYear":68,"table":827,"note":15483,"datasets":15484,"metrics":15485,"families":15486,"methods":15487,"methodIds":15488,"rows":120,"failures":30},"lemon2026-table-vi","Average plane thickness and planarity over all S3E sequences (no ground-truth map; MapEval definitions)",[15454],[23,1830],[78,23],[15458,15459,81],[15461,15439],{"slug":15490,"sourceId":15439,"sourceLabel":15440,"sourceYear":68,"table":838,"note":15491,"datasets":15492,"metrics":15493,"families":15494,"methods":15495,"methodIds":15498,"rows":618,"failures":30},"lemon2026-table-vii","Loop counts after rejection and recall, and RMSE of ATE (m) of the first PGO without and with loop recall (LR) on S3E",[15454],[568],[25],[15496,15497],"FPGO with loop recall","FPGO without loop recall",[],{"slug":15500,"sourceId":15439,"sourceLabel":15440,"sourceYear":68,"table":852,"note":15501,"datasets":15502,"metrics":15503,"families":15504,"methods":15505,"methodIds":15509,"rows":224,"failures":30},"lemon2026-table-viii","Loop closure outlier rejection on RING++ candidates; correctness judged against the framework's optimized trajectory (pose distance below 5 m); PCM an…",[15454],[23],[23],[15506,15507,15508],"GNC Best-F1","Ours (loop processing module)","PCM Best-F1",[15439,14330],{"slug":15511,"sourceId":15439,"sourceLabel":15440,"sourceYear":68,"table":7552,"note":15512,"datasets":15513,"metrics":15514,"families":15515,"methods":15516,"methodIds":15518,"rows":274,"failures":30},"lemon2026-table-x","Scalability on R3LIVE subdivided into 5, 10 and 20 sessions; success = each session correctly aligned with all adjacent overlapping sessions",[2381],[1933],[1935],[15517],"LEMON-Mapping",[15439],{"slug":15520,"sourceId":15521,"sourceLabel":15522,"sourceYear":562,"table":244,"note":15523,"datasets":15524,"metrics":15526,"families":15527,"methods":15528,"methodIds":15533,"rows":2882,"failures":356},"wei2025fusionportablev2-table-5","wei2025fusionportablev2","Wei et al., 2025a","Translation ATE [m] with EVO, 'mean ATE' per Sec. 7.1.2; reference is 3-DoF MS60 (handheld, legged) or 6-DoF INS (UGV, vehicle) per Table 4; x = undef…",[15525],"FusionPortableV2",[22],[25],[6667,15529,1437,15530,2381,15531,6346,15532],"DROID-SLAM (left frame camera, pre-trained, no fine-tuning)","FAST-LIO2 (IMU+LiDAR)","R3LIVE (IMU+LiDAR+left frame camera)","VINS-Fusion (LC) (IMU+stereo, loop closure)",[6178,321,2387,1513],{"slug":15535,"sourceId":15521,"sourceLabel":15522,"sourceYear":562,"table":1072,"note":15536,"datasets":15537,"metrics":15538,"families":15539,"methods":15540,"methodIds":15543,"rows":721,"failures":30},"wei2025fusionportablev2-table-6","Maps downsampled to 0.1 m, initially aligned in CloudCompare, then registered to the Leica GT map; inlier threshold tau = 0.2 m; RE = RMSE of truncate…",[15525],[2343,75,76],[78],[15541,15542],"FAST-LIO2 (FL2)","R3LIVE (R3L)",[321,2387],{"slug":15545,"sourceId":15521,"sourceLabel":15522,"sourceYear":562,"table":15546,"note":15547,"datasets":15548,"metrics":15549,"families":15550,"methods":15551,"methodIds":15553,"rows":154,"failures":30},"wei2025fusionportablev2-text-fig-5-caption-vor","Text Fig. 5 caption (VoR)","Wheel-encoder intrinsic calibration check: encoder-integrated trajectory after calibration aligned to 3DM-GQ7 INS trajectory",[15525],[329],[25],[15552],"wheel encoder odometry after motion-based calibration",[],{"slug":15555,"sourceId":15521,"sourceLabel":15522,"sourceYear":562,"table":15556,"note":15557,"datasets":15558,"metrics":15559,"families":15560,"methods":15561,"methodIds":15563,"rows":63,"failures":154},"wei2025fusionportablev2-text-sec-6-2-3","Text Sec. 6.2.3","Quality of the Leica GT map per Leica Cyclone registration report (pairwise scan errors); reference data quality, not a SLAM result",[15525],[75],[78],[15562],"Leica Cyclone registration of RTC360 and BLK360 scans (GT map)",[],{"slug":15565,"sourceId":15521,"sourceLabel":15522,"sourceYear":562,"table":8632,"note":15566,"datasets":15567,"metrics":15568,"families":15569,"methods":15570,"methodIds":15571,"rows":63,"failures":154},"wei2025fusionportablev2-text-sec-7-1-1","DROID-SLAM runtime in the localization benchmark, 320 x 240 images, global BA layer enabled, pre-trained TartanAir model",[15525],[148,219],[40],[6667],[6178],{"slug":15573,"sourceId":15461,"sourceLabel":15574,"sourceYear":562,"table":325,"note":15575,"datasets":15576,"metrics":15577,"families":15578,"methods":15579,"methodIds":15588,"rows":2607,"failures":120},"lamm2025-table-ii","Wei et al., 2025b","KITTI sequences split into overlapping sessions; FAST-LIO2 initial odometry; FAST-LIO2 column is the single-run result; 'Fail' = merging failed; ablat…",[1997],[568],[25],[15580,15458,15581,15582,15583,15584,15585,15586,15587],"BTC (loop-detection-only merging)","Disco-SLAM","FAST-LIO2 (single run)","LAMM full model","LAMM wo M-detector","LAMM wo loop filter","LAMM-PCM","LAMM-SOLiD",[321,15461],{"slug":15590,"sourceId":15461,"sourceLabel":15574,"sourceYear":562,"table":731,"note":15591,"datasets":15592,"metrics":15593,"families":15594,"methods":15595,"methodIds":15600,"rows":1042,"failures":30},"lamm2025-table-iv","Dynamic point removal on 141 manually selected consecutive frames of HeLiPR Town 01 (many pedestrians and vehicles); SA static accuracy, DA dynamic ac…",[5072],[23],[23],[15596,15597,15598,15599,8920],"BeautyMap","Bi-M-Detector (LAMM)","ERASOR","M-Detector",[3526,15461,3528],{"slug":15602,"sourceId":15461,"sourceLabel":15574,"sourceYear":562,"table":818,"note":15603,"datasets":15604,"metrics":15605,"families":15606,"methods":15607,"methodIds":15611,"rows":102,"failures":30},"lamm2025-table-v","HeLiPR Town and Roundabout: FAST-LIO2 ATE of each input sequence, ATE after merging the three sequences of the same LiDAR type, and after merging all…",[5072],[568],[25],[1437,15608,15609,15610],"LAMM merged (Avia sequences)","LAMM merged (Ouster sequences)","LAMM merged multi-LiDAR",[321,15461],{"slug":15613,"sourceId":2915,"sourceLabel":15614,"sourceYear":681,"table":91,"note":15615,"datasets":15616,"metrics":15617,"families":15618,"methods":15619,"methodIds":15621,"rows":608,"failures":30},"elasticfusion2015-table-i","Whelan et al., 2015a","TUM RGB-D real-world sequences; ATE RMSE between all estimated poses and ground truth associated by timestamp; Frame-to-model is ElasticFusion with al…",[2872],[568],[25],[2907,5233,15620,2909,2910,2911],"ElasticFusion ablation: Frame-to-model (deformations disabled)",[2915,2448],{"slug":15623,"sourceId":2915,"sourceLabel":15614,"sourceYear":681,"table":325,"note":15624,"datasets":15625,"metrics":15626,"families":15627,"methods":15628,"methodIds":15629,"rows":608,"failures":30},"elasticfusion2015-table-ii","ICL-NUIM living room with synthetic noise; ATE RMSE; on kt1 the camera never revisits mapped areas so Frame-to-model equals ElasticFusion; only kt3 tr…",[2870],[568],[25],[2907,5233,15620,2909,2910,2911],[2915,2448],{"slug":15631,"sourceId":2915,"sourceLabel":15614,"sourceYear":681,"table":279,"note":15632,"datasets":15633,"metrics":15634,"families":15635,"methods":15636,"methodIds":15637,"rows":608,"failures":30},"elasticfusion2015-table-iii","ICL-NUIM living room surface reconstruction accuracy: mean distance from each point to the nearest surface of the ground-truth 3D model",[2870],[75],[78],[2907,5233,15620,2909,2910,2911],[2915,2448],{"slug":15639,"sourceId":2915,"sourceLabel":15614,"sourceYear":681,"table":15640,"note":15641,"datasets":15642,"metrics":15643,"families":15644,"methods":15645,"methodIds":15648,"rows":63,"failures":30},"elasticfusion2015-text-sec-vii-a","Text Sec. VII-A","Loop-closure ablation stated in text: only local loops or only global loops enabled",[2872],[568],[25],[15646,15647],"ElasticFusion ablation: global loops only","ElasticFusion ablation: local loops only",[2915],{"slug":15650,"sourceId":2915,"sourceLabel":15614,"sourceYear":681,"table":15651,"note":15641,"datasets":15652,"metrics":15653,"families":15654,"methods":15655,"methodIds":15656,"rows":356,"failures":30},"elasticfusion2015-text-sec-vii-b","Text Sec. VII-B",[2870],[568,75],[25,78],[15646,15647],[2915],{"slug":15658,"sourceId":2915,"sourceLabel":15614,"sourceYear":681,"table":15659,"note":15660,"datasets":15661,"metrics":15663,"families":15664,"methods":15665,"methodIds":15666,"rows":274,"failures":30},"elasticfusion2015-text-sec-vii-c","Text Sec. VII-C","Average frame processing time over the Hotel sequence (7725 frames, 4.1 million surfels); time grows with surfel count",[15662],"ElasticFusion qualitative datasets",[148,38],[40],[5233],[2915],{"slug":15668,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":69,"note":15670,"datasets":15671,"metrics":15672,"families":15673,"methods":15674,"methodIds":15675,"rows":598,"failures":30},"kintinuous2015-table-1","Whelan et al., 2015b","TUM RGB-D ATE statistics of Kintinuous (m), mean over ten runs of each dataset; mean angular velocity (deg\u002Fs) of each sequence given in the table",[2872],[329,568],[25],[2909],[2448],{"slug":15677,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":563,"note":15678,"datasets":15679,"metrics":15680,"families":15681,"methods":15682,"methodIds":15684,"rows":416,"failures":30},"kintinuous2015-table-10","Backend latency at the moment of loop closure, every-frame pose graph: total of frontend recognition, iSAM optimisation and map deformation (ms); sub-…",[11820],[23],[23],[15683],"Kintinuous (every-frame pose graph)",[2448],{"slug":15686,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":3189,"note":15687,"datasets":15688,"metrics":15689,"families":15690,"methods":15691,"methodIds":15693,"rows":416,"failures":30},"kintinuous2015-table-11","Backend latency at the moment of loop closure, subsampled pose graph: total of frontend recognition, iSAM optimisation and map deformation (ms); sub-t…",[11820],[23],[23],[15692],"Kintinuous (subsampled pose graph)",[2448],{"slug":15695,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":108,"note":15696,"datasets":15697,"metrics":15698,"families":15699,"methods":15700,"methodIds":15703,"rows":2262,"failures":154},"kintinuous2015-table-2","TUM RGB-D ATE RMSE (m); Kintinuous value is the best estimate over ten runs; other systems as compared by the authors; MRS produced no estimate on fr3…",[2872],[568],[25],[2907,15701,15702,2911],"Kintinuous (Ours)","MRS (multi-resolution surfel maps)",[2448],{"slug":15705,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":17,"note":15706,"datasets":15707,"metrics":15708,"families":15709,"methods":15710,"methodIds":15712,"rows":618,"failures":30},"kintinuous2015-table-3","Comparison with the unified keyframe and voxel dense visual SLAM of Meilland and Comport (2013) using their metric; fr2\u002Flarge_no_loop values of Kintin…",[2872],[329],[25],[15701,15711],"Unified (Meilland and Comport 2013)",[2448],{"slug":15714,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":33,"note":15706,"datasets":15715,"metrics":15716,"families":15717,"methods":15718,"methodIds":15719,"rows":618,"failures":30},"kintinuous2015-table-4",[2872],[329],[25],[15701,15711],[2448],{"slug":15721,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":244,"note":15722,"datasets":15723,"metrics":15724,"families":15725,"methods":15726,"methodIds":15727,"rows":340,"failures":30},"kintinuous2015-table-5","Seven hand-held datasets: RMS residual of point-to-plane ICP between the deformation-corrected map and a 2-pass map rebuilt from the optimised pose gr…",[11820],[23],[23],[15683,15692],[2448],{"slug":15729,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":1072,"note":15730,"datasets":15731,"metrics":15732,"families":15733,"methods":15734,"methodIds":15735,"rows":654,"failures":654},"kintinuous2015-table-6","Open-source DVO SLAM with default parameters on the authors' seven datasets: post-processing time for final pose-graph optimisation and keyframe loop…",[11820],[23],[23],[2907],[],{"slug":15737,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":621,"note":15738,"datasets":15739,"metrics":15740,"families":15741,"methods":15742,"methodIds":15750,"rows":654,"failures":30},"kintinuous2015-table-7","Frontend (volumetric fusion thread) average frame processing time on fr1\u002Fdesk for different volume shifting thresholds ms (voxels); 16 voxels chosen a…",[2872],[38],[40],[15743,15744,15745,15746,15747,15748,15749],"Kintinuous (ms = 1)","Kintinuous (ms = 16)","Kintinuous (ms = 2)","Kintinuous (ms = 32)","Kintinuous (ms = 4)","Kintinuous (ms = 64)","Kintinuous (ms = 8)",[2448],{"slug":15752,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":633,"note":15678,"datasets":15753,"metrics":15754,"families":15755,"methods":15756,"methodIds":15757,"rows":654,"failures":30},"kintinuous2015-table-8",[11820],[23],[23],[15683],[2448],{"slug":15759,"sourceId":2448,"sourceLabel":15669,"sourceYear":681,"table":644,"note":15687,"datasets":15760,"metrics":15761,"families":15762,"methods":15763,"methodIds":15764,"rows":654,"failures":30},"kintinuous2015-table-9",[11820],[23],[23],[15692],[2448],{"slug":15766,"sourceId":798,"sourceLabel":15767,"sourceYear":374,"table":325,"note":15768,"datasets":15769,"metrics":15771,"families":15772,"methods":15773,"methodIds":15779,"rows":2119,"failures":30},"vilens2023-table-ii","Wisth et al., 2023","Mean 10 m RPE with std in parentheses; VILENS variants run at 15 Hz unless noted; ground truth Leica TS16 tracking",[15770],"authors' ANYmal datasets",[2411,330],[332],[15774,15775,15776,15777,15778],"CompSLAM (loosely coupled, output 5 Hz)","VILENS (full)","VILENS-IR (ablation: ICP and IMU only, output 2 Hz)","VILENS-LVI (ablation: lidar and visual features with IMU)","VILENS-LVIK (ablation: adds leg kinematics)",[798],{"slug":15781,"sourceId":798,"sourceLabel":15767,"sourceYear":374,"table":279,"note":15782,"datasets":15783,"metrics":15784,"families":15785,"methods":15786,"methodIds":15788,"rows":608,"failures":30},"vilens2023-table-iii","Ablation of online velocity bias estimation; mean 10 m RPE with std in parentheses (LSM rotation row duplicates SUB and differs from Table II)",[15770],[2411,330],[332],[15775,15787],"VILENS-NO-BIAS (ablation: no online velocity bias)",[798],{"slug":15790,"sourceId":798,"sourceLabel":15767,"sourceYear":374,"table":731,"note":15791,"datasets":15792,"metrics":15793,"families":15794,"methods":15795,"methodIds":15802,"rows":120,"failures":30},"vilens2023-table-iv","Timing of VILENS modules, mean with std; module frequency given in the table",[15770],[38],[40],[15796,15797,15798,15799,15800,15801],"VILENS module: IMU","VILENS module: Leg kinematics","VILENS module: Lidar ICP","VILENS module: Lidar point cloud features","VILENS module: Optimization","VILENS module: Visual features with lidar depth",[798],{"slug":15804,"sourceId":798,"sourceLabel":15767,"sourceYear":374,"table":818,"note":15805,"datasets":15806,"metrics":15807,"families":15808,"methods":15809,"methodIds":15813,"rows":274,"failures":30},"vilens2023-table-v","Frequency and mean latency of VILENS outputs, latency relative to the IMU input",[15770],[23],[23],[15810,15811,15812],"VILENS output: Factor graph optimized","VILENS output: ICP optimized","VILENS output: IMU forward-propagated",[798],{"slug":15815,"sourceId":15816,"sourceLabel":15817,"sourceYear":2267,"table":91,"note":15818,"datasets":15819,"metrics":15820,"families":15821,"methods":15822,"methodIds":15824,"rows":1991,"failures":618},"lioekf2024-table-i","lioekf2024","Wu et al., 2024a","Default parameters for FAST-LIO2 and LIO-SAM, LIO-SAM loop closure disabled; one LIO-EKF configuration for all data; KITTI relative errors and ATE; LI…",[6122,1378,6441],[329,438,23],[25,441,23],[1437,15823,334],"LIO-EKF",[321,15816,338],{"slug":15826,"sourceId":15816,"sourceLabel":15817,"sourceYear":2267,"table":325,"note":15827,"datasets":15828,"metrics":15829,"families":15830,"methods":15831,"methodIds":15832,"rows":52,"failures":154},"lioekf2024-table-ii","Average processing time of the scan correction step, averaged over sequences of each dataset",[6122,1378,6441],[38],[40],[1437,15823,334],[321,15816,338],{"slug":15834,"sourceId":15816,"sourceLabel":15817,"sourceYear":2267,"table":279,"note":15835,"datasets":15836,"metrics":15837,"families":15838,"methods":15839,"methodIds":15843,"rows":102,"failures":30},"lioekf2024-table-iii","Ablation: LIO-EKF update with 1 (EKF), 10 or 100 iterations (IEKF); KITTI relative errors and processing time",[6122,1378],[438,23,38],[40,441,23],[15840,15841,15842],"LIO-EKF (1 iteration)","LIO-EKF (10 iterations)","LIO-EKF (100 iterations)",[15816],{"slug":15845,"sourceId":15816,"sourceLabel":15817,"sourceYear":2267,"table":731,"note":15846,"datasets":15847,"metrics":15848,"families":15849,"methods":15850,"methodIds":15855,"rows":2882,"failures":30},"lioekf2024-table-iv","Ablation: fixed correspondence thresholds (0.3 m, 1 m), KISS-ICP adaptive threshold, and the proposed threshold; KITTI relative errors",[6122,6441],[438,23],[441,23],[15851,15852,15853,15854],"LIO-EKF with KISS-ICP adaptive threshold","LIO-EKF with fixed threshold 0.3 m","LIO-EKF with fixed threshold 1 m","LIO-EKF with proposed adaptive threshold",[15816],{"slug":15857,"sourceId":15858,"sourceLabel":15859,"sourceYear":2267,"table":91,"note":15860,"datasets":15861,"metrics":15862,"families":15863,"methods":15864,"methodIds":15866,"rows":301,"failures":416},"voxelmappp2024-table-i","voxelmappp2024","Wu et al., 2024b","M2DGR structured urban sequences; ATE (m); A-LOAM, LeGO-LOAM, LIO-SAM and LINS copied from the M2DGR paper; FAST-LIO2, Faster-LIO, VoxelMap and VoxelM…",[6122],[329],[25],[4330,1437,317,4769,334,2197,8319,15865],"VoxelMap++",[392,304,321,395,4783,338,15858,7515],{"slug":15868,"sourceId":15858,"sourceLabel":15859,"sourceYear":2267,"table":325,"note":15869,"datasets":15870,"metrics":15872,"families":15873,"methods":15874,"methodIds":15876,"rows":3819,"failures":30},"voxelmappp2024-table-ii","Own Livox HAP data pushed on a cart around closed loops; end-to-end error between start and terminal point (no RTK available)",[15871],"VoxelMap++ own datasets",[1551],[1553],[1437,317,15875,8319,15865],"LIO-Livox",[304,321,15858,7515],{"slug":15878,"sourceId":15858,"sourceLabel":15859,"sourceYear":2267,"table":279,"note":15879,"datasets":15880,"metrics":15881,"families":15882,"methods":15883,"methodIds":15884,"rows":3819,"failures":30},"voxelmappp2024-table-iii","Own Livox HAP data pushed on a cart around closed loops; end-to-end error between start and terminal point",[15871],[1551],[1553],[1437,317,15875,8319,15865],[304,321,15858,7515],{"slug":15886,"sourceId":15858,"sourceLabel":15859,"sourceYear":2267,"table":731,"note":15887,"datasets":15888,"metrics":15889,"families":15890,"methods":15891,"methodIds":15892,"rows":29,"failures":30},"voxelmappp2024-table-iv","Resource usage: average computation time per scan and memory usage",[15871],[219,38],[40],[1437,317,8319,15865],[304,321,15858,7515],{"slug":15894,"sourceId":15895,"sourceLabel":15896,"sourceYear":562,"table":325,"note":15897,"datasets":15898,"metrics":15900,"families":15901,"methods":15902,"methodIds":15905,"rows":9098,"failures":608},"livgs2025-table-ii","livgs2025","Xiao et al., 2025","Tracking accuracy with rpg trajectory evaluation: t_rel = average translational RMSE drift (%), r_rel = average rotational RMSE drift (deg\u002F100 m), t_a…",[15899],"NTU4DRadLM",[568,2411,330],[25,332],[3170,3171,15903,5866,15904,892,81,3177],"HDL-graph-SLAM","NeRF-LOAM",[394,15895,3183,3248,763,3186],{"slug":15907,"sourceId":15895,"sourceLabel":15896,"sourceYear":562,"table":731,"note":15908,"datasets":15909,"metrics":15910,"families":15911,"methods":15912,"methodIds":15913,"rows":608,"failures":120},"livgs2025-table-iv","Closed-loop test on a segment of R3LIVE hku_park_00 (handheld, Livox Avia 10 Hz, 1280 x 1024 images at 30 Hz); same metrics as Table II; LiV-GS has no…",[8050],[568,2411,330],[25,332],[3170,3171,15903,5866,15904,892,81,3177],[394,15895,3183,3248,763,3186],{"slug":15915,"sourceId":15895,"sourceLabel":15896,"sourceYear":562,"table":5451,"note":15916,"datasets":15917,"metrics":15919,"families":15920,"methods":15921,"methodIds":15922,"rows":356,"failures":30},"livgs2025-text-sec-iv-e","System throughput (processed frames divided by total time) and mean per-operation module runtimes; modules run asynchronously",[15918],"not stated (Fig. 7 relates runtime to the NTU4DRadLM accuracy and rendering results, but the runtime data are not attributed to a dataset)",[148,23,38],[40,23],[81],[15895],{"slug":15924,"sourceId":15925,"sourceLabel":15926,"sourceYear":562,"table":15927,"note":15928,"datasets":15929,"metrics":15930,"families":15931,"methods":15932,"methodIds":15934,"rows":618,"failures":30},"gslivm2025-iccv-supp-table-4","gslivm2025","Xie et al., 2025","ICCV Supp. Table 4","Rendering comparison with LiV-GS on sequences cp and nyl2; LiV-GS values copied from its preprint (marked *), GS-LIVM values from the authors' runs; d…",[8984],[23],[23],[15933,81],"LiV-GS* (preprint result)",[15925,15895],{"slug":15936,"sourceId":15925,"sourceLabel":15926,"sourceYear":562,"table":15937,"note":15938,"datasets":15939,"metrics":15941,"families":15942,"methods":15943,"methodIds":15944,"rows":618,"failures":30},"gslivm2025-iccv-supp-table-5","ICCV Supp. Table 5","GPU usage under the same optimization time; unit written 'Mb' (megabytes or megabits not specified); added by second checker from the ICCV supplement",[15940,8050],"FAST-LIVO dataset (row-to-dataset mapping inferred from Table 1 caption order)",[219],[40],[6110,5866,8107,81],[15925,5824,3183],{"slug":15946,"sourceId":15925,"sourceLabel":15926,"sourceYear":562,"table":15947,"note":15948,"datasets":15949,"metrics":15950,"families":15951,"methods":15952,"methodIds":15954,"rows":416,"failures":154},"gslivm2025-iccv-supp-table-6","ICCV Supp. Table 6","Mapping FPS; sequence not stated; Gaussian-LIC value copied from its preprint (marked *), which the GS-LIVM text says ran on an RTX 3090; added by sec…",[8984],[148],[40],[6110,15953,5866,8107,81],"Gaussian-LIC* (preprint result)",[8082,15925,5824,3183],{"slug":15956,"sourceId":15925,"sourceLabel":15926,"sourceYear":562,"table":2451,"note":15957,"datasets":15958,"metrics":15960,"families":15961,"methods":15962,"methodIds":15964,"rows":224,"failures":30},"gslivm2025-supp-table-4","Comparison with fully optimized offline 3DGS (no iteration limit) on training views; arXiv v1 supplementary",[15959,8050],"Botanic Garden",[23],[23],[15963,81],"3DGS [13] (fully optimized)",[15925,5824],{"slug":15966,"sourceId":15925,"sourceLabel":15926,"sourceYear":562,"table":6381,"note":15967,"datasets":15968,"metrics":15970,"families":15971,"methods":15972,"methodIds":15976,"rows":1042,"failures":30},"gslivm2025-supp-table-5","PSNR on additional sequences and LiDAR types; the supplement does not restate whether the Table 1 protocol (at most 100 frames and 5 min, COLMAP input…",[15959,15969,8984,9485],"FAST-LIVO or R3LIVE dataset (not stated)",[23],[23],[15973,15974,15975,81],"3DGS [13]","MonoGS [21]","Nerf-SLAM [27]",[15925,5824,3183],{"slug":15978,"sourceId":15925,"sourceLabel":15926,"sourceYear":562,"table":11902,"note":15979,"datasets":15980,"metrics":15981,"families":15982,"methods":15983,"methodIds":15988,"rows":301,"failures":30},"gslivm2025-supp-table-7","Tracking accuracy on Botanic Garden using Velodyne VLP-16 data; RPE and ATE over full transformations; units, statistic and alignment not stated; 'Our…",[15959],[329,23],[25,23],[15984,15985,15986,15987],"FAST-LIO2 [43]","LVI-SAM [33]","OURS","R3LIVE [18]",[321,15925,2386,2387],{"slug":15990,"sourceId":15925,"sourceLabel":15926,"sourceYear":562,"table":9540,"note":15991,"datasets":15992,"metrics":15994,"families":15995,"methods":15996,"methodIds":15997,"rows":618,"failures":30},"gslivm2025-supp-table-8","Ultra-long sequences with nr = 2: mapping time vs duration, peak memory and rendering metrics; arXiv v1 supplementary",[15993],"R3LIVE or FAST-LIVO dataset (not stated)",[219,23],[40,23],[81],[15925],{"slug":15999,"sourceId":15925,"sourceLabel":15926,"sourceYear":562,"table":69,"note":16000,"datasets":16001,"metrics":16004,"families":16005,"methods":16006,"methodIds":16007,"rows":1069,"failures":30},"gslivm2025-table-1","Rendering quality, mean over all observation images; reduced scenes of at most 100 frames and 5 min reconstruction; NeRF-SLAM, MonoGS and 3DGS given C…",[15959,16002,16003,8050],"FAST-LIVO dataset (row-to-dataset mapping inferred from caption order)","NTU-VIRAL",[23],[23],[15973,15974,15975,81],[15925,5824,3183],{"slug":16009,"sourceId":15925,"sourceLabel":15926,"sourceYear":562,"table":108,"note":16010,"datasets":16011,"metrics":16012,"families":16013,"methods":16014,"methodIds":16015,"rows":224,"failures":30},"gslivm2025-table-2","Mapping time (MT) against sequence duration (DT), number of 3D Gaussians and maximum GPU memory for the full sequence, ns = 3",[15959,5829,16003,8050],[219,23],[40,23],[81],[15925],{"slug":16017,"sourceId":1476,"sourceLabel":16018,"sourceYear":698,"table":325,"note":16019,"datasets":16020,"metrics":16022,"families":16023,"methods":16024,"methodIds":16027,"rows":224,"failures":30},"fastlio2021-table-ii","Xu & Zhang, 2021","Running time of the Kalman gain computation with the conventional versus the proposed formula, same pipeline and number of feature points",[16021],"not_reported (own data)",[23],[23],[16025,16026],"New Formula (FAST-LIO)","Old Formula (conventional Kalman gain)",[1476],{"slug":16029,"sourceId":1476,"sourceLabel":16018,"sourceYear":698,"table":279,"note":16030,"datasets":16031,"metrics":16033,"families":16034,"methods":16035,"methodIds":16038,"rows":274,"failures":30},"fastlio2021-table-iii","Processing time for a LiDAR scan at 10 Hz in the handheld indoor large-rotation test; LOAM variants use FAST-LIO feature extraction; effective feature…",[16032],"own handheld indoor data",[38],[40],[12642,16036,16037],"LOAM (livox_mapping implementation [10])","LOAM+IMU (livox_horizon_loam, loosely coupled)",[1476,4784],{"slug":16040,"sourceId":1476,"sourceLabel":16018,"sourceYear":698,"table":1193,"note":16041,"datasets":16042,"metrics":16043,"families":16044,"methods":16045,"methodIds":16046,"rows":63,"failures":30},"fastlio2021-text-sec-iv-b","Indoor UAV flight on a circle path (1.8 m radius, 1.4 m height), landed at take-off point; 50 Hz odometry",[13442],[1551,38],[40,1553],[12642],[1476],{"slug":16048,"sourceId":1476,"sourceLabel":16018,"sourceYear":698,"table":1221,"note":16049,"datasets":16050,"metrics":16052,"families":16053,"methods":16054,"methodIds":16055,"rows":63,"failures":30},"fastlio2021-text-sec-iv-d","Handheld loop around HKU Main Building, returned to start after about 140 m; scan rate 10 Hz",[16051],"own handheld outdoor data",[1551,38],[40,1553],[12642],[1476],{"slug":16057,"sourceId":1476,"sourceLabel":16018,"sourceYear":698,"table":16058,"note":16059,"datasets":16060,"metrics":16062,"families":16063,"methods":16064,"methodIds":16066,"rows":63,"failures":30},"fastlio2021-text-sec-iv-d-lins","Text Sec.IV-D (LINS)","LINS seaport dataset (Velodyne VLP-16, Xsens MTiG-710), both at 10 Hz; FAST-LIO uses 784 feature points per scan, LINS downsamples to 147",[16061],"LINS dataset",[38],[40],[12642,16065],"LINS [21]",[1476,4783],{"slug":16068,"sourceId":16069,"sourceLabel":16070,"sourceYear":16,"table":69,"note":16071,"datasets":16072,"metrics":16074,"families":16075,"methods":16076,"methodIds":16078,"rows":641,"failures":30},"xu2019ogmvslam-table-1","xu2019ogmvslam","Xu et al., 2019","Localization mode on the building-scale maps; 15 AprilTag markers on the floor of an 80 m basement corridor loop, five loops with a robotic wheelchair…",[16073],"authors' basement corridor data",[23],[23],[16077],"proposed OGM-enhanced ORB2 RGB-D RTLS",[16069],{"slug":16080,"sourceId":16069,"sourceLabel":16070,"sourceYear":16,"table":108,"note":16081,"datasets":16082,"metrics":16083,"families":16084,"methods":16085,"methodIds":16086,"rows":641,"failures":30},"xu2019ogmvslam-table-2","Distance between adjacent markers estimated by the system versus measured true distance, same corridor experiment",[16073],[1819],[1821],[16077],[16069],{"slug":16088,"sourceId":16069,"sourceLabel":16070,"sourceYear":16,"table":3060,"note":16089,"datasets":16090,"metrics":16092,"families":16093,"methods":16094,"methodIds":16096,"rows":274,"failures":274},"xu2019ogmvslam-text-sec-4-2","Localization-mode update rate of the 2D pose on the OGM as reported by ROS rostopic, 640 x 480 RGB and registered depth images",[16091],"authors' laboratory data",[148],[40],[16095],"proposed OGM-enhanced ORB2 RGB-D RTLS (localization mode)",[16069],{"slug":16098,"sourceId":16069,"sourceLabel":16070,"sourceYear":16,"table":16099,"note":16100,"datasets":16101,"metrics":16103,"families":16104,"methods":16105,"methodIds":16106,"rows":274,"failures":30},"xu2019ogmvslam-text-sec-5-2-1","Text Sec.5.2.1","Repeatability test: one fixed marker measured 100 times from each of six camera locations (600 measurements) in the laboratory-scale map; accuracy not…",[16102],"authors' laboratory room data",[23],[23],[16077],[16069],{"slug":16108,"sourceId":321,"sourceLabel":16109,"sourceYear":306,"table":731,"note":16110,"datasets":16111,"metrics":16113,"families":16114,"methods":16115,"methodIds":16118,"rows":2119,"failures":356},"fastlio2-2022-table-iv","Xu et al., 2022","Absolute translational error RMSE (m) in sequences with good ground truth; loop closure of LILI-OM and LIO-SAM deactivated; all on Manifold 2-C; map-s…",[309,310,312,16112],"UrbanLoco HK (ulhk)",[568],[25],[16116,16117,5567,4769,334],"FAST-LIO2 (1000m), default local map size","FAST-LIO2 (Feature), feature-based variant",[321,337,4783,338],{"slug":16120,"sourceId":321,"sourceLabel":16109,"sourceYear":306,"table":818,"note":16121,"datasets":16122,"metrics":16124,"families":16125,"methods":16126,"methodIds":16127,"rows":1339,"failures":800},"fastlio2-2022-table-v","End-to-end errors (m) in sequences that end at the start position; LILI-OM parameters tuned per lili sequence, FAST-LIO2 parameters fixed; map-size va…",[309,16123,16112],"LiLi-OM dataset (lili)",[1551],[1553],[16116,16117,5567,4769,334],[321,337,4783,338],{"slug":16129,"sourceId":321,"sourceLabel":16109,"sourceYear":306,"table":827,"note":16130,"datasets":16131,"metrics":16132,"families":16133,"methods":16134,"methodIds":16137,"rows":16138,"failures":30},"fastlio2-2022-table-vi","Average total processing time per scan (odometry plus mapping) of FAST-LIO2 with 1000 m map; competitor Odo.\u002FMap. columns (LILI-OM, LIO-SAM, LINS) and…",[309,16123,310,312,16112],[38],[40],[16135,16136],"FAST-LIO2 (1000)","FAST-LIO2 (ARM)",[321],38,{"slug":16140,"sourceId":321,"sourceLabel":16109,"sourceYear":306,"table":838,"note":16141,"datasets":16142,"metrics":16144,"families":16145,"methods":16146,"methodIds":16149,"rows":274,"failures":30},"fastlio2-2022-table-vii","Private handheld sequence at 100 Hz scan rate, about 650 m outdoor-indoor hybrid scene; component times omitted, totals only",[16143],"private handheld dataset (Livox Avia)",[38],[40],[16147,16136,16148],"FAST-LIO [22] (Intel)","FAST-LIO2 (Intel)",[1476,321],{"slug":16151,"sourceId":321,"sourceLabel":16109,"sourceYear":306,"table":8521,"note":16152,"datasets":16153,"metrics":16154,"families":16155,"methods":16156,"methodIds":16157,"rows":63,"failures":154},"fastlio2-2022-text-sec-vii-b1","Handheld 100 Hz sequence returning to the start after about 650 m",[16143],[1551,23],[1553,23],[1437,5567],[321,337],{"slug":16159,"sourceId":321,"sourceLabel":16109,"sourceYear":306,"table":16160,"note":16161,"datasets":16162,"metrics":16164,"families":16165,"methods":16166,"methodIds":16167,"rows":154,"failures":30},"fastlio2-2022-text-sec-vii-b2","Text Sec.VII-B2","Aggressive UAV flip, average and maximum angular velocity 912 and 1198 deg\u002Fs",[16163],"private UAV flight",[38],[40],[1437],[321],{"slug":16169,"sourceId":321,"sourceLabel":16109,"sourceYear":306,"table":16170,"note":16171,"datasets":16172,"metrics":16173,"families":16174,"methods":16175,"methodIds":16176,"rows":154,"failures":154},"fastlio2-2022-text-sec-vii-b3","Text Sec.VII-B3","Fast-motion handheld run on a footbridge, up to 7 m\u002Fs, returning to start; trajectory 81 m",[16143],[1551],[1553],[1437],[321],{"slug":16178,"sourceId":321,"sourceLabel":16109,"sourceYear":306,"table":8279,"note":16179,"datasets":16180,"metrics":16182,"families":16183,"methods":16184,"methodIds":16185,"rows":274,"failures":30},"fastlio2-2022-text-sec-vii-c","Airborne mapping at Hong Kong Wetland Park with down-facing Livox Avia at 10 Hz; no quantitative ground truth; LILI-OM failed on all three",[16181],"private aerial dataset (Hong Kong Wetland Park)",[38],[40],[1437],[321],{"slug":16187,"sourceId":16188,"sourceLabel":16189,"sourceYear":562,"table":33,"note":16190,"datasets":16191,"metrics":16193,"families":16194,"methods":16195,"methodIds":16197,"rows":416,"failures":30},"xu2025pointleveluncertainty-table-4","xu2025pointleveluncertainty","Xu et al., 2025a","Random Forest, qualified if C2C below 20 mm; mean over 5 spatial folds with 95% CI",[16192],"BMW assembly hall MLS dataset",[74,23],[78,23],[16196],"Random Forest",[],{"slug":16199,"sourceId":16188,"sourceLabel":16189,"sourceYear":562,"table":244,"note":16200,"datasets":16201,"metrics":16202,"families":16203,"methods":16204,"methodIds":16206,"rows":416,"failures":30},"xu2025pointleveluncertainty-table-5","XGBoost, same folds and labels as Table 4; mean with 95% CI",[16192],[74,23],[78,23],[16205],"XGBoost",[],{"slug":16208,"sourceId":16188,"sourceLabel":16189,"sourceYear":562,"table":4020,"note":16209,"datasets":16210,"metrics":16211,"families":16212,"methods":16213,"methodIds":16215,"rows":154,"failures":30},"xu2025pointleveluncertainty-text-sec-3-3","Agreement of RF and XGBoost feature importance scores",[16192],[23],[23],[16214],"RF versus XGBoost",[],{"slug":16217,"sourceId":16218,"sourceLabel":16219,"sourceYear":562,"table":69,"note":16220,"datasets":16221,"metrics":16223,"families":16224,"methods":16225,"methodIds":16228,"rows":29,"failures":30},"xu2025dualmlsuncertainty-table-1","xu2025dualmlsuncertainty","Xu et al., 2025b","Trajectory drift as shortest distance between each total-station reference trajectory point and the MLS trajectory (no time sync)",[16222],"TUM Geodetic Lab dual-MLS scans",[23],[23],[16226,16227],"Leica BLK ARC","Z+F FlexScan 22",[],{"slug":16230,"sourceId":16218,"sourceLabel":16219,"sourceYear":562,"table":108,"note":16231,"datasets":16232,"metrics":16233,"families":16234,"methods":16235,"methodIds":16237,"rows":224,"failures":30},"xu2025dualmlsuncertainty-table-2","Random noise: STD of orthogonal distances to a best-fit plane (wall, ceiling, floor) or RANSAC cylinder",[16222],[1830],[78],[16226,16236,16227],"RTC360",[],{"slug":16239,"sourceId":16218,"sourceLabel":16219,"sourceYear":562,"table":3060,"note":16240,"datasets":16241,"metrics":16242,"families":16243,"methods":16244,"methodIds":16245,"rows":154,"failures":154},"xu2025dualmlsuncertainty-text-sec-4-2","P2P comparison at B&W targets with local alignment (only the first static part used for alignment)",[16222],[23],[23],[16226],[],{"slug":16247,"sourceId":16248,"sourceLabel":16249,"sourceYear":68,"table":244,"note":16250,"datasets":16251,"metrics":16252,"families":16253,"methods":16254,"methodIds":16255,"rows":578,"failures":30},"xu2026propagationprediction-table-5","xu2026propagationprediction","Xu et al., 2026","Regression of per-point C2C distance; mean over 5 folds with 95% CI",[16192],[23],[23],[13992,16205],[],{"slug":16257,"sourceId":16248,"sourceLabel":16249,"sourceYear":68,"table":1072,"note":16258,"datasets":16259,"metrics":16260,"families":16261,"methods":16262,"methodIds":16263,"rows":578,"failures":30},"xu2026propagationprediction-table-6","Proportion of points whose absolute prediction error does not exceed m mm; mean with 95% CI",[16192],[23],[23],[13992,16205],[],{"slug":16265,"sourceId":16266,"sourceLabel":16267,"sourceYear":345,"table":69,"note":16268,"datasets":16269,"metrics":16270,"families":16271,"methods":16272,"methodIds":16275,"rows":1079,"failures":30},"psmslam2017-table-1","psmslam2017","Yan et al., 2017","TUM RGB-D visual odometry only (no back-end); RMSE computed with the TUM benchmark scripts; PSM VO RPE on fr1\u002Fdesk2 printed as 0.50 (likely 0.050)",[2872],[568,330],[25,332],[16273,16274],"PSM VO (visual odometry)","sigma-DVO (visual odometry)",[16266],{"slug":16277,"sourceId":16266,"sourceLabel":16267,"sourceYear":345,"table":108,"note":16278,"datasets":16279,"metrics":16280,"families":16281,"methods":16282,"methodIds":16289,"rows":429,"failures":356},"psmslam2017-table-2","Complete SLAM absolute trajectory error on TUM RGB-D; '-' = no result given",[2872],[568],[25],[16283,16284,16285,16286,13253,16287,16288],"DVO SLAM [14]","ElasticFusion [34]","Kintinuous [33]","MRSMap [28]","RGB-D SLAM [4] (Endres et al.)","sigma-DVO SLAM",[2915,2448,13640,16266],{"slug":16291,"sourceId":16266,"sourceLabel":16267,"sourceYear":345,"table":17,"note":16292,"datasets":16293,"metrics":16294,"families":16295,"methods":16296,"methodIds":16297,"rows":29,"failures":30},"psmslam2017-table-3","Complete SLAM ATE of sigma-DVO SLAM and PSM SLAM on TUM RGB-D (keyframe counts in the same table not extracted)",[2872],[568],[25],[13253,16288],[16266],{"slug":16299,"sourceId":16266,"sourceLabel":16267,"sourceYear":345,"table":33,"note":16300,"datasets":16301,"metrics":16302,"families":16303,"methods":16304,"methodIds":16305,"rows":608,"failures":30},"psmslam2017-table-4","Surface reconstruction accuracy on ICL-NUIM living room with noise: mean distance from reconstructed points to the nearest ground-truth surface (m)",[2870],[75],[78],[16283,16284,16285,16286,13253,16287],[2915,2448,13640,16266],{"slug":16307,"sourceId":16266,"sourceLabel":16267,"sourceYear":345,"table":244,"note":16308,"datasets":16309,"metrics":16310,"families":16311,"methods":16312,"methodIds":16313,"rows":608,"failures":30},"psmslam2017-table-5","Absolute trajectory error on ICL-NUIM living room with noise (m)",[2870],[568],[25],[16283,16284,16285,16286,13253,16287],[2915,2448,13640,16266],{"slug":16315,"sourceId":16316,"sourceLabel":16317,"sourceYear":2267,"table":69,"note":16318,"datasets":16319,"metrics":16320,"families":16321,"methods":16322,"methodIds":16328,"rows":2607,"failures":30},"gsslam2024-table-1","gsslam2024","Yan et al., 2024","Replica ATE RMSE, 8 scenes; * = reproduced with official code; methods in upper part run below 5 FPS",[3223],[568],[25],[16323,16324,16325,81,16326,16327],"CoSLAM [ 41 ]","ESLAM [ 11 ]","NICE-SLAM [ 55 ]","Point-SLAM [ 27 ]","Vox-Fusion ∗ [ 48 ]",[3181,3182,3184,3185],{"slug":16330,"sourceId":16316,"sourceLabel":16317,"sourceYear":2267,"table":108,"note":16331,"datasets":16332,"metrics":16333,"families":16334,"methods":16335,"methodIds":16342,"rows":1069,"failures":30},"gsslam2024-table-2","TUM RGB-D ATE on three sequences; * = reproduced with official code",[2872],[568],[25],[16336,16323,16337,16324,16338,16339,16325,16340,81,3175,16327,16341],"BAD-SLAM [ 30 ]","DI-Fusion [ 9 ]","ElasticFusion [ 46 ]","Kintinuous [ 45 ]","ORB-SLAM2 [ 20 ]","iMAP ∗ [ 35 ]",[9325,3181,2915,3182,6378,2448,3184,1511,3185],{"slug":16344,"sourceId":16316,"sourceLabel":16317,"sourceYear":2267,"table":17,"note":16345,"datasets":16346,"metrics":16347,"families":16348,"methods":16349,"methodIds":16351,"rows":1042,"failures":30},"gsslam2024-table-3","Replica reconstruction averaged over 8 scenes; meshes from TSDF fusion of estimated poses and depth (Supp. Sec. 5); precision, recall and F1 at 1 cm t…",[3223],[74,76,23],[78,23],[16323,16324,16325,81,16350],"Vox-Fusion [ 48 ]",[3181,3182,3184],{"slug":16353,"sourceId":16316,"sourceLabel":16317,"sourceYear":2267,"table":33,"note":16354,"datasets":16355,"metrics":16356,"families":16357,"methods":16358,"methodIds":16361,"rows":224,"failures":63},"gsslam2024-table-4","Runtime and memory on Replica room0 (GS-SLAM on i9-13900K and RTX 4090; hardware of baselines not stated)",[3223],[148,219],[40],[16359,16324,16360,16325,16326,16350],"CoSLAM [ 55 ]","GS-SLAM",[3181,3182,3184,3185],{"slug":16363,"sourceId":16316,"sourceLabel":16317,"sourceYear":2267,"table":244,"note":16364,"datasets":16365,"metrics":16366,"families":16367,"methods":16368,"methodIds":16370,"rows":1042,"failures":120},"gsslam2024-table-5","Runtime and memory on TUM RGB-D; GS-SLAM (light) uses zero-order spherical harmonics",[2872],[148,219],[40],[16324,16360,16369,16325,3175],"GS-SLAM (light)",[3182,3184,3185],{"slug":16372,"sourceId":16373,"sourceLabel":16374,"sourceYear":68,"table":69,"note":16375,"datasets":16376,"metrics":16378,"families":16379,"methods":16380,"methodIds":16382,"rows":10593,"failures":578},"yan2026tunnel-table-1","yan2026tunnel","Yan et al., 2026a","ATE (RMSE, m) on KMCT; each ROS bag run five times and the best trial reported; 'Failed' = failure to run",[16377],"Kimera-Multi Campus-Tunnel (KMCT)",[568,330],[25,332],[2378,9129,334,2380,2197,892,4271,16381,1606],"VINS-FEN",[2385,321,395,338,2386,763,251,16373],{"slug":16384,"sourceId":16373,"sourceLabel":16374,"sourceYear":68,"table":108,"note":16385,"datasets":16386,"metrics":16388,"families":16389,"methods":16390,"methodIds":16393,"rows":721,"failures":63},"yan2026tunnel-table-2","ATE (RMSE, m) on WHU-Helmet with dataset ground-truth trajectory; LiDAR baselines use variants adapted to the Livox configuration; 'Failed' = failure…",[16387],"WHU-Helmet (WHUH)",[568,330],[25,332],[9361,2378,9129,6345,461,892,16391,16392,16381,1606],"R3live++","This work",[7567,2385,321,2117,2118,763,9122,251,16373],{"slug":16395,"sourceId":16373,"sourceLabel":16374,"sourceYear":68,"table":17,"note":16396,"datasets":16397,"metrics":16398,"families":16399,"methods":16400,"methodIds":16401,"rows":356,"failures":30},"yan2026tunnel-table-3","Mean Map Entropy of dense LiDAR maps, lower is better (local consistency only); averages over 7 KMCT sequences for methods that ran on all; per-sequen…",[16377],[23],[23],[2378,9129,2380,4271],[2385,321,2386,16373],{"slug":16403,"sourceId":16373,"sourceLabel":16374,"sourceYear":68,"table":33,"note":16404,"datasets":16405,"metrics":16406,"families":16407,"methods":16408,"methodIds":16409,"rows":120,"failures":30},"yan2026tunnel-table-4","Mean Map Entropy on WHU-Helmet, lower is better; average of Tunnel and Subway for methods that ran on both; LOAM failed on Tunnel",[16387],[23],[23],[9361,2378,9129,6345,4271,16391],[7567,2385,321,2117,9122,16373],{"slug":16411,"sourceId":16373,"sourceLabel":16374,"sourceYear":68,"table":244,"note":16412,"datasets":16413,"metrics":16414,"families":16415,"methods":16416,"methodIds":16418,"rows":849,"failures":30},"yan2026tunnel-table-5","Time consumption per frame; proposed method component times (VIO, LO, EKF) not extracted, only their sum",[16377,16387],[38],[40],[2380,9140,16417],"This work (Sum of VIO, LO and EKF)",[2386,9122,16373],{"slug":16420,"sourceId":16373,"sourceLabel":16374,"sourceYear":68,"table":1072,"note":16421,"datasets":16422,"metrics":16423,"families":16424,"methods":16425,"methodIds":16429,"rows":120,"failures":30},"yan2026tunnel-table-6","Ablation of deep-feature VIO and CEKF; averages over KMCT 07_api-003, 07_sob-002 and 07_spl-007; per-sequence rows not extracted",[16377],[568,330],[25,332],[16426,16427,16428],"Base + EKF (standard EKF without selection)","Proposed method","Standard VIO + Base (VINS-Mono front end)",[16373],{"slug":16431,"sourceId":16373,"sourceLabel":16374,"sourceYear":68,"table":621,"note":16432,"datasets":16433,"metrics":16434,"families":16435,"methods":16436,"methodIds":16441,"rows":578,"failures":30},"yan2026tunnel-table-7","Degeneracy detection modules swapped into the same base system; averages over KMCT 07_api-003, 07_sob-002 and 07_spl-007; per-sequence rows not extrac…",[16377],[568,330],[25,332],[16437,16438,16427,16439,16440],"Base Only (no degeneracy detection)","LION + Base","X-ICP + Base","Zhang's + Base",[16373],{"slug":16443,"sourceId":16373,"sourceLabel":16374,"sourceYear":68,"table":4863,"note":16444,"datasets":16445,"metrics":16446,"families":16447,"methods":16448,"methodIds":16449,"rows":63,"failures":30},"yan2026tunnel-text-sec-4-3","Voxelized partial map compared with the KMCT ground-truth map; 3 sigma voxel error",[16377],[75],[78],[2380,4271],[2386,16373],{"slug":16451,"sourceId":16452,"sourceLabel":16453,"sourceYear":68,"table":279,"note":16454,"datasets":16455,"metrics":16457,"families":16458,"methods":16459,"methodIds":16462,"rows":339,"failures":30},"yan2026-underground3dgsslam-table-iii","yan2026_underground3dgsslam","Yan et al., 2026b","ATE RMSE on the authors' nine underground RGB-D field sequences (Kinect2 on a mobile robot); trajectory reference from a multi-sensor (LiDAR, IMU, cam…",[16456],"Underground_RGB-D (authors' field test dataset)",[568],[25],[3166,3168,5233,16460,5866,3174,6668,81,16461,3177],"GS-ICP SLAM","RTG-SLAM",[3181,2915,3182,3183,3184,1511,3186,16452],{"slug":16464,"sourceId":16452,"sourceLabel":16453,"sourceYear":68,"table":818,"note":16465,"datasets":16466,"metrics":16467,"families":16468,"methods":16469,"methodIds":16470,"rows":274,"failures":30},"yan2026-underground3dgsslam-table-v","Average time per frame for each stage, averaged over multiple underground sequences",[16456],[38],[40],[81],[16452],{"slug":16472,"sourceId":16452,"sourceLabel":16453,"sourceYear":68,"table":827,"note":16473,"datasets":16474,"metrics":16475,"families":16476,"methods":16477,"methodIds":16478,"rows":598,"failures":30},"yan2026-underground3dgsslam-table-vi","ATE RMSE on three TUM RGB-D sequences; ElasticFusion and ORB-SLAM2 values are identical to those printed in SplaTAM Table 1 (from Point-SLAM), suggest…",[2872],[568],[25],[3166,3168,5233,3170,5866,3174,6668,81,16461,3177],[3181,2915,3182,3183,3184,1511,3186,16452],{"slug":16480,"sourceId":16452,"sourceLabel":16453,"sourceYear":68,"table":1278,"note":16481,"datasets":16482,"metrics":16483,"families":16484,"methods":16485,"methodIds":16486,"rows":154,"failures":30},"yan2026-underground3dgsslam-text-sec-iv-c","Average frame rate over multiple underground sequences",[16456],[148],[40],[81],[16452],{"slug":16488,"sourceId":16489,"sourceLabel":16490,"sourceYear":4828,"table":69,"note":16491,"datasets":16492,"metrics":16503,"families":16504,"methods":16505,"methodIds":16510,"rows":2262,"failures":30},"yang2016goicp-table-1","yang2016goicp","Yang et al., 2016","Go-ICP with distance transform and trimming on 10 partially overlapping point-set pairs; 100 random relative poses per pair and direction; N = 1000 da…",[16493,16494,16495,16496,16497,16498,16499,16500,16501,16502],"Bowl (Kinect, authors)","Buddha (Stanford 3D)","Bunny (Stanford 3D)","Chef (ref. [67])","Denture (structured light scanner)","Dinosaur (ref. [67])","Dragon (Stanford 3D)","Loom (Kinect, authors)","Owl (ref. [66])","Room (ref. [68])",[38],[40],[16506,16507,16508,16509],"Go-ICP (DT, trimming rho = 10%)","Go-ICP (DT, trimming rho = 20%)","Go-ICP (DT, trimming rho = 30%)","Go-ICP (DT, trimming rho = 40%)",[16489],{"slug":16512,"sourceId":16489,"sourceLabel":16490,"sourceYear":4828,"table":16513,"note":16514,"datasets":16515,"metrics":16518,"families":16519,"methods":16520,"methodIds":16523,"rows":654,"failures":63},"yang2016goicp-text-sec-6-2","Text Sec. 6.2","Partial scans registered to full reconstructed models: 10 bunny scans and 10 dragon scans, 100 random initial poses each (2,000 tasks), N = 1000 data…",[16516,16517,6626],"Stanford bunny","Stanford bunny and dragon",[23,38,1933],[40,23,1935],[16521,16522],"Go-ICP (DT and kd-tree)","Go-ICP (DT)",[16489],{"slug":16525,"sourceId":16489,"sourceLabel":16490,"sourceYear":4828,"table":16526,"note":16527,"datasets":16528,"metrics":16530,"families":16531,"methods":16532,"methodIds":16534,"rows":274,"failures":63},"yang2016goicp-text-sec-6-3","Text Sec. 6.3","Trimmed Go-ICP on the 10 partially overlapping pairs of Table 1 (2,000 tasks); errors against manually set ground truths",[16529],"10 point-set pairs of Table 1",[23,1933],[23,1935],[16533],"Go-ICP (DT, trimming)",[16489],{"slug":16536,"sourceId":16489,"sourceLabel":16490,"sourceYear":4828,"table":16537,"note":16538,"datasets":16539,"metrics":16541,"families":16542,"methods":16543,"methodIds":16544,"rows":356,"failures":63},"yang2016goicp-text-sec-6-4","Text Sec. 6.4","Camera localization: 100 depth images (400 to 600 points each) from one sequence of the camera localization dataset [68] registered to a 3-D office sc…",[16540],"camera localization dataset [68], office scene",[23,38],[40,23],[16522],[16489],{"slug":16546,"sourceId":16547,"sourceLabel":16548,"sourceYear":213,"table":33,"note":16549,"datasets":16550,"metrics":16551,"families":16552,"methods":16553,"methodIds":16560,"rows":2252,"failures":30},"d3vo2020-table-4","d3vo2020","Yang et al., 2020a","KITTI odometry test split of the paper (sequences 01, 02, 06, 08, 09, 10; 00, 03, 04, 05, 07 are in the depth-network training set); relative translat…",[469],[438],[441],[16554,16555,16556,16557,16558,16559],"D3VO (monocular)","DSO [ 16 ] (monocular)","ORB [ 52 ] (monocular)","ORB2 [ 53 ] (stereo)","S. DSO [ 74 ] (stereo)","S. LSD [ 18 ] (stereo)",[1507,119,1511],{"slug":16562,"sourceId":16547,"sourceLabel":16548,"sourceYear":213,"table":1072,"note":16563,"datasets":16564,"metrics":16565,"families":16566,"methods":16567,"methodIds":16583,"rows":6820,"failures":578},"d3vo2020-table-6","EuRoC MAV test sequences (all others used for training); RMS of ATE after aligning with ground truth (alignment type not stated); M+I values from Delm…",[743],[568],[25],[16568,16554,16569,16555,16570,16571,16572,16573,16574,16575,16576,16556,16577,16578,16579,16580,16581,16582],"Basalt [ 71 ] (stereo-inertial)","D3VO (monocular, listed in the stereo-inertial block)","Dd (monocular)","Dd+Dp (monocular)","Dd+Du (monocular)","End-end VO (D3VO PoseNet only) (monocular)","MSCKF [ 51 ] (monocular-inertial)","OKVIS [ 44 ] (monocular-inertial)","OKVIS [ 44 ] (stereo-inertial)","ROVIO [ 3 ] (monocular-inertial)","SVO [ 22 ] (monocular-inertial)","VI-DSO [ 72 ] (monocular-inertial)","VI-ORB [ 54 ] (monocular-inertial)","VINS [ 57 ] (monocular-inertial)","VINS [ 57 ] (stereo-inertial)",[1507,3756,1509,1510,119,1513],{"slug":16585,"sourceId":14330,"sourceLabel":16586,"sourceYear":213,"table":16587,"note":16588,"datasets":16589,"metrics":16591,"families":16592,"methods":16593,"methodIds":16597,"rows":356,"failures":30},"yang2020gnc-text-sec-v-a-g-reg","Yang et al., 2020b","Text Sec.V-A G-REG","PASCAL+ car-2 mesh; points sampled on vertices, edges and faces, random transform, Gaussian noise sigma 0.05; 40 point-to-point, 80 point-to-line and…",[16590],"PASCAL+ (car-2 mesh)",[23],[23],[16594,16595,16596,4204],"ADAPT","GNC-GM","GNC-TLS",[4183,14330],{"slug":16599,"sourceId":14330,"sourceLabel":16586,"sourceYear":213,"table":16600,"note":16601,"datasets":16602,"metrics":16604,"families":16605,"methods":16606,"methodIds":16607,"rows":654,"failures":30},"yang2020gnc-text-sec-v-a-p-reg","Text Sec.V-A P-REG","Stanford Bunny scaled into a unit cube, random rigid transform per run, N = 100 correspondences, inlier noise sigma 0.01, outliers replaced by random…",[16603],"Stanford 3D Scanning Repository Bunny",[23],[23],[16594,16595,16596,4204],[4183,14330],{"slug":16609,"sourceId":14330,"sourceLabel":16586,"sourceYear":213,"table":16610,"note":16611,"datasets":16612,"metrics":16614,"families":16615,"methods":16616,"methodIds":16617,"rows":154,"failures":30},"yang2020gnc-text-sec-v-b-csail","Text Sec.V-B CSAIL","CSAIL pose graph (described in SE-Sync [6]); odometry kept, random outlier loop closures; 10 Monte Carlo runs; average trajectory error [m]; all techn…",[16613],"CSAIL",[23],[23],[16596],[14330],{"slug":16619,"sourceId":14330,"sourceLabel":16586,"sourceYear":213,"table":16620,"note":16621,"datasets":16622,"metrics":16624,"families":16625,"methods":16626,"methodIds":16627,"rows":120,"failures":274},"yang2020gnc-text-sec-v-b-intel","Text Sec.V-B INTEL","INTEL pose graph (described in SE-Sync [6]); odometry kept, loop closures spoiled with random outliers (random pose pairs with random measurements); 1…",[16623],"INTEL",[23],[23],[16594,16595,16596],[14330],{"slug":16629,"sourceId":14330,"sourceLabel":16586,"sourceYear":213,"table":3144,"note":16630,"datasets":16631,"metrics":16633,"families":16634,"methods":16635,"methodIds":16636,"rows":356,"failures":30},"yang2020gnc-text-sec-v-c","FG3DCar, all 600 images, ground-truth 3D shape model and 2D landmarks, random incorrect 3D-2D correspondences as outliers; RANSAC with 100 max iterati…",[16632],"FG3DCar",[23],[23],[16594,16595,16596,4204],[4183,14330],{"slug":16638,"sourceId":14330,"sourceLabel":16586,"sourceYear":213,"table":16639,"note":16640,"datasets":16641,"metrics":16642,"families":16643,"methods":16644,"methodIds":16646,"rows":154,"failures":30},"yang2020gnc-text-sec-v-c-sos","Text Sec.V-C SOS","Outlier-free weighted shape alignment solved by the SOS relaxation (17), converted to an SDP with GloptiPoly 3 in Matlab; relaxation stated to be empi…",[2070],[23],[23],[16645],"SOS non-minimal solver for shape alignment (GloptiPoly 3, Matlab)",[14330],{"slug":16648,"sourceId":1789,"sourceLabel":16649,"sourceYear":698,"table":91,"note":16650,"datasets":16651,"metrics":16653,"families":16654,"methods":16655,"methodIds":16657,"rows":618,"failures":30},"yang2021teaser-table-i","Yang et al., 2021","Object pose estimation on eight scenes of the UW RGB-D dataset [36]: object (cereal box or cap) cut from the scene, scene randomly transformed, FPFH c…",[16652],"large-scale hierarchical multi-view RGB-D object dataset [36]",[23],[23],[16656],"TEASER",[1789],{"slug":16659,"sourceId":1789,"sourceLabel":16649,"sourceYear":698,"table":325,"note":16660,"datasets":16661,"metrics":16662,"families":16663,"methods":16664,"methodIds":16668,"rows":721,"failures":30},"yang2021teaser-table-ii","3DMatch test scenes, 3DSmoothNet correspondences, success = rotation error \u003C 10 deg and translation error \u003C 30 cm; beta = 5 cm; CERT = subset certifie…",[2578],[38,1933],[40,1935],[16665,16666,8954,16667],"RANSAC-10K","RANSAC-1K","TEASER++ (CERT)",[4183,1789],{"slug":16670,"sourceId":1789,"sourceLabel":16649,"sourceYear":698,"table":16671,"note":16672,"datasets":16673,"metrics":16675,"families":16676,"methods":16677,"methodIds":16678,"rows":274,"failures":154},"yang2021teaser-text-app-s","Text App. S","Increasing number of correspondences at a fixed 95% outlier rate, protocol of Sec. XI-B",[16674],"Stanford Bunny (synthetic correspondences)",[23,38],[40,23],[16656,8954],[1789],{"slug":16680,"sourceId":1789,"sourceLabel":16649,"sourceYear":698,"table":16681,"note":16682,"datasets":16683,"metrics":16684,"families":16685,"methods":16686,"methodIds":16687,"rows":608,"failures":30},"yang2021teaser-text-app-t","Text App. T","Per-scene values listed under the Fig. 18 panels in App. T for the Table I experiment; units are not printed per scene (Table I uses rad and m); per-s…",[16652],[23],[23],[16656],[1789],{"slug":16689,"sourceId":1789,"sourceLabel":16649,"sourceYear":698,"table":16690,"note":16691,"datasets":16692,"metrics":16693,"families":16694,"methods":16695,"methodIds":16697,"rows":154,"failures":154},"yang2021teaser-text-sec-viii-c","Text Sec. VIII-C","Cost of solving the full SDP relaxation directly",[3980],[38],[40],[16696],"TEASER SDP relaxation solved with MOSEK",[1789],{"slug":16699,"sourceId":1789,"sourceLabel":16649,"sourceYear":698,"table":16700,"note":16701,"datasets":16702,"metrics":16704,"families":16705,"methods":16706,"methodIds":16708,"rows":63,"failures":30},"yang2021teaser-text-sec-xi-a","Text Sec. XI-A","Optimality certification of GNC rotation estimates with K = 100 TIMs, 100 Monte Carlo runs per outlier rate",[16703],"Stanford Bunny (synthetic TIMs)",[23,38],[40,23],[16707],"TEASER++ certifier (Algorithm 3, DRS)",[1789],{"slug":16710,"sourceId":1789,"sourceLabel":16649,"sourceYear":698,"table":16711,"note":16712,"datasets":16713,"metrics":16714,"families":16715,"methods":16716,"methodIds":16717,"rows":63,"failures":63},"yang2021teaser-text-sec-xi-b","Text Sec. XI-B","Stanford Bunny downsampled to N = 100 correspondences, known scale, outlier rates 0% to 90%; laptop i7-8850H, 32 GB RAM",[16674],[38],[40],[8954],[1789],{"slug":16719,"sourceId":1789,"sourceLabel":16649,"sourceYear":698,"table":16720,"note":16721,"datasets":16722,"metrics":16724,"families":16725,"methods":16726,"methodIds":16728,"rows":154,"failures":30},"yang2021teaser-text-sec-xi-c","Text Sec. XI-C","Correspondence-free test on the Bunny downsampled to 100 points with partial overlap; Go-ICP with trimming as baseline",[16723],"Stanford Bunny (100 points)",[38],[40],[16727],"Go-ICP",[16489],{"slug":16730,"sourceId":1789,"sourceLabel":16649,"sourceYear":698,"table":16731,"note":16732,"datasets":16733,"metrics":16734,"families":16735,"methods":16736,"methodIds":16737,"rows":154,"failures":30},"yang2021teaser-text-sec-xi-e","Text Sec. XI-E","Certification cost on 3DMatch for problems with few TIMs",[2578],[38],[40],[16667],[1789],{"slug":16739,"sourceId":16740,"sourceLabel":16741,"sourceYear":306,"table":69,"note":16742,"datasets":16743,"metrics":16744,"families":16745,"methods":16746,"methodIds":16749,"rows":849,"failures":30},"voxfusion2022-table-1","voxfusion2022","Yang et al., 2022","Replica trajectory error (ATE via TUM scripts); iMap* is the NICE-SLAM re-implementation of iMAP",[3223],[568],[25],[16747,81,16748],"NICE-SLAM [ 40 ]","iMap* [ 31 ]",[6378,3184],{"slug":16751,"sourceId":16740,"sourceLabel":16741,"sourceYear":306,"table":108,"note":16752,"datasets":16753,"metrics":16754,"families":16755,"methods":16756,"methodIds":16758,"rows":4017,"failures":30},"voxfusion2022-table-2","Replica mesh reconstruction; iMAP values from its paper, NICE-SLAM values from its supplementary without mesh culling",[3223],[75,76,23],[78,23],[16747,81,16757],"iMap [ 31 ]",[6378,3184],{"slug":16760,"sourceId":16740,"sourceLabel":16741,"sourceYear":306,"table":17,"note":16761,"datasets":16762,"metrics":16763,"families":16764,"methods":16765,"methodIds":16766,"rows":1042,"failures":30},"voxfusion2022-table-3","ScanNet trajectory RMSE on 5 scenes selected as in NICE-SLAM; DI-Fusion, iMap* and NICE-SLAM values taken from NICE-SLAM; unit not printed in Table 3…",[2963],[568],[25],[16337,16747,81,16748],[6378,3184],{"slug":16768,"sourceId":16740,"sourceLabel":16741,"sourceYear":306,"table":33,"note":16769,"datasets":16770,"metrics":16771,"families":16772,"methods":16773,"methodIds":16774,"rows":63,"failures":30},"voxfusion2022-table-4","Average time per iteration on Replica, single NVIDIA RTX 3090",[3223],[23],[23],[81],[],{"slug":16776,"sourceId":16740,"sourceLabel":16741,"sourceYear":306,"table":244,"note":16777,"datasets":16778,"metrics":16779,"families":16780,"methods":16781,"methodIds":16782,"rows":356,"failures":30},"voxfusion2022-table-5","Memory of implicit decoder and voxel embeddings on Replica office-0; NICE-SLAM uses 4 dense grid levels",[3223],[219],[40],[3174,81],[3184],{"slug":16784,"sourceId":16785,"sourceLabel":16786,"sourceYear":2267,"table":325,"note":16787,"datasets":16788,"metrics":16789,"families":16790,"methods":16791,"methodIds":16793,"rows":415,"failures":30},"yang2024lifelong-table-ii","yang2024lifelong","Yang et al., 2024","Dynamic object removal on SemanticKITTI; point-wise labels, moving classes counted as dynamic; sequences and scan ranges follow the ERASOR setup; auth…",[8901],[23],[23],[15598,16792,81,8920],"Ground-Octomap",[3526,3528,16785],{"slug":16795,"sourceId":16785,"sourceLabel":16786,"sourceYear":2267,"table":279,"note":16796,"datasets":16797,"metrics":16801,"families":16802,"methods":16803,"methodIds":16805,"rows":1042,"failures":274},"yang2024lifelong-table-iii","Average Chamfer distance after aligning several session maps into one frame; poses from XGrids proprietary software (XGrid datasets) or SC-LIO-SAM and…",[16798,671,16799,16800],"LT-ParkingLot","XGrid-Outdoor","XGrid-Parking",[2343],[78],[522,16804,11699,81],"LT-SLAM",[6961,16785],{"slug":16807,"sourceId":16785,"sourceLabel":16786,"sourceYear":2267,"table":731,"note":16808,"datasets":16809,"metrics":16811,"families":16812,"methods":16813,"methodIds":16816,"rows":2119,"failures":30},"yang2024lifelong-table-iv","Map change detection against manually introduced changes (relocated buildings, cars, trees); precision and recall for positive differences (PD, new da…",[16798,16810,671,16799,16800],"Mean (four datasets)",[23],[23],[16814,81,16815],"KNN","PCL-OC",[16785],{"slug":16818,"sourceId":16785,"sourceLabel":16786,"sourceYear":2267,"table":818,"note":16819,"datasets":16820,"metrics":16821,"families":16822,"methods":16823,"methodIds":16825,"rows":618,"failures":30},"yang2024lifelong-table-v","Memory to store all input downsampled session maps versus the map version control store (base map, positive and negative differences, boundaries); NCL…",[16798,310,16799,16800],[219],[40],[16824,81],"All maps (store every input map)",[16785],{"slug":16827,"sourceId":16785,"sourceLabel":16786,"sourceYear":2267,"table":2225,"note":16828,"datasets":16829,"metrics":16830,"families":16831,"methods":16832,"methodIds":16833,"rows":154,"failures":30},"yang2024lifelong-text-sec-iv-a","Dynamic object removal processing time for a whole sequence; authors state it is 1.5 times faster than Ground-Octomap and that ERASOR could only proce…",[8901],[23],[23],[81],[16785],{"slug":16835,"sourceId":16785,"sourceLabel":16786,"sourceYear":2267,"table":1928,"note":16836,"datasets":16837,"metrics":16838,"families":16839,"methods":16840,"methodIds":16841,"rows":63,"failures":154},"yang2024lifelong-text-sec-iv-b","Time to align XGrid-Outdoor session maps of about 10M points including the full grid search, compared with LT-SLAM keyframe descriptor traversal and g…",[16799],[23],[23],[16804,81],[6961,16785],{"slug":16843,"sourceId":16785,"sourceLabel":16786,"sourceYear":2267,"table":1278,"note":16844,"datasets":16845,"metrics":16846,"families":16847,"methods":16848,"methodIds":16849,"rows":154,"failures":30},"yang2024lifelong-text-sec-iv-c","Map change detection run time on LT-ParkingLot with about 10K simulated change points in a 0.8M-point map; authors state KNN and PCL-OC take similar t…",[16798],[23],[23],[81],[16785],{"slug":16851,"sourceId":16852,"sourceLabel":16853,"sourceYear":2267,"table":108,"note":16854,"datasets":16855,"metrics":16857,"families":16858,"methods":16859,"methodIds":16864,"rows":2119,"failures":608},"yarovoi2024review-table-2","yarovoi2024review","Yarovoi & Cho, 2024","Hilti 2022 handheld construction-site sequences; errors vs motion-capture GT; translation = Euclidean distance, rotation = smallest angle; RMSE and ST…",[16856],"Hilti SLAM Challenge Dataset 2022",[329,568,23],[25,23],[16860,16861,334,16862,16863],"ART-SLAM final","ART-SLAM odom","Lego-LOAM (IMU)","Lego-LOAM (no IMU)",[395,338],{"slug":16866,"sourceId":16852,"sourceLabel":16853,"sourceYear":2267,"table":17,"note":16867,"datasets":16868,"metrics":16869,"families":16870,"methods":16871,"methodIds":16872,"rows":641,"failures":120},"yarovoi2024review-table-3","Average nearest-neighbor distance between the cloud accumulated with each method's LiDAR poses and a reference cloud generated with GT poses; no deske…",[16856],[3417],[78],[16860,16861,334,16862,16863],[395,338],{"slug":16874,"sourceId":16852,"sourceLabel":16853,"sourceYear":2267,"table":33,"note":16875,"datasets":16876,"metrics":16877,"families":16878,"methods":16879,"methodIds":16883,"rows":224,"failures":356},"yarovoi2024review-table-4","LIO-SAM on Exp06 replayed with LiDAR scans provided at 10, 5 or 2 Hz by skipping scans; * = loss of tracking",[16856],[329,568,23],[25,23],[16880,16881,16882],"LIO-SAM, LiDAR 10 Hz","LIO-SAM, LiDAR 2 Hz","LIO-SAM, LiDAR 5 Hz",[338],{"slug":16885,"sourceId":2117,"sourceLabel":16886,"sourceYear":16,"table":91,"note":16887,"datasets":16888,"metrics":16890,"families":16891,"methods":16892,"methodIds":16896,"rows":2119,"failures":30},"liomapping2019-table-i","Ye et al., 2019","Handheld VLP-16 + MTi-100 sequences with motion-capture ground truth; trajectories aligned with Umeyama's method (scale handling not stated); motion f…",[16889],"Own handheld motion-capture sequences",[568,23],[25,23],[16893,13430,16894,16895,461],"LIO","LIO-no-ex (no online extrinsic estimation)","LIO-raw (no motion compensation)",[2117,2118],{"slug":16898,"sourceId":2117,"sourceLabel":16886,"sourceYear":16,"table":325,"note":16899,"datasets":16900,"metrics":16902,"families":16903,"methods":16904,"methodIds":16905,"rows":120,"failures":30},"liomapping2019-table-ii","Mean processing time per new input of each module with a 16-line lidar; sweeps processed at 0.2 s (indoor) or 0.3 s (outdoor) intervals",[16901],"Own sequences (16-line lidar)",[23,38],[40,23],[13430],[2117],{"slug":16907,"sourceId":16908,"sourceLabel":16909,"sourceYear":374,"table":33,"note":16910,"datasets":16911,"metrics":16913,"families":16914,"methods":16915,"methodIds":16920,"rows":3757,"failures":30},"yin2023semanticbimloc-table-4","yin2023semanticbimloc","Yin et al., 2023","Ten self-collected VLP-16 sequences; RMSE of x-y translation and yaw against offline Cartographer 2D SLAM reference poses; trajectories aligned with t…",[16912],"self-collected NUS SDE4 sequences",[568,23],[25,23],[16916,16917,16918,16919],"ICP (ORG): libpointmatcher point-to-plane ICP with default geometric outlier filters (baseline)","Sem (ORG): ICP (ORG) plus semantic labelling and selection (ablation)","Sem (w_c w_rho): full semantic localization (proposed)","Sem (w_rho): semantic filtering with Huber weight only (ablation)",[12022,16908],{"slug":16922,"sourceId":16908,"sourceLabel":16909,"sourceYear":374,"table":1072,"note":16923,"datasets":16924,"metrics":16925,"families":16926,"methods":16927,"methodIds":16932,"rows":1069,"failures":30},"yin2023semanticbimloc-table-6","LiDAR-only comparison (no IMU for any method) on four sequences; 2D RMSE against the Cartographer 2D reference; Delta Z = mean height of the last 50 p…",[16912],[568,1551,23],[25,1553,23],[16928,16929,16930,16931],"BIM-based Localization (proposed)","DLO [55]","LOAM [5] (A-LOAM code)","Open3D SLAM [56]",[392,2088,16908],{"slug":16934,"sourceId":16908,"sourceLabel":16909,"sourceYear":374,"table":7969,"note":16935,"datasets":16936,"metrics":16937,"families":16938,"methods":16939,"methodIds":16940,"rows":356,"failures":30},"yin2023semanticbimloc-text-sec-4-3","Mean time cost of Algorithm 2 per scan in four case studies (Fig. 13, Table 5)",[16912],[38],[40],[16918],[16908],{"slug":16942,"sourceId":16943,"sourceLabel":16944,"sourceYear":698,"table":279,"note":16945,"datasets":16946,"metrics":16947,"families":16948,"methods":16949,"methodIds":16961,"rows":16962,"failures":30},"litamin2-2021-table-iii","litamin2_2021","Yokozuka et al., 2021","KITTI odometry sequences 00-10; KITTI stats translation error (%) averaged over 100-800 m segments with the benchmark code; rows without loop closure;…",[469],[438],[441],[16950,16951,16952,16953,16954,2197,16955,16956,16957,16958,16959,16960],"DeepLO (from [23])","LO-Net (Frame-to-Frame)","LO-Net (Frame-to-Model)","LOAM (from [10])","LOAM (open source, run by authors)","LiTAMIN [2], without loop closure","LiTAMIN2 (ICP), without loop closure","LiTAMIN2 (ICP+Cov), without loop closure","SuMa (Frame-to-Frame)","SuMa (Frame-to-Model), without loop closure","hdl graph slam",[394,395,16943,2118,450,3309,432],123,{"slug":16964,"sourceId":16943,"sourceLabel":16944,"sourceYear":698,"table":16965,"note":16966,"datasets":16967,"metrics":16968,"families":16969,"methods":16970,"methodIds":16975,"rows":224,"failures":30},"litamin2-2021-table-iii-overall","Table III (overall)","KITTI stats over all sequences 00-10 (final column), translation error %; LiTAMIN2 with 3 m voxels",[469],[438],[441],[16954,2197,16971,16955,16972,16956,16973,16957,16958,16974,16959,16960],"LiTAMIN [2], with loop closure","LiTAMIN2 (ICP), with loop closure","LiTAMIN2 (ICP+Cov), with loop closure","SuMa (Frame-to-Model), with loop closure",[394,395,16943,2118,432],{"slug":16977,"sourceId":16943,"sourceLabel":16944,"sourceYear":698,"table":731,"note":16978,"datasets":16979,"metrics":16980,"families":16981,"methods":16982,"methodIds":16983,"rows":224,"failures":30},"litamin2-2021-table-iv","KITTI odometry 00-10; absolute trajectory error, average of all frames (translation part, m); statistic and alignment not stated; loop closure as mark…",[469],[22],[25],[16954,2197,16971,16955,16972,16956,16973,16957,16958,16974,16959,16960],[394,395,16943,2118,432],{"slug":16985,"sourceId":16943,"sourceLabel":16944,"sourceYear":698,"table":818,"note":16986,"datasets":16987,"metrics":16988,"families":16989,"methods":16990,"methodIds":16992,"rows":654,"failures":30},"litamin2-2021-table-v","KITTI odometry 00-10; average odometry frame rate over all frames; LiTAMIN2, LiTAMIN and SuMa include loop closing, others without; all run by the aut…",[469],[148],[40],[16991,2197,16971,16972,16973,16974,16960],"LOAM (open source)",[394,395,16943,2118,432],{"slug":16994,"sourceId":16995,"sourceLabel":16996,"sourceYear":562,"table":17,"note":16997,"datasets":16998,"metrics":17000,"families":17001,"methods":17002,"methodIds":17004,"rows":578,"failures":356},"yu2025-3dgs-lidar-heritage-table-3","yu2025_3dgs_lidar_heritage","Yu et al., 2025","Dissemination comparison in Unity VR between the GeoSLAM point cloud (Point Cloud Viewer plugin) and the Polycam 3DGS model; hardware not reported",[16999],"Bouwpub case study (TU Delft)",[148,218,219,23],[40,23],[6110,14928,17003],"Point Cloud (13 million vertices)",[],{"slug":17006,"sourceId":16995,"sourceLabel":16996,"sourceYear":562,"table":3060,"note":17007,"datasets":17008,"metrics":17009,"families":17010,"methods":17011,"methodIds":17015,"rows":274,"failures":30},"yu2025-3dgs-lidar-heritage-text-sec-4-2","Laplacian variance of greyscale renderings of the Bouwpub front facade; higher means sharper high-frequency detail; HBIM model used as reference",[16999],[23],[23],[17012,17013,17014],"HBIM ground-truth model","LiDAR-based point cloud (GeoSLAM)","Polycam-based 3DGS model",[],{"slug":17017,"sourceId":17018,"sourceLabel":17019,"sourceYear":698,"table":17020,"note":17021,"datasets":17022,"metrics":17024,"families":17025,"methods":17026,"methodIds":17028,"rows":63,"failures":63},"yuan2021lidarcameracalib-text-sec-iv-a1","yuan2021lidarcameracalib","Yuan et al., 2021","Text Sec.IV-A1","Robustness test: 20 runs per scene setting with random initial values within ±5° and ±10 cm of the CAD value",[17023],"authors' 6 indoor and outdoor calibration scenes",[23,1933],[23,1935],[11009,17027],"proposed pipeline (Livox Avia and RealSense D435i)",[],{"slug":17030,"sourceId":17018,"sourceLabel":17019,"sourceYear":698,"table":17031,"note":17032,"datasets":17033,"metrics":17034,"families":17035,"methods":17036,"methodIds":17037,"rows":154,"failures":154},"yuan2021lidarcameracalib-text-sec-iv-a3","Text Sec.IV-A3","Cross validation: extrinsic from each scene applied to all six scenes (36 cases); 20% largest residuals removed as outliers",[17023],[23],[23],[11009],[],{"slug":17039,"sourceId":17018,"sourceLabel":17019,"sourceYear":698,"table":1193,"note":17040,"datasets":17041,"metrics":17043,"families":17044,"methods":17045,"methodIds":17047,"rows":154,"failures":154},"yuan2021lidarcameracalib-text-sec-iv-b","Checkerboard comparison; board edge inflation seen in the colored cloud",[17042],"authors' calibration scene with checkerboard",[23],[23],[17046],"Livox Avia point cloud of the checkerboard (sensor artefact from laser beam divergence seen in the Fig. 19 zoom; the text does not tie it to one calibration method)",[],{"slug":17049,"sourceId":17018,"sourceLabel":17019,"sourceYear":698,"table":1208,"note":17050,"datasets":17051,"metrics":17053,"families":17054,"methods":17055,"methodIds":17057,"rows":154,"failures":154},"yuan2021lidarcameracalib-text-sec-iv-c","Spinning LiDAR test: 20 random initial values within ±3° and ±5 cm of the CAD value",[17052],"authors' Ouster OS2-64 and industrial camera data",[1933],[1935],[17056],"proposed pipeline (Ouster OS2-64)",[],{"slug":17059,"sourceId":7515,"sourceLabel":17060,"sourceYear":306,"table":325,"note":17061,"datasets":17062,"metrics":17063,"families":17064,"methods":17065,"methodIds":17074,"rows":9253,"failures":154},"yuan2022voxelmap-table-ii","Yuan et al., 2022","KITTI odometry training sequences 00 to 10, LiDAR odometry with loop closure off for all methods; each cell printed as rotation [deg] \u002F translation [m…",[436],[329,23],[25,23],[17066,17067,17068,17069,17070,17071,17072,17073],"FAST-LIO2 [13]","Lego-Loam [7]","LiTAMIN2 [24]","MULLS [30]","Ours (full)","Ours (w\u002Fo adaptive)","Ours (w\u002Fo prob.)","Suma [14]",[321,395,597,432,7515],{"slug":17076,"sourceId":7515,"sourceLabel":17060,"sourceYear":306,"table":279,"note":17077,"datasets":17078,"metrics":17079,"families":17080,"methods":17081,"methodIds":17082,"rows":578,"failures":30},"yuan2022voxelmap-table-iii","Time consumption per LiDAR scan averaged over all KITTI training sequences; loop closure of MULLS, SuMa and LeGO-LOAM turned off; SuMa uses GPU, other…",[436],[38],[40],[17066,17067,17069,17070,17073],[321,395,597,432,7515],{"slug":17084,"sourceId":7515,"sourceLabel":17060,"sourceYear":306,"table":731,"note":17085,"datasets":17086,"metrics":17088,"families":17089,"methods":17090,"methodIds":17092,"rows":224,"failures":30},"yuan2022voxelmap-table-iv","Handheld Intel RealSense L515 sequences in laboratory and warehouse, routes start and end at the same place; constant-velocity prior; VoxelMap max vox…",[17087],"authors' L515 handheld datasets",[1551,38],[40,1553],[17070,17091],"SSL_SLAM [33]",[7515],{"slug":17094,"sourceId":7515,"sourceLabel":17060,"sourceYear":306,"table":818,"note":17095,"datasets":17096,"metrics":17098,"families":17099,"methods":17100,"methodIds":17102,"rows":608,"failures":30},"yuan2022voxelmap-table-v","Livox Avia with built-in IMU (10 Hz LiDAR, 200 Hz IMU), LiDAR-inertial mode; same parameters as KITTI (max voxel 3 m, 3 layers); all three methods sha…",[17097],"authors' Livox Avia datasets",[1551,38],[40,1553],[17066,17101,17070],"Faster-LIO [35]",[304,321,7515],{"slug":17104,"sourceId":7515,"sourceLabel":17060,"sourceYear":306,"table":1193,"note":17105,"datasets":17106,"metrics":17108,"families":17109,"methods":17110,"methodIds":17112,"rows":356,"failures":30},"yuan2022voxelmap-text-sec-iv-b","Both methods re-run on the dataset of the original SSL_SLAM paper (UGV, slow and stable motion); numbers stated in text only; the error metric is not…",[17107],"SSL_SLAM dataset [33]",[23,38],[40,23],[17091,17111],"our method",[7515],{"slug":17114,"sourceId":17115,"sourceLabel":17116,"sourceYear":374,"table":818,"note":17117,"datasets":17118,"metrics":17119,"families":17120,"methods":17121,"methodIds":17126,"rows":3207,"failures":63},"sdvloam2023-table-v","sdvloam2023","Yuan et al., 2023a","KITTI odometry; relative translational error (%) of LiDAR-assisted depth-enhanced visual odometry; baseline values from the original publications; LIM…",[469],[438],[441],[17122,4762,17123,17124,17125],"DEMO","Huang et al.","LIMO*","Our VO module",[17127,4960,17115],"demo2014",{"slug":17129,"sourceId":17115,"sourceLabel":17116,"sourceYear":374,"table":17130,"note":17131,"datasets":17132,"metrics":17133,"families":17134,"methods":17135,"methodIds":17144,"rows":17146,"failures":30},"sdvloam2023-table-vi-kitti-part","Table VI (KITTI part)","KITTI odometry; relative translational error (%) of visual-LiDAR odometry; '+' = open-source LiDAR odometry modified by the authors to use the SDV-LOA…",[469],[438],[441],[17136,17137,17138,17139,17140,17141,17142,17143],"A-LOAM+","CT-ICP+","Fast-LOAM+","ISC-LOAM+","LeGO-LOAM+","MULLS+","Ours (SDV-LOAM)","V-LOAM",[17115,17145],"vloam2015",86,{"slug":17148,"sourceId":17149,"sourceLabel":17150,"sourceYear":2267,"table":91,"note":17151,"datasets":17152,"metrics":17153,"families":17154,"methods":17155,"methodIds":17159,"rows":849,"failures":154},"srlivo2024-table-i","srlivo2024","Yuan et al., 2024","RMSE of ATE on NTU-VIRAL; baselines rerun from the authors' source code; 'x' = system drifted halfway through the run",[16003],[568],[25],[17156,17157,17158],"Fast-LIVO","Ours (SR-LIVO)","R3Live",[815,2387,17149],{"slug":17161,"sourceId":17149,"sourceLabel":17150,"sourceYear":2267,"table":325,"note":17162,"datasets":17163,"metrics":17165,"families":17166,"methods":17167,"methodIds":17168,"rows":2119,"failures":63},"srlivo2024-table-ii","Rendering quality of the colorized map projected onto each image, following NeRF-style evaluation; higher is better; 'x' = drifted halfway",[16003,17164],"R3Live dataset",[23],[23],[17157,17158],[2387,17149],{"slug":17170,"sourceId":17149,"sourceLabel":17150,"sourceYear":2267,"table":279,"note":17171,"datasets":17172,"metrics":17173,"families":17174,"methods":17175,"methodIds":17182,"rows":2607,"failures":63},"srlivo2024-table-iii","RMSE of ATE of LIO output versus LiDAR-assisted VIO output within each framework on NTU-VIRAL; 'x' = drifted halfway",[16003],[568],[25],[17176,17177,17178,17179,17180,17181],"Fast-LIVO (LIO)","Fast-LIVO(V) (LiDAR-assisted VIO)","Ours (SR-LIVO LIO)","Ours(V) (authors' R3Live-like LiDAR-assisted VIO module, ablation)","R3Live (LIO module)","R3Live(V) (LiDAR-assisted VIO module)",[815,2387,17149],{"slug":17184,"sourceId":17149,"sourceLabel":17150,"sourceYear":2267,"table":827,"note":17185,"datasets":17186,"metrics":17187,"families":17188,"methods":17189,"methodIds":17190,"rows":598,"failures":30},"srlivo2024-table-vi","Total time for handling a sweep as printed in Table VI (per-module Vision and LiDAR columns not extracted); the R3Live totals do not equal the sum of…",[16003,17164],[38],[40],[17157,17158],[2387,17149],{"slug":17192,"sourceId":17193,"sourceLabel":17194,"sourceYear":68,"table":69,"note":17195,"datasets":17196,"metrics":17197,"families":17198,"methods":17199,"methodIds":17202,"rows":52,"failures":30},"yuan2026-adaptive3dgsslam-table-1","yuan2026_adaptive3dgsslam","Yuan et al., 2026","Baseline selection: rendering results on Replica cited from the MonoGS paper [28] per the table caption; not an experiment of this paper",[3223],[23],[23],[5866,17200,17201],"NICE-SLAM [65]","Vox-Fusion [66]",[3183,3184],{"slug":17204,"sourceId":17193,"sourceLabel":17194,"sourceYear":68,"table":108,"note":17205,"datasets":17206,"metrics":17208,"families":17209,"methods":17210,"methodIds":17215,"rows":608,"failures":30},"yuan2026-adaptive3dgsslam-table-2","Change-ratio threshold sensitivity of the proposed method on ReplicaCAD; training-view metrics on update frames, test-view metrics on 100 separately g…",[17207],"ReplicaCAD (FRL apartment)",[568,23,38],[25,40,23],[17211,17212,17213,17214],"Proposed method, change ratio threshold >=1%","Proposed method, change ratio threshold >=2% (default)","Proposed method, change ratio threshold >=3.5%","Proposed method, change ratio threshold >=5%",[17193],{"slug":17217,"sourceId":17193,"sourceLabel":17194,"sourceYear":68,"table":17,"note":17218,"datasets":17219,"metrics":17220,"families":17221,"methods":17222,"methodIds":17228,"rows":608,"failures":30},"yuan2026-adaptive3dgsslam-table-3","ReplicaCAD comparison: proposed and MonoGS update the baseline model, SplaTAM, Photo-SLAM and GS-ICP-SLAM rebuild from the update sequence only; test…",[17207],[568,23,38],[25,40,23],[16427,17223,17224,17225,17226,17227],"Proposed method (RGB-only)","Rebuild with GS-ICP-SLAM","Rebuild with Photo-SLAM","Rebuild with SplaTAM","Update with MonoGS",[3183,5182,3186,17193],{"slug":17230,"sourceId":17193,"sourceLabel":17194,"sourceYear":68,"table":33,"note":17231,"datasets":17232,"metrics":17233,"families":17234,"methods":17235,"methodIds":17236,"rows":641,"failures":30},"yuan2026-adaptive3dgsslam-table-4","Per-step runtime on the ReplicaCAD update sequence (1000 frames); tracking and keyframe determination averaged over all frames, Gaussian edit and mapp…",[17207],[23,38],[40,23],[16427,17226,17227],[3183,3186,17193],{"slug":17238,"sourceId":17193,"sourceLabel":17194,"sourceYear":68,"table":244,"note":17239,"datasets":17240,"metrics":17242,"families":17243,"methods":17244,"methodIds":17248,"rows":52,"failures":30},"yuan2026-adaptive3dgsslam-table-5","Real-world lab case (Intel RealSense D435; 1225 baseline frames then 427 update frames); PSNR and SSIM are means over 300 random frames sampled from b…",[17241],"authors' real-world RGB-D dataset",[23,38],[40,23],[17245,17246,17247],"Adaptive 3DGS-SLAM","Baseline without updating (MonoGS initial model)","MonoGS updating pipeline",[3183,17193],{"slug":17250,"sourceId":17193,"sourceLabel":17194,"sourceYear":68,"table":5342,"note":17251,"datasets":17252,"metrics":17253,"families":17254,"methods":17255,"methodIds":17256,"rows":154,"failures":30},"yuan2026-adaptive3dgsslam-text-sec-4-4","Size of the final updated 3DGS model on ReplicaCAD; about 70% of primitives originate from the baseline model",[17207],[23],[23],[16427],[17193],{"slug":17258,"sourceId":17193,"sourceLabel":17194,"sourceYear":68,"table":13311,"note":17259,"datasets":17260,"metrics":17261,"families":17262,"methods":17263,"methodIds":17264,"rows":154,"failures":30},"yuan2026-adaptive3dgsslam-text-sec-5-4","Reduction in the number of processed frames relative to the MonoGS updating pipeline in the real-world case",[17241],[23],[23],[17245],[17193],{"slug":17266,"sourceId":17267,"sourceLabel":17268,"sourceYear":107,"table":69,"note":17269,"datasets":17270,"metrics":17272,"families":17273,"methods":17274,"methodIds":17276,"rows":608,"failures":30},"yun2018reflection-table-1","yun2018reflection","Yun & Sim, 2018","Processing time per LS3DPC model (single TLS scan) on Intel i7-4790K",[17271],"authors' outdoor LS3DPC models (RIEGL VZ-400)",[38],[40],[17275],"proposed algorithm",[],{"slug":17278,"sourceId":17279,"sourceLabel":17280,"sourceYear":698,"table":91,"note":17281,"datasets":17282,"metrics":17283,"families":17284,"methods":17285,"methodIds":17292,"rows":6820,"failures":608},"manhattanslam2021-table-i","manhattanslam2021","Yunus et al., 2021","Translation ATE RMSE (m); ORB-SLAM2 and SP-SLAM run without bundle adjustment and loop closure for fairness; 'x' tracking failure, '-' result not avai…",[2870,2872],[568],[25],[17286,17287,17288,17289,17290,17291],"L-SLAM [10]","ORB-SLAM2 [6] (BA and loop closure disabled)","Ours (ManhattanSLAM)","RGBD-SLAM [12] (Li et al. 2020)","S-SLAM [11]","SP-SLAM [5] (BA and loop closure disabled)",[17279,1511],{"slug":17294,"sourceId":17279,"sourceLabel":17280,"sourceYear":698,"table":325,"note":17295,"datasets":17296,"metrics":17298,"families":17299,"methods":17300,"methodIds":17302,"rows":120,"failures":30},"manhattanslam2021-table-ii","TAMU RGB-D long indoor loops without ground truth; drift = Euclidean distance between start and end of the estimated loop trajectory",[17297],"TAMU RGB-D",[1551],[1553],[17287,17288,17301],"Ours\u002F-MF (ablation: feature tracking only)",[17279,1511],{"slug":17304,"sourceId":17279,"sourceLabel":17280,"sourceYear":698,"table":279,"note":17305,"datasets":17306,"metrics":17307,"families":17308,"methods":17309,"methodIds":17313,"rows":29,"failures":30},"manhattanslam2021-table-iii","ICL-NUIM living room; reconstruction error of the point cloud generated from the surfels (cm); ElasticFusion and InfiniTAM need a GPU, DSM and Manhatt…",[2870],[75],[78],[17310,17311,17312,17288],"DSM [14] (Dense Surfel Mapping)","E-Fus [15] (ElasticFusion, IJRR version)","InfiniTAM [41] (InfiniTAM v3 report)",[15044,2915,17279],{"slug":17315,"sourceId":17279,"sourceLabel":17280,"sourceYear":698,"table":1994,"note":17316,"datasets":17317,"metrics":17319,"families":17320,"methods":17321,"methodIds":17324,"rows":274,"failures":154},"manhattanslam2021-text-sec-iv","Average timing over the experiments; dense mapping runs on a separate thread",[17318],"ICL-NUIM, TUM RGB-D and TAMU RGB-D",[148,38],[40],[17288,17322,17323],"Ours (ManhattanSLAM, dense mapping thread)","Ours (ManhattanSLAM, tracking)",[17279],{"slug":17326,"sourceId":17327,"sourceLabel":17328,"sourceYear":107,"table":279,"note":17329,"datasets":17330,"metrics":17331,"families":17332,"methods":17333,"methodIds":17339,"rows":578,"failures":30},"zhang2018trajeval-table-iii","zhang2018trajeval","Zhang & Scaramuzza, 2018","VINS-Mono on EuRoC MH01; ATE of the whole trajectory after yaw-only rigid-body (4-DoF) alignment computed from the first Q states",[1482],[568,23],[25,23],[17334,17335,17336,17337,17338],"VINS-Mono (states used: 1 - 1355)","VINS-Mono (states used: 1 - 1807, all)","VINS-Mono (states used: 1 - 452)","VINS-Mono (states used: 1 - 904)","VINS-Mono (states used: 1)",[251],{"slug":17341,"sourceId":2118,"sourceLabel":17342,"sourceYear":921,"table":91,"note":17343,"datasets":17344,"metrics":17346,"families":17347,"methods":17348,"methodIds":17349,"rows":356,"failures":30},"loam2014-table-i","Zhang & Singh, 2014","Relative drift as % of distance; indoor corridor loop, drift = gap between start and finish",[17345],"Own datasets",[1551,23],[1553,23],[461],[2118],{"slug":17351,"sourceId":2118,"sourceLabel":17342,"sourceYear":921,"table":325,"note":17352,"datasets":17353,"metrics":17355,"families":17356,"methods":17357,"methodIds":17360,"rows":224,"failures":30},"loam2014-table-ii","Handheld lidar walked at 0.5 m\u002Fs with about 0.5 m up-down motion; ground truth measured by tape ruler; error as % of distance",[17354],"Own handheld datasets",[23],[23],[17358,81,17359],"IMU (IMU orientation, translation by the method)","Ours+IMU",[2118],{"slug":17362,"sourceId":2118,"sourceLabel":17342,"sourceYear":921,"table":15659,"note":17363,"datasets":17364,"metrics":17365,"families":17366,"methods":17367,"methodIds":17369,"rows":154,"failures":154},"loam2014-text-sec-vii-c","KITTI odometry benchmark server result over 100 m to 800 m segments (39.2 km total); per-sequence values not in the paper",[469],[438],[441],[17368],"LOAM (lidar only)",[2118],{"slug":17371,"sourceId":17145,"sourceLabel":17372,"sourceYear":681,"table":91,"note":17373,"datasets":17374,"metrics":17376,"families":17377,"methods":17378,"methodIds":17383,"rows":29,"failures":30},"vloam2015-table-i","Zhang & Singh, 2015","Relative position error as a fraction of distance travelled, based on 3D coordinates; no independent reference instrument",[17375],"authors' custom camera-lidar sensor data",[1551],[1553],[17379,17380,17381,17382],"F-V (fisheye camera, visual odometry only)","F-VL (fisheye camera, V-LOAM visual plus lidar odometry)","W-V (wide-angle camera, visual odometry only)","W-VL (wide-angle camera, V-LOAM visual plus lidar odometry)",[17145],{"slug":17385,"sourceId":17145,"sourceLabel":17372,"sourceYear":681,"table":325,"note":17386,"datasets":17387,"metrics":17388,"families":17389,"methods":17390,"methodIds":17395,"rows":618,"failures":63},"vloam2015-table-ii","Relative position errors in fast motion tests; 'Failed' = visual features lost tracking during fast turns",[17375],[1551],[1553],[17391,17392,17393,17394],"V-LOAM Fi-Fa (fisheye camera, fast motion)","V-LOAM Fi-S (fisheye camera, slow motion)","V-LOAM W-Fa (wide-angle camera, fast motion)","V-LOAM W-S (wide-angle camera, slow motion)",[17145],{"slug":17397,"sourceId":17145,"sourceLabel":17372,"sourceYear":681,"table":17398,"note":17399,"datasets":17400,"metrics":17402,"families":17403,"methods":17404,"methodIds":17405,"rows":154,"failures":30},"vloam2015-text-sec-viii","Text Sec. VIII","KITTI odometry benchmark result as stated by the authors (ranked first at the time); no per-sequence table in the paper",[17401],"KITTI odometry benchmark",[438],[441],[17143],[17145],{"slug":17407,"sourceId":450,"sourceLabel":17408,"sourceYear":345,"table":69,"note":17409,"datasets":17410,"metrics":17412,"families":17413,"methods":17414,"methodIds":17417,"rows":618,"failures":30},"loam2017-auro-table-1","Zhang & Singh, 2017","Computation break-down per program call in the Hokuyo accuracy tests; odometry runs about 10 Hz and mapping once per sweep",[17411],"author-collected Hokuyo datasets",[23,38],[40,23],[17415,17416],"LOAM mapping program","LOAM odometry program",[450],{"slug":17419,"sourceId":450,"sourceLabel":17408,"sourceYear":345,"table":108,"note":17420,"datasets":17421,"metrics":17422,"families":17423,"methods":17424,"methodIds":17426,"rows":356,"failures":30},"loam2017-auro-table-2","Relative drift of motion estimation; corridor from start and end gap of a closed loop, orchard against GPS\u002FINS; lidar speed 0.5 m\u002Fs",[17411],[1551],[1553],[17425],"LOAM (Hokuyo 2-axis lidar)",[450],{"slug":17428,"sourceId":450,"sourceLabel":17408,"sourceYear":345,"table":17,"note":17429,"datasets":17430,"metrics":17432,"families":17433,"methods":17434,"methodIds":17438,"rows":224,"failures":30},"loam2017-auro-table-3","Motion estimation errors with and without IMU; handheld lidar, 0.5 m\u002Fs walking, ground truth by tape ruler",[17431],"author-collected handheld Hokuyo datasets",[1551],[1553],[17435,17436,17437],"IMU (orientation from IMU only, translation from the method)","Ours (no IMU)","Ours+IMU (IMU pre-processing followed by the method)",[450],{"slug":17440,"sourceId":450,"sourceLabel":17408,"sourceYear":345,"table":33,"note":17441,"datasets":17442,"metrics":17444,"families":17445,"methods":17446,"methodIds":17447,"rows":618,"failures":30},"loam2017-auro-table-4","Computation break-down per program call in the Velodyne HDL-32E tests; odometry at 10 Hz, mapping stacks 1 s of scans",[17443],"author-collected Velodyne HDL-32E datasets",[23,38],[40,23],[17415,17416],[450],{"slug":17449,"sourceId":450,"sourceLabel":17408,"sourceYear":345,"table":244,"note":17450,"datasets":17451,"metrics":17452,"families":17453,"methods":17454,"methodIds":17455,"rows":2420,"failures":30},"loam2017-auro-table-5","KITTI odometry training sequences with GPS\u002FINS ground truth; mean relative position error over 100-800 m segments in 3D; mapping run on every scan",[469],[438],[441],[461],[450],{"slug":17457,"sourceId":450,"sourceLabel":17408,"sourceYear":345,"table":8981,"note":17458,"datasets":17459,"metrics":17461,"families":17462,"methods":17463,"methodIds":17465,"rows":356,"failures":30},"loam2017-auro-text-sec-7-4","HDL-32E vehicle runs judged by matching trajectory and laser points to a satellite image; values are upper bounds",[17460],"author-collected HDL-32E",[1551,23],[1553,23],[17464],"LOAM (HDL-32E)",[450],{"slug":17467,"sourceId":450,"sourceLabel":17408,"sourceYear":345,"table":17468,"note":17469,"datasets":17470,"metrics":17471,"families":17472,"methods":17473,"methodIds":17475,"rows":63,"failures":30},"loam2017-auro-text-sec-7-5","Text Sec. 7.5","KITTI odometry benchmark ranking at time of writing",[469,17401],[438,38],[40,441],[461,17474],"LOAM (mapping every scan)",[450],{"slug":17477,"sourceId":17478,"sourceLabel":17479,"sourceYear":107,"table":69,"note":17480,"datasets":17481,"metrics":17483,"families":17484,"methods":17485,"methodIds":17490,"rows":618,"failures":30},"zhang2018lvio-table-1","zhang2018lvio","Zhang & Singh, 2018","Average CPU time of K-D tree operations for map voxel configurations, averaged over datasets from confined, open, structured and vegetated areas",[17482],"authors' data (multiple environments)",[23],[23],[17486,17487,17488,17489],"Scan matching map: One-level voxels, K-D trees for all voxels","Scan matching map: One-level voxels, K-D trees for each voxel","Scan matching map: Two-level voxels, K-D trees for all voxels","Scan matching map: Two-level voxels, K-D trees for each voxel (adopted)",[17478],{"slug":17492,"sourceId":17478,"sourceLabel":17479,"sourceYear":107,"table":108,"note":17493,"datasets":17494,"metrics":17496,"families":17497,"methods":17498,"methodIds":17502,"rows":224,"failures":30},"zhang2018lvio-table-2","Average CPU processing time with the two Velodyne sensor suites",[17495],"authors' data",[38],[40],[17499,17500,17501],"Scan matching","Visual-inertial odometry, CPU feature tracking","Visual-inertial odometry, GPU feature tracking",[17478],{"slug":17504,"sourceId":17478,"sourceLabel":17479,"sourceYear":107,"table":17,"note":17505,"datasets":17506,"metrics":17508,"families":17509,"methods":17510,"methodIds":17515,"rows":618,"failures":30},"zhang2018lvio-table-3","Relative position error at the end of Accuracy Test 2, pipeline configurations compared at original and doubled data speed",[17507],"authors' data, Accuracy Test 2 (sensor suite Fig. 16a on passenger vehicle)",[1551],[1553],[17511,17512,17513,17514],"Complete pipeline","IMU + scan matching","One-step optimization (all constraints in one factor-graph-style problem at 5 Hz)","Visual-inertial odometry",[17478],{"slug":17517,"sourceId":17478,"sourceLabel":17479,"sourceYear":107,"table":33,"note":17518,"datasets":17519,"metrics":17521,"families":17522,"methods":17523,"methodIds":17526,"rows":274,"failures":30},"zhang2018lvio-table-4","Relative position error on Accuracy Test 2 compared with the authors' earlier LOAM (IMU plus lidar) and V-LOAM (camera plus lidar)",[17520],"authors' data, Accuracy Test 2",[1551],[1553],[17511,17524,17525],"LOAM (Zhang & Singh, 2014)","V-LOAM (Zhang & Singh, 2015)",[2118,17145,17478],{"slug":17528,"sourceId":17478,"sourceLabel":17479,"sourceYear":107,"table":244,"note":17529,"datasets":17530,"metrics":17532,"families":17533,"methods":17534,"methodIds":17536,"rows":120,"failures":30},"zhang2018lvio-table-5","Average CPU processing time on the custom handheld Contour device",[17531],"authors' data (Contour)",[38],[40],[17535,17500,17501],"Scan matching (1 Hz on Contour)",[17478],{"slug":17538,"sourceId":17478,"sourceLabel":17479,"sourceYear":107,"table":17539,"note":17540,"datasets":17541,"metrics":17543,"families":17544,"methods":17545,"methodIds":17546,"rows":154,"failures":154},"zhang2018lvio-text-sec-10-1-1","Text Sec. 10.1.1","Upper bound: horizontal error \u003C 1.0 m from satellite-image overlay, vertical \u003C 2.0 m from building floors",[17542],"authors' data, Accuracy Test 1 (sensor suite Fig. 16a on utility vehicle)",[1551],[1553],[17511],[17478],{"slug":17548,"sourceId":17478,"sourceLabel":17479,"sourceYear":107,"table":17549,"note":17550,"datasets":17551,"metrics":17553,"families":17554,"methods":17555,"methodIds":17556,"rows":154,"failures":154},"zhang2018lvio-text-sec-10-1-3","Text Sec. 10.1.3","Upper bound on horizontal drift from satellite-image overlay; vertical drift not evaluable",[17552],"authors' data, Aggressive Motion Test 3 (sensor suite Fig. 16b on passenger vehicle)",[1551],[1553],[17511],[17478],{"slug":17558,"sourceId":17478,"sourceLabel":17479,"sourceYear":107,"table":17559,"note":17560,"datasets":17561,"metrics":17564,"families":17565,"methods":17566,"methodIds":17568,"rows":63,"failures":154},"zhang2018lvio-text-sec-10-3","Text Sec. 10.3","Localization of a winter run on a summer map (and the reverse), error estimated by inspecting the merged map",[17562,17563],"authors' data, air-ground test (DJI S1000 drone)","authors' data, forest localization test (sensor suite Fig. 16b, handheld)",[23,38],[40,23],[17567],"Localization on existing map",[17478],{"slug":17570,"sourceId":17127,"sourceLabel":17571,"sourceYear":921,"table":91,"note":17572,"datasets":17573,"metrics":17575,"families":17576,"methods":17577,"methodIds":17582,"rows":29,"failures":30},"demo2014-table-i","Zhang et al., 2014","Author-collected tests with the Xtion RGB-D camera and with the custom camera plus rotating Hokuyo LiDAR; the camera starts and stops at the same posi…",[17574],"DEMO author-collected tests",[1551],[1553],[17578,17579,17580,17581],"DVO","Fovis","Our VO (Lidar)","Our VO (RGB-D)",[17127,6025],{"slug":17584,"sourceId":17127,"sourceLabel":17571,"sourceYear":921,"table":325,"note":17585,"datasets":17586,"metrics":17587,"families":17588,"methods":17589,"methodIds":17591,"rows":2420,"failures":30},"demo2014-table-ii","KITTI odometry training sequences 00-10; left monochrome camera plus Velodyne; mean relative position error as a percentage (KITTI protocol)",[469],[438],[441],[17590],"DEMO (camera + LiDAR)",[17127],{"slug":17593,"sourceId":5431,"sourceLabel":17594,"sourceYear":4828,"table":539,"note":17595,"datasets":17596,"metrics":17598,"families":17599,"methods":17600,"methodIds":17604,"rows":274,"failures":63},"zhang2016degeneracy-text-sec-vi","Zhang et al., 2016","Test 3, handheld camera and lidar pack, 538 m path that returns to the exact start; end position error relative to distance traveled; values stated in…",[17597],"authors' own data (Test 3)",[1551],[1553],[17601,17602,17603],"Const Motion Prior (consistent motion prior added to all modules)","W\u002F Remapping (proposed solution remapping)","W\u002FO Remapping (host vision-lidar system of ref. [19] without solution remapping)",[17145,5431],{"slug":17606,"sourceId":17607,"sourceLabel":17608,"sourceYear":374,"table":91,"note":17609,"datasets":17610,"metrics":17613,"families":17614,"methods":17615,"methodIds":17621,"rows":2119,"failures":30},"dynbench2023-table-i","dynbench2023","Zhang et al., 2023a","Point-wise dynamic point removal accuracy (%): SA static accuracy, DA dynamic accuracy, AA = sqrt(SA x DA); methods marked * are offline and need a pr…",[17611,3541,17612],"Argoverse 2.0","Semi-indoor (authors' custom)",[23],[23],[17616,17617,17618,17619,17620],"ERASOR* [16]","Octomap [8]","Octomap w G","Octomap w GF","Removert* [5]",[17607,3526,3527,3528],{"slug":17623,"sourceId":17607,"sourceLabel":17608,"sourceYear":374,"table":325,"note":17624,"datasets":17625,"metrics":17626,"families":17627,"methods":17628,"methodIds":17629,"rows":578,"failures":30},"dynbench2023-table-ii","Runtime per frame (s, mean plus or minus std) and number of parameters to tune; desktop 12th Gen Intel Core i9-12900KF (24 cores); the dataset used fo…",[2070],[23,38],[40,23],[17616,17617,17618,17619,17620],[17607,3526,3527,3528],{"slug":17631,"sourceId":17632,"sourceLabel":17633,"sourceYear":374,"table":17,"note":17634,"datasets":17635,"metrics":17636,"families":17637,"methods":17638,"methodIds":17648,"rows":4017,"failures":30},"goslam2023-table-3","goslam2023","Zhang et al., 2023b","ATE RMSE on 8 ScanNet scenes, RGB-D and monocular input; iMAP and NICE-SLAM values copied from NICE-SLAM; DROID-SLAM (VO) is DROID-SLAM without final…",[2963],[568],[25],[17639,17640,17641,17642,17643,17644,17645,17646,17647],"DROID-SLAM [ 41 ] (RGB-D)","DROID-SLAM [ 41 ] (VO) (RGB-D)","DROID-SLAM [ 41 ] (VO) (monocular)","DROID-SLAM [ 41 ] (monocular)","NICE-SLAM [ 53 ] (RGB-D)","ORB-SLAM3 [ 6 ] (monocular)","Ours (RGB-D)","Ours (monocular)","iMAP ∗ [ 35 ] (RGB-D)",[6178,6378,3184,763],{"slug":17650,"sourceId":17632,"sourceLabel":17633,"sourceYear":374,"table":33,"note":17651,"datasets":17652,"metrics":17653,"families":17654,"methods":17655,"methodIds":17660,"rows":2607,"failures":641},"goslam2023-table-4","Replica, average over 8 scenes; RGB-D group (iMAP*, NICE-SLAM, GO-SLAM) and monocular group; values for other methods copied from their papers; '-' me…",[3223],[568,148,75,76,23],[25,40,78,23],[17642,17656,17643,17657,17658,17659,17645,17646,17647],"Li et al. [ 20 ] (monocular)","NICER-SLAM [ 52 ] (concurrent, unpublished at the time) (monocular)","NeRF-SLAM [ 31 ] (concurrent, unpublished at the time) (monocular)","Orbeez-SLAM [ 9 ] (monocular)",[6178,6378,3184],{"slug":17662,"sourceId":17632,"sourceLabel":17633,"sourceYear":374,"table":644,"note":17663,"datasets":17664,"metrics":17665,"families":17666,"methods":17667,"methodIds":17670,"rows":618,"failures":154},"goslam2023-table-9","Hardware requirements on Replica with RGB-D input, all run on NVIDIA RTX 3090",[3223],[148,219],[40],[17668,17669,81,16341],"DROID-SLAM [ 41 ]","NICE-SLAM [ 53 ]",[6178,6378,3184],{"slug":17672,"sourceId":17673,"sourceLabel":17674,"sourceYear":374,"table":17675,"note":17676,"datasets":17677,"metrics":17679,"families":17680,"methods":17681,"methodIds":17685,"rows":608,"failures":30},"zhang2023hiltioxford-fig-9-printed-table","zhang2023hiltioxford","Zhang et al., 2023c","Fig. 9 (printed table)","Per-sequence RMSE ATE (cm) of the top three teams, printed as numbers inside Fig. 9; bold marks sub-cm results",[17678],"Hilti-Oxford (Hilti SLAM Challenge 2022)",[568],[25],[17682,17683,17684],"CSIRO (Wildcat SLAM)","HKU (FastLIO2, BALM)","Vision&Robotics (MC2SLAM)",[799],{"slug":17687,"sourceId":17673,"sourceLabel":17674,"sourceYear":374,"table":325,"note":17688,"datasets":17689,"metrics":17690,"families":17691,"methods":17692,"methodIds":17717,"rows":608,"failures":63},"zhang2023hiltioxford-table-ii","Hilti SLAM Challenge 2022, 8 challenge sequences; ATE = mean over sequences of RMSE ATE (cm) against sparse millimeter control points; teams used thei…",[17678],[568,23],[25,23],[17693,17694,17695,17696,17697,17698,17699,17700,17701,17702,17703,17704,17705,17706,17707,17708,17709,17710,17711,17712,17713,17714,17715,17716],"AIST: VITAMIN-E [24], [25]","AIST: VITAMIN-E [24], [25] (LiDAR+IMU+3 cameras; SW Opt., real-time; no global BA; causal; no LC)","Beihang Uni.: based on [18] FAST-LIO2 and [21] VINS-Mono (LiDAR+IMU+2 cameras; filter, real-time; no global BA; causal; no LC)","Beihang Uni.: based on [18], [21]","CSIRO: Wildcat SLAM [15]","CSIRO: Wildcat SLAM [15] (LiDAR+IMU; SW Opt., real-time odometry; global BA yes; causal no; LC yes; same params yes)","HKU: FastLIO2 [18], BALM [19]","HKU: FastLIO2 [18], BALM [19] (LiDAR+IMU; filter, real-time; global BA yes; causal no information; LC yes)","HKUST & Georgia Tech: based on [18] FAST-LIO2 and [26] Bayesian ICP (LiDAR+IMU+5 cameras; filter, real-time; global BA yes; causal no; LC yes)","HKUST & Georgia Tech: based on [18], [26]","KAIST: based on [18] FAST-LIO2 and [20] Bayesian ICP (LiDAR+IMU; filter; global BA yes; causal no; LC yes)","KAIST: based on [18], [20]","KTH & NTU: VIRAL SLAM [27]","KTH & NTU: VIRAL SLAM [27] (LiDAR+IMU; SW Opt., not real-time; no global BA; causal; no LC)","Luxembourg Uni.: based on [18] FAST-LIO2 and [22] OpenVINS (LiDAR+IMU+1 camera; filter, real-time; no global BA; causal; no LC)","Luxembourg Uni.: based on [18], [22]","MINES ParisTech: CT-ICP [23]","MINES ParisTech: CT-ICP [23] (LiDAR+IMU; Opt., real-time; no global BA; causal; LC yes)","Stuttgart Uni. & TUM: based on [28]","Stuttgart Uni. & TUM: based on [28] DROID-SLAM (IMU+4 cameras; SW Opt., not real-time; no global BA; causal; no LC)","TUM (text: Smart Robotics Lab): OKVIS2.0 [16] (IMU+5 cameras; SW Opt., real-time; global BA yes; causal no; LC yes)","TUM: OKVIS2.0 [16]","Vision & Robotics: MC2SLAM [17]","Vision & Robotics: MC2SLAM [17] (LiDAR+IMU; SW Opt., real-time; global BA yes; causal no; LC yes; same params yes)",[596,799],{"slug":17719,"sourceId":17720,"sourceLabel":17721,"sourceYear":2267,"table":633,"note":17722,"datasets":17723,"metrics":17724,"families":17725,"methods":17726,"methodIds":17784,"rows":6147,"failures":30},"zhang2024-3dlidarslam-survey-table-8","zhang2024_3dlidarslam_survey","Zhang et al., 2024a","Compiled by the survey from original papers (not re-run). KITTI odometry relative translational error; 00-10 mean column (mean over the sequences a me…",[469],[438],[441],[17727,17728,17729,17730,17731,17732,17733,17734,17735,17736,17737,17738,17739,17740,17741,17742,17743,17744,17745,17746,17747,17748,17749,17750,17751,17752,17753,17754,17755,17756,17757,17758,17759,17760,17761,17762,17763,17764,17765,17766,17767,17768,17769,17770,17771,17772,17773,17774,17775,17776,17777,17778,17779,17780,17781,17782,17783],"Aydemir et al. (Aydemir et al., 2022)","CLS (Velas et al., 2016)","CR-LDSO (Yuan, Cheng, & Yang, 2023)","CT-ICP (Dellenbach et al., 2022) [LP]","Cho et al. (Cho et al., 2020)","DEMO (Zhang et al., 2017)","DMLO (Li & Wang, 2020)","DMLO+Mapping (Li & Wang, 2020)","DV-LOAM (Wang, Liu, Wang, et al., 2021) [LP]","DVL-SLAM (Shin et al., 2020) [LP]","DeLORA (Nubert et al., 2021)","E-LOAM (Guo et al., 2023) [LP]","ELO (Zheng & Zhu, 2021)","F-LOAM (Wang, Wang, Chen, & Xie, 2021)","FALO (García Daza et al., 2020)","GICP (Segal et al., 2009)","GLIM (Koide et al., 2021a) [LP]","Generalized LOAM (Honda et al., 2022)","HDL-Graph-SLAM (Koide et al., 2019) [LP]","HPPLO-Net (Zhou et al., 2023)","Huang et al. (Huang et al., 2020) [LP]","ICP-po2pl (Besl & McKay, 1992)","ICP-po2po (Besl & McKay, 1992)","IMLS-SLAM (Deschaud, 2018)","ISC-LOAM (Wang, Wang, & Xie, 2021) [LP]","LO-Net + Mapping (Li et al., 2019)","LOAM (Ji & Singh, 2017)","LeGO-LOAM (Shan & Englot, 2018) [LP]","LiTAMIN (Yokozuka et al., 2020)","LiTAMIN (Yokozuka et al., 2020) [LP]","LiTAMIN2 (Yokozuka et al., 2021)","LiTAMIN2 (Yokozuka et al., 2021) [LP]","Lo-Net (Li et al., 2019)","LoDoNet (Zheng et al., 2020)","MULLS-LO (Pan et al., 2021)","MULLS-SLAM (Pan et al., 2021) [LP]","NDT-LOAM (Chen, Ma, et al., 2022)","PSF-LO (Chen, Wang, et al., 2021)","PSF-LO no-o (Chen, Wang, et al., 2021)","PWCLO-Net (Wang, Wu, Liu, & Wang, 2021)","RGB-L (Sauerbeck et al., 2023)","RSLO (Xu, Lin, et al., 2022)","S4-SLAM (Zhou, He, et al., 2021) [LP]","SA-LOAM-LOOP (Li, Kong, Zhao, Li, et al., 2021) [LP]","SA-LOAM-ODOM (Li, Kong, Zhao, Li, et al., 2021)","SDV-LOAM (Yuan, Wang, et al., 2023)","SelfVoxeLO (Xu et al., 2021)","SuMa (scan-model) (Behley & Stachniss, 2018)","SuMa (scan-model) (Behley & Stachniss, 2018) [LP]","SuMa (scan-scan) (Behley & Stachniss, 2018)","SuMa++ (Chen et al., 2019) [LP]","T-LOAM (Zhou, Guo, et al., 2022)","TVL-SLAM (Chou & Chou, 2022) [LP]","TVLO (Seo & Chou, 2019) [LP]","TransLO (Liu et al., 2023)","Velas et al. (Velas et al., 2018)","Vizzo et al. (Vizzo et al., 2021)",[478,1944,596,393,394,395,450,3309,597,3291,3292,432,1968,3264],{"slug":17786,"sourceId":17720,"sourceLabel":17721,"sourceYear":2267,"table":644,"note":17787,"datasets":17788,"metrics":17789,"families":17790,"methods":17791,"methodIds":17793,"rows":17794,"failures":30},"zhang2024-3dlidarslam-survey-table-9","Compiled by the survey from original papers (not re-run). KITTI odometry relative rotational error; 00-10 mean column; caption prints unit as %\u002F100 m…",[469],[439],[441],[17728,17731,17733,17734,17735,17737,17738,17739,17740,17741,17742,17743,17792,17745,17746,17748,17749,17751,17752,17753,17754,17755,17756,17757,17758,17759,17760,17761,17762,17766,17767,17768,17770,17771,17773,17774,17775,17776,17777,17778,17779,17781,17783],"H-VLO (Aydemir et al., 2022)",[478,1944,393,394,395,450,3309,597,3291,3292,432,1968,3264],43,{"slug":17796,"sourceId":17797,"sourceLabel":17798,"sourceYear":2267,"table":69,"note":17799,"datasets":17800,"metrics":17801,"families":17802,"methods":17803,"methodIds":17814,"rows":3766,"failures":30},"zhang2024globalbimreg-table-1","zhang2024globalbimreg","Zhang et al., 2024b","Coarse registration on 250 samples (50 per model) with random rigid perturbations (roll and pitch within 30 deg, yaw within 180 deg, translation withi…",[6873],[23],[23],[17804,8947,17805,17806,17807,17808,17809,17810,17811,17812,17813],"DCP","FilterReg (initialised with FPFH-RANSAC)","GMMTree (initialised with FPFH-RANSAC)","GO-ICP","Ours (primitive-level coarse registration)","PLADE","PointNetLK","RANSAC (FPFH features)","RMMG","Super4PCS",[4183,16489,17797,8957],{"slug":17816,"sourceId":17797,"sourceLabel":17798,"sourceYear":2267,"table":108,"note":17817,"datasets":17818,"metrics":17819,"families":17820,"methods":17821,"methodIds":17824,"rows":102,"failures":30},"zhang2024globalbimreg-table-2","Fine registration after coarse alignment on the simulation samples; errors against the benchmark alignment",[6873],[23],[23],[17822,9972,17823],"ICP (point-to-plane)","Ours (point-level fine registration with BIM-point association)",[478,1944,17797],{"slug":17826,"sourceId":17797,"sourceLabel":17798,"sourceYear":2267,"table":17,"note":17827,"datasets":17828,"metrics":17829,"families":17830,"methods":17831,"methodIds":17834,"rows":429,"failures":30},"zhang2024globalbimreg-table-3","Sensitivity of the proposed method: point clouds voxel-downsampled at different sizes, and partial clouds simulating temporal construction stages (Cas…",[6873],[23],[23],[17832,17833],"Ours (coarse registration)","Ours (fine registration)",[17797],{"slug":17836,"sourceId":17797,"sourceLabel":17798,"sourceYear":2267,"table":33,"note":17837,"datasets":17838,"metrics":17840,"families":17841,"methods":17842,"methodIds":17843,"rows":2252,"failures":30},"zhang2024globalbimreg-table-4","Jockey Club One Health Tower construction site, floors 06-12; handheld Ouster OS0-128 clouds reconstructed per floor with FAST-LIO2 and registered to…",[17839],"Jockey Club One Health Tower site data (self-collected)",[23],[23],[17832,17833],[17797],{"slug":17845,"sourceId":17797,"sourceLabel":17798,"sourceYear":2267,"table":17846,"note":17847,"datasets":17848,"metrics":17849,"families":17850,"methods":17851,"methodIds":17852,"rows":274,"failures":30},"zhang2024globalbimreg-text-sec-4-1-2","Text Sec.4.1.2","Average coarse registration computation time over the simulation samples (text accompanying Fig. 11)",[6873],[23],[23],[17832,17809,17811],[4183,17797],{"slug":17854,"sourceId":17855,"sourceLabel":17856,"sourceYear":562,"table":91,"note":17857,"datasets":17858,"metrics":17859,"families":17860,"methods":17861,"methodIds":17872,"rows":2420,"failures":30},"hislam2-2025-table-i","hislam2_2025","Zhang et al., 2025","Replica camera tracking ATE [cm], average of 8 scenes (per-scene values not extracted); RGB-D and RGB groups; DROID-SLAM with global BA enabled",[3223],[329],[25],[17862,17863,17864,17865,17866,17867,17868,81,17869,17870,17871],"DROID-SLAM [ 20 ]","ESLAM [ 67 ]","GLORIE-SLAM [ 59 ]","MGS-SLAM [ 69 ]","MonoGS [ 32 ]","NICE-SLAM [ 29 ]","NICER-SLAM [ 58 ]","Point-SLAM [ 68 ]","Splat-SLAM [ 31 ]","SplatTAM [ 31 ]",[6178,3182,3183,3184,3185,3186],{"slug":17874,"sourceId":17855,"sourceLabel":17856,"sourceYear":562,"table":325,"note":17875,"datasets":17876,"metrics":17877,"families":17878,"methods":17879,"methodIds":17884,"rows":578,"failures":30},"hislam2-2025-table-ii","ScanNet camera tracking ATE [cm], average of 8 scenes (per-scene values not extracted)",[2963],[329],[25],[17880,17863,17864,17881,17882,17883,17867,81,17869,17870],"Co-SLAM [ 35 ]","GO-SLAM [ 71 ]","HI-SLAM [ 36 ]","LoopSplat [ 70 ]",[3181,3182,3184,3185],{"slug":17886,"sourceId":17855,"sourceLabel":17856,"sourceYear":562,"table":279,"note":17887,"datasets":17888,"metrics":17890,"families":17891,"methods":17892,"methodIds":17895,"rows":120,"failures":30},"hislam2-2025-table-iii","Waymo Open front camera, averaged over 9 sequences following the OpenGS-SLAM protocol",[17889],"Waymo Open",[329],[25],[17864,17866,17868,17893,81,17894],"OpenGS-SLAM [ 66 ]","Photo-SLAM [ 72 ]",[3183,5182],{"slug":17897,"sourceId":17855,"sourceLabel":17856,"sourceYear":562,"table":731,"note":17898,"datasets":17899,"metrics":17900,"families":17901,"methods":17902,"methodIds":17909,"rows":7879,"failures":30},"hislam2-2025-table-iv","Replica mesh reconstruction for RGB-only methods (meshes from TSDF fusion of rendered depth for HI-SLAM2); accuracy and completeness in cm, completion…",[3223],[75,76,23],[78,23],[17903,17904,17905,17906,17907,17908],"GLORIE-SLAM [59] (3DGS-based, RGB)","GO-SLAM [71] (NeRF-based, RGB)","HI-SLAM [36] (NeRF-based, RGB)","NICER-SLAM [58] (NeRF-based, RGB)","Ours (HI-SLAM2)","Splat-SLAM [33] (3DGS-based, RGB)",[],{"slug":17911,"sourceId":17855,"sourceLabel":17856,"sourceYear":562,"table":17912,"note":17913,"datasets":17914,"metrics":17915,"families":17916,"methods":17917,"methodIds":17918,"rows":63,"failures":30},"hislam2-2025-text-sec-iv-h","Text Sec. IV-H","Online tracking, loop closing and mapping frame rate on Replica",[3223],[148,23],[40,23],[17907],[],{"slug":17920,"sourceId":17855,"sourceLabel":17856,"sourceYear":562,"table":17921,"note":17922,"datasets":17923,"metrics":17925,"families":17926,"methods":17927,"methodIds":17929,"rows":356,"failures":30},"hislam2-2025-text-sec-iv-i","Text Sec. IV-I","Self-collected factory-hall robot sequence, 4073 frames (6 min 52 s), left camera only; stage runtimes converted from minutes and seconds",[17924],"self-collected factory hall",[23],[23],[17928,17907],"DROID-SLAM + 3DGS (baseline)",[],{"slug":17931,"sourceId":17932,"sourceLabel":17933,"sourceYear":68,"table":108,"note":17934,"datasets":17935,"metrics":17937,"families":17938,"methods":17939,"methodIds":17942,"rows":578,"failures":30},"bimloc2026-table-2","bimloc2026","Zhang et al., 2026","Simulation (Gazebo, synthetic VLP-16), CityU-02 with curtain walls removed; structure-level discrepancy detection",[17936],"BIM-robot simulation benchmark (CityU-02)",[23],[23],[17940,17941],"BIM-Loc","BIM-Reg",[17932,17797],{"slug":17944,"sourceId":17932,"sourceLabel":17933,"sourceYear":68,"table":17,"note":17945,"datasets":17946,"metrics":17948,"families":17949,"methods":17950,"methodIds":17953,"rows":2607,"failures":30},"bimloc2026-table-3","HKUST office (SLABIM F03-F05), handheld Livox Mid-360; GT trajectories from SLABIM; baselines use points sampled from the same BIM",[17947],"SLABIM (HKUST office benchmark)",[568,23],[25,23],[17940,17951,17952],"Fast-Loc","PALoc",[17932],{"slug":17955,"sourceId":17932,"sourceLabel":17933,"sourceYear":68,"table":33,"note":17956,"datasets":17957,"metrics":17958,"families":17959,"methods":17960,"methodIds":17961,"rows":849,"failures":30},"bimloc2026-table-4","HKUST office; per-scan point-to-BIM distance RMSE, distances \u003C 0.2 m only; reference is the prior BIM itself",[17947],[23],[23],[17940,17951,17952],[17932],{"slug":17963,"sourceId":17932,"sourceLabel":17933,"sourceYear":68,"table":244,"note":17964,"datasets":17965,"metrics":17967,"families":17968,"methods":17969,"methodIds":17971,"rows":3547,"failures":356},"bimloc2026-table-5","CityU active construction site, handheld LiDAR; no GT trajectories; scan-to-BIM RMSE (\u003C 0.2 m) against the prior BIM",[17966],"CityU construction benchmark",[23],[23],[17940,17970,17951,13691,17952],"CAD-Mesher",[17932,13684],{"slug":17973,"sourceId":17932,"sourceLabel":17933,"sourceYear":68,"table":633,"note":17974,"datasets":17975,"metrics":17977,"families":17978,"methods":17979,"methodIds":17982,"rows":618,"failures":30},"bimloc2026-table-8","Ablation (App. C.1): mean runtime of each BIM-Loc module with and without the discrepancy module; module given in metric_as_written; the unit of work…",[17976],"CityU construction benchmark (ablation context, App. C.1)",[23],[23],[17980,17981],"BIM-Loc (w\u002F discrepancy detection)","BIM-Loc (w\u002Fo discrepancy detection)",[17932],{"slug":17984,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":108,"note":17988,"datasets":17989,"metrics":17991,"families":17992,"methods":17993,"methodIds":17995,"rows":2388,"failures":30},"zhang1994icp-table-2","zhang1994icp","Zhang, 1994",1994,"Synthetic 3-D curve, 200 points per frame, true r=[0.02,0.25,-0.15], t=[40,120,-50]; noise std varied; 15 iterations; mean of 10 tries",[17990],"synthetic 3-D curve (Sec. 5.1)",[23,38],[40,23],[17994],"iterative pseudo point matching (proposed)",[17985],{"slug":17997,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":17,"note":17998,"datasets":17999,"metrics":18000,"families":18001,"methods":18002,"methodIds":18003,"rows":598,"failures":30},"zhang1994icp-table-3","Same synthetic curve, noise std 2 added to both curves; fraction of first-frame points varied from 1 to 1\u002F10; mean of 10 tries (iteration count not re…",[17990],[23,38],[40,23],[17994],[17985],{"slug":18005,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":33,"note":18006,"datasets":18007,"metrics":18008,"families":18009,"methods":18010,"methodIds":18013,"rows":721,"failures":30},"zhang1994icp-table-4","Same synthetic curve; non-symmetric criterion (2) vs symmetric criterion (1); 10 iterations; mean of 10 tries; execution-time unit not restated in Tab…",[17990],[23,38],[40,23],[18011,18012],"non-symmetric criterion (2), default","symmetric criterion (1), variant",[17985],{"slug":18015,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":6714,"note":18016,"datasets":18017,"metrics":18018,"families":18019,"methods":18020,"methodIds":18021,"rows":274,"failures":30},"zhang1994icp-text-sec-5-1","Single case study, noise std 2, after 15 iterations",[17990],[23,38],[40,23],[17994],[17985],{"slug":18023,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":2391,"note":18024,"datasets":18025,"metrics":18026,"families":18027,"methods":18028,"methodIds":18031,"rows":120,"failures":30},"zhang1994icp-text-sec-5-2","Noise std 3; coarse-to-fine (5 iterations with 1 of 5 points, then 10 with all) vs 15 iterations with all points; mean of 10 experiments",[17990],[23,38],[40,23],[18029,18030],"proposed with coarse-to-fine sampling","proposed, all points, 15 iterations",[17985],{"slug":18033,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":1143,"note":18034,"datasets":18035,"metrics":18037,"families":18038,"methods":18039,"methodIds":18043,"rows":274,"failures":30},"zhang1994icp-text-sec-5-3","Real chair scene, 3-D curves from trinocular stereo (588 and 763 points), about 4 deg and 100 mm displacement; single runs",[18036],"chair scene (authors' data)",[38],[40],[18040,18041,18042],"proposed, all points (converged after 12 iterations)","proposed, coarse-to-fine 1 of 10 points (13 iterations)","proposed, coarse-to-fine 1 of 5 points (12 iterations, same estimate)",[17985],{"slug":18045,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":6572,"note":18046,"datasets":18047,"metrics":18049,"families":18050,"methods":18051,"methodIds":18052,"rows":120,"failures":30},"zhang1994icp-text-sec-6-1","Rock scene dense maps (correlation stereo, 71505 and 51503 points); error relative to a manual registration from marks on rocks; three perturbed initi…",[18048],"rock scene (authors' data)",[23],[23],[17994],[17985],{"slug":18054,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":2984,"note":18055,"datasets":18056,"metrics":18058,"families":18059,"methods":18060,"methodIds":18062,"rows":63,"failures":63},"zhang1994icp-text-sec-6-2","Head figure range images, true rotation -0.1745 rad, initial error 15 deg, about 150 grid points, converged after 39 iterations; run by X. Chen with C…",[18057],"head figure range images",[23],[23],[18061],"proposed algorithm with modified closest-point search (Chen 1992)",[17985],{"slug":18064,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":3023,"note":18065,"datasets":18066,"metrics":18067,"families":18068,"methods":18069,"methodIds":18071,"rows":120,"failures":30},"zhang1994icp-text-sec-7-2","Rock scene rerun with Dmax forced to decrease monotonically; same three initial estimates; data resolution about 5 cm",[18048],[23],[23],[18070],"proposed with monotonic Dmax constraint",[17985],{"slug":18073,"sourceId":17985,"sourceLabel":17986,"sourceYear":17987,"table":18074,"note":18075,"datasets":18076,"metrics":18077,"families":18078,"methods":18079,"methodIds":18080,"rows":63,"failures":30},"zhang1994icp-text-sec-7-3","Text Sec.7.3","Real chair scene: non-symmetric vs symmetric matching criterion, both converged after 12 iterations; estimates differ by 0.6% in rotation and translat…",[18036],[38],[40],[18011,18012],[17985],{"slug":18082,"sourceId":4787,"sourceLabel":18083,"sourceYear":698,"table":325,"note":18084,"datasets":18085,"metrics":18087,"families":18088,"methods":18089,"methodIds":18093,"rows":2262,"failures":30},"superodom2021-table-ii","Zhao et al., 2021","Translational ATE computed with evo against total-station prism trajectory; odometry only, no loop closing",[18086],"authors' DS-drone sensor-suite data (hand-carried)",[329,568],[25],[334,461,18090,18091,18092],"Ours (Super Odometry)","VINS (VINS-Mono)","VINS-Depth (authors' depth-enhanced VINS [20])",[338,2118,4787,251],{"slug":18095,"sourceId":4787,"sourceLabel":18083,"sourceYear":698,"table":279,"note":18096,"datasets":18097,"metrics":18099,"families":18100,"methods":18101,"methodIds":18106,"rows":1042,"failures":274},"superodom2021-table-iii","Average running time per module; submodules run in parallel so total time equals the slowest submodule; 'bypass' = VIO not run",[18098],"authors' DS-drone data",[38],[40],[334,18102,18103,18104,18105],"Ours (Super Odometry, total = slowest parallel submodule)","Super Odometry IMU odometry submodule","Super Odometry LIO submodule","Super Odometry VIO submodule",[338,4787],{"slug":18108,"sourceId":18109,"sourceLabel":18110,"sourceYear":2267,"table":18111,"note":18112,"datasets":18113,"metrics":18115,"families":18116,"methods":18117,"methodIds":18119,"rows":154,"failures":154},"zhao2024deskew-text-sec-4-frame-63","zhao2024deskew","Zhao et al., 2024a","Text Sec.4 frame 63","Mean point-to-point distance to the TLS reference cloud against cut-off threshold for frame 63; difference between skewed and deskewed clouds at large…",[18114],"UoM indoor data",[3417],[78],[18118],"proposed registration-based deskewing (point-to-plane ICP)",[18109],{"slug":18121,"sourceId":18109,"sourceLabel":18110,"sourceYear":2267,"table":18122,"note":18123,"datasets":18124,"metrics":18125,"families":18126,"methods":18127,"methodIds":18128,"rows":63,"failures":63},"zhao2024deskew-text-sec-4-indoor-evaluation","Text Sec.4 indoor evaluation","PRDT computed for every skewed and deskewed point pair in the UoM indoor dataset with angle threshold 10 deg; percentages stated in text describing th…",[18114],[23],[23],[18118],[18109],{"slug":18130,"sourceId":18109,"sourceLabel":18110,"sourceYear":2267,"table":18131,"note":18132,"datasets":18133,"metrics":18135,"families":18136,"methods":18137,"methodIds":18139,"rows":154,"failures":154},"zhao2024deskew-text-sec-4-theoretical-analysis","Text Sec.4 theoretical analysis","Theoretical mean motion distortion for straight motion, approximated as half the distance travelled during one scan, for scan rates 10 to 30 fps; valu…",[18134],"not_applicable (analytical model)",[23],[23],[18138],"theoretical straight-motion distortion model (no deskewing)",[],{"slug":18141,"sourceId":18109,"sourceLabel":18110,"sourceYear":2267,"table":18142,"note":18143,"datasets":18144,"metrics":18147,"families":18148,"methods":18149,"methodIds":18154,"rows":416,"failures":356},"zhao2024deskew-text-sec-5-discussion","Text Sec.5 Discussion","Summary statements in the Discussion on indoor improvement, simulated outdoor improvement and runtime for about 70,000-point clouds",[18114,18145,18146],"not_reported (author statement for outdoor use; Sec. 5 says outdoor results are interpreted from virtual environments, i.e. the synthetic street model, and indoor tests)","not_reported (point clouds of about 70,000 points)",[75,23,38],[40,78,23],[18150,18151,18152,18153],"proposed deskewing: complete process","proposed deskewing: coordinate update step","proposed deskewing: registration step (point-to-plane ICP)","proposed point cloud deskewing method",[18109],{"slug":18156,"sourceId":18157,"sourceLabel":18158,"sourceYear":2267,"table":108,"note":18159,"datasets":18160,"metrics":18163,"families":18164,"methods":18165,"methodIds":18184,"rows":3207,"failures":356},"zhao2024subtmrs-table-2","zhao2024subtmrs","Zhao et al., 2024b","ICCV 2023 SLAM Challenge overall results; LiDAR track teams use LiDAR+IMU, visual track teams use IMU+camera; each team on its own hardware; ATE in m…",[18161,18162],"SubT-MRS (ICCV 2023 SLAM Challenge, LiDAR track)","SubT-MRS (ICCV 2023 SLAM Challenge, visual track)",[329,113,218,219,23,38],[25,40,23],[18166,18167,18168,18169,18170,18171,18172,18173,18174,18175,18176,18177,18178,18179,18180,18181,18182,18183],"Jiang et al.: LET-NET [27], VINS-Mono [35] (hybrid)","Jiang et al.: LET-NET, VINS-Mono","Kim et al.: FAST-LIO2 [51], Point-LIO [20], Quatro [26] (filter)","Kim et al.: FAST-LIO2, Point-LIO, Quatro","Li et al.: ORB-SLAM3","Li et al.: ORB-SLAM3 [4] (SW Opt.)","Liu et al.: FAST-LIO2 [52], HBA [28] (filter)","Liu et al.: FAST-LIO2, HBA","Peng et al.: DVI-SLAM","Peng et al.: DVI-SLAM [33] (learning)","Thien et al.: VR-SLAM","Thien et al.: VR-SLAM [32] (SW Opt; monocular camera and UWB per reference title)","Weitong et al.: FAST-LIO [2] (cites Faster-LIO), Pose Graph [11] (filter)","Weitong et al.: FAST-LIO, Pose Graph","Yibin et al.: LIO-EKF","Yibin et al.: LIO-EKF [46] (filter; [46] is KISS-ICP in the reference list)","Zhong et al.: DLO [8], Scan-Context++ [22] (SW Opt)","Zhong et al.: DLO, Scan-Context++",[763],{"slug":18186,"sourceId":18157,"sourceLabel":18158,"sourceYear":2267,"table":17,"note":18187,"datasets":18188,"metrics":18190,"families":18191,"methods":18192,"methodIds":18198,"rows":2119,"failures":356},"zhao2024subtmrs-table-3","LiDAR track per-sequence ATE (m); real-world geometric degradation (Urban to Laurel Caverns), simulated drone (Factory, Ocean, Sewerage, TartanAir-der…",[18189],"SubT-MRS (LiDAR track)",[329],[25],[18193,18194,18195,18196,18197],"Kim et al. (FAST-LIO2, Point-LIO, Quatro)","Liu et al. (FAST-LIO2, HBA)","Weitong et al. (FAST-LIO, Pose Graph)","Yibin et al. (LIO-EKF)","Zhong et al. (DLO, Scan-Context++)",[],{"slug":18200,"sourceId":18157,"sourceLabel":18158,"sourceYear":2267,"table":33,"note":18201,"datasets":18202,"metrics":18204,"families":18205,"methods":18206,"methodIds":18211,"rows":721,"failures":63},"zhao2024subtmrs-table-4","Visual track per-sequence ATE (m); real-world visual degradation (Lowlight 1 to Outdoor Night) and simulated drone (End of World, Moon, Western Desert…",[18203],"SubT-MRS (visual track)",[329],[25],[18207,18208,18209,18210],"Jiang et al. (LET-NET, VINS-Mono)","Li et al. (ORB-SLAM3)","Peng et al. (DVI-SLAM)","Thien et al. (VR-SLAM)",[763],{"slug":18213,"sourceId":1646,"sourceLabel":18214,"sourceYear":2267,"table":91,"note":18215,"datasets":18216,"metrics":18217,"families":18218,"methods":18219,"methodIds":18221,"rows":1069,"failures":30},"trajlo2024-table-i","Zheng & Zhu, 2024","KITTI odometry RTE (%); points are motion-corrected in KITTI so continuous registration is disabled and motion constraints span 4 scans; 0.205 deg ver…",[469],[438],[441],[3131,8717,571,18220],"Ours (Traj-LO)",[596,393,577,1646],{"slug":18223,"sourceId":1646,"sourceLabel":18214,"sourceYear":2267,"table":279,"note":18224,"datasets":18225,"metrics":18227,"families":18228,"methods":18229,"methodIds":18243,"rows":6014,"failures":18244},"trajlo2024-table-iii","Hilti 2021 handheld sequences; L1 Ouster OS0-64, L2 Livox MID70, I IMU embedded in L1; ground truth from Hilti PLT 300 total station or MoCap; x diver…",[18226],"Hilti 2021 SLAM challenge",[329],[25],[18230,18231,18232,18233,18234,18235,18236,18237,18238,18239,334,18240,18241,18242],"CLIC (L1+I)","CLIC (L2+I)","CT-ICP (L1)","CT-ICP (L2)","FAST-LIO [7] (L1+I)","FAST-LIO [7] (L2+I)","FLOAM (L1)","FLOAM (L2)","KISS-ICP (L1)","KISS-ICP (L2)","Ours (Traj-LO, L1)","Ours (Traj-LO, L1+L2)","Ours (Traj-LO, L2)",[596,321,393,577,338,1646],23,{"slug":18246,"sourceId":1646,"sourceLabel":18214,"sourceYear":2267,"table":731,"note":18247,"datasets":18248,"metrics":18249,"families":18250,"methods":18251,"methodIds":18254,"rows":618,"failures":30},"trajlo2024-table-iv","Marginalization ablation on NTU VIRAL nya01 (indoor) and sbs01 (outdoor) with one segment (seg1) or four segments (seg4) in the window",[1636],[329],[25],[18252,18253],"Traj-LO with marginalization","Traj-LO without marginalization",[1646],{"slug":18256,"sourceId":1646,"sourceLabel":18214,"sourceYear":2267,"table":818,"note":18257,"datasets":18258,"metrics":18259,"families":18260,"methods":18261,"methodIds":18268,"rows":224,"failures":30},"trajlo2024-table-v","Total processing time for NTU VIRAL nya01 (sequence duration 395 s), LiDAR-only methods",[1636],[219,23],[40,23],[3131,8717,571,18262,18263,18264,18265,18266,18267],"Traj-LO seg1","Traj-LO seg1 with marginalization","Traj-LO seg1 without marginalization","Traj-LO seg4","Traj-LO seg4 with marginalization","Traj-LO seg4 without marginalization",[596,393,577,1646],{"slug":18270,"sourceId":815,"sourceLabel":18271,"sourceYear":306,"table":325,"note":18272,"datasets":18273,"metrics":18274,"families":18275,"methods":18276,"methodIds":18279,"rows":3547,"failures":356},"fastlivo2022-table-ii","Zheng et al., 2022","Absolute translational error RMSE on the nine NTU-VIRAL sequences (horizontal OS1-16, its IMU, left camera); same parameters for all sequences; DVL-SL…",[16003],[568],[25],[18277,1437,811,4779,18278],"DVL-SLAM (no loop closure)","SVO2.0 (edgelets+prior)",[321,815,4786,1512],{"slug":18281,"sourceId":815,"sourceLabel":18271,"sourceYear":306,"table":279,"note":18282,"datasets":18283,"metrics":18285,"families":18286,"methods":18287,"methodIds":18288,"rows":340,"failures":30},"fastlivo2022-table-iii","Mean time per frame of each module; FAST-LIVO on a desktop Intel i7 and on the Qualcomm RB5 ARM platform",[18284],"private datasets",[38],[40],[811],[815],{"slug":18290,"sourceId":815,"sourceLabel":18271,"sourceYear":306,"table":18291,"note":18292,"datasets":18293,"metrics":18295,"families":18296,"methods":18297,"methodIds":18298,"rows":154,"failures":30},"fastlivo2022-text-sec-vi-b2","Text Sec.VI-B2","LiDAR-degenerated scene: facing a wall about 30 m long; FAST-LIO2 and SVO2.0 drift shown only qualitatively",[18294],"private dataset",[1551],[1553],[811],[815],{"slug":18300,"sourceId":815,"sourceLabel":18271,"sourceYear":306,"table":18301,"note":18302,"datasets":18303,"metrics":18304,"families":18305,"methods":18306,"methodIds":18307,"rows":154,"failures":30},"fastlivo2022-text-sec-vi-b3","Text Sec.VI-B3","Visual challenge: indoor-outdoor transitions, two aggressive motions, texture-less white wall; path 79.52 m",[18294],[1551],[1553],[811],[815],{"slug":18309,"sourceId":815,"sourceLabel":18271,"sourceYear":306,"table":12389,"note":18310,"datasets":18311,"metrics":18312,"families":18313,"methods":18314,"methodIds":18315,"rows":274,"failures":30},"fastlivo2022-text-sec-vi-c","Mean time per LiDAR and image frame over all private datasets",[18284],[38],[40],[811,4779],[815,4786],{"slug":18317,"sourceId":797,"sourceLabel":18318,"sourceYear":562,"table":325,"note":18319,"datasets":18320,"metrics":18324,"families":18325,"methods":18326,"methodIds":18332,"rows":15170,"failures":52},"fastlivo2-2025-table-ii","Zheng et al., 2025","Absolute translational error RMSE on Hilti'22 and Hilti'23 (handheld: PandarXT-32, BMI085; robot: BPearl, MTi-670; front camera), scored through the o…",[18321,18322,18323],"Hilti'22","Hilti'23","NTU-VIRAL, Hilti'22, Hilti'23",[568],[25],[1437,811,2380,18327,81,18328,18329,18330,2381,18331],"Our LIO","Ours (w normal)","Ours (w\u002Fo expo)","Ours (w\u002Fo update)","SDV-LOAM",[321,815,797,2386,2387],{"slug":18334,"sourceId":797,"sourceLabel":18318,"sourceYear":562,"table":279,"note":18335,"datasets":18336,"metrics":18338,"families":18339,"methods":18340,"methodIds":18342,"rows":3323,"failures":63},"fastlivo2-2025-table-iii","Processing time per LiDAR and image frame; x = system failed; FAST-LIVO2 split into LiDAR and image parts; only the Hilti'22 construction sequences an…",[18321,18337],"Hilti'22, Hilti'23, NTU-VIRAL, FAST-LIVO2 private",[38],[40],[811,14056,18341,2380,2381],"FAST-LIVO2 (ARM)",[815,797,2386,2387],{"slug":18344,"sourceId":797,"sourceLabel":18318,"sourceYear":562,"table":18345,"note":18346,"datasets":18347,"metrics":18349,"families":18350,"methods":18351,"methodIds":18352,"rows":154,"failures":154},"fastlivo2-2025-text-sec-ix-c","Text Sec.IX-C","Return-to-start error in a very dim mining tunnel with LiDAR and visual degeneration; R3LIVE and FAST-LIVO failed; value is an upper bound",[18348],"FAST-LIVO2 private dataset",[1551],[1553],[14056],[797],{"slug":18354,"sourceId":797,"sourceLabel":18318,"sourceYear":562,"table":18355,"note":18356,"datasets":18357,"metrics":18359,"families":18360,"methods":18361,"methodIds":18362,"rows":154,"failures":30},"fastlivo2-2025-text-sec-x-a2","Text Sec.X-A2","Fully onboard UAV navigation; FAST-LIVO2 shares the NUC with planning (8.43 ms) and MPC (18.5 ms)",[18358],"UAV flights (Basement, Woods, Narrow Opening, SYSU Campus)",[38],[40],[14056],[797],{"slug":18364,"sourceId":797,"sourceLabel":18318,"sourceYear":562,"table":18365,"note":18366,"datasets":18367,"metrics":18368,"families":18369,"methods":18370,"methodIds":18371,"rows":618,"failures":30},"fastlivo2-2025-text-sec-x-b","Text Sec.X-B","Airborne mapping on MARS-LVIG (DJI M300 RTK, Livox Avia, global-shutter RGB camera); reference trajectory source not stated in this paper",[5857],[329,38],[25,40],[14056,2381],[797,2387],{"slug":18373,"sourceId":3263,"sourceLabel":18374,"sourceYear":374,"table":325,"note":18375,"datasets":18376,"metrics":18378,"families":18379,"methods":18380,"methodIds":18383,"rows":3819,"failures":30},"shinemapping2023-table-ii","Zhong et al., 2023","Mesh reconstruction quality from posed LiDAR scans, all methods meshed by marching cubes on the same fixed grid with 10 cm voxel or feature size; comp…",[18377],"MaiCity (synthetic)",[2343,74,75,76],[78],[81,18381,12812,18382,82],"Ours + DR","VDB Fusion",[85,3263,3264,3265],{"slug":18385,"sourceId":3263,"sourceLabel":18374,"sourceYear":374,"table":279,"note":18375,"datasets":18386,"metrics":18387,"families":18388,"methods":18389,"methodIds":18390,"rows":3819,"failures":30},"shinemapping2023-table-iii",[3253],[2343,74,75,76],[78],[81,18381,12812,18382,82],[85,3263,3264,3265],{"slug":18392,"sourceId":8957,"sourceLabel":18393,"sourceYear":4828,"table":69,"note":18394,"datasets":18395,"metrics":18397,"families":18398,"methods":18399,"methodIds":18407,"rows":2252,"failures":30},"zhou2016fgr-table-1","Zhou et al., 2016","25 synthetic range-image pairs per noise level; RMSE of ground-truth correspondence distances, unit surface diameter; GoICP variants on 1,000 points",[18396],"Synthetic range images (AIM@SHAPE, Berkeley Angel, Stanford Bunny)",[23],[23],[18400,18401,18402,18403,18404,18405,18406],"CZK [7] (Choi et al. variant of Rusu's algorithm)","GoICP [42]","GoICP-Trimming [42]","OpenCV [8] (implementation of Drost et al.)","Our approach (FGR)","PCL [19,34] (PCL implementation of Rusu et al.)","Super 4PCS [26]",[1788,16489,8957],{"slug":18409,"sourceId":8957,"sourceLabel":18393,"sourceYear":4828,"table":108,"note":18410,"datasets":18411,"metrics":18413,"families":18414,"methods":18415,"methodIds":18416,"rows":2252,"failures":30},"zhou2016fgr-table-2","Average running time of each global method on each synthetic model and over all models (the number of tests averaged per model is not stated; each mod…",[18412],"Synthetic range images",[38],[40],[18400,18401,18402,18403,18404,18405,18406],[1788,16489,8957],{"slug":18418,"sourceId":8957,"sourceLabel":18393,"sourceYear":4828,"table":17,"note":18419,"datasets":18420,"metrics":18421,"families":18422,"methods":18423,"methodIds":18428,"rows":598,"failures":30},"zhou2016fgr-table-3","Timing of local refinement methods and FGR on the same five synthetic models as Table 2 (same point counts; FGR column identical to Table 2); the capt…",[18412],[38],[40],[18404,18424,18425,18426,18427],"PCL ICP point-to-plane","PCL ICP point-to-point","Sparse ICP point-to-plane [5]","Sparse ICP point-to-point [5]",[478,1232,1944,8957],{"slug":18430,"sourceId":8957,"sourceLabel":18393,"sourceYear":4828,"table":33,"note":18431,"datasets":18432,"metrics":18434,"families":18435,"methods":18436,"methodIds":18437,"rows":654,"failures":30},"zhou2016fgr-table-4","Average running time over the 188 UWA pairwise tests",[18433],"UWA benchmark",[38],[40],[18400,18401,18402,18403,18404,18405,18406],[1788,16489,8957],{"slug":18439,"sourceId":8957,"sourceLabel":18393,"sourceYear":4828,"table":244,"note":18440,"datasets":18441,"metrics":18443,"families":18444,"methods":18445,"methodIds":18446,"rows":641,"failures":30},"zhou2016fgr-table-5","Choi et al. scene benchmark: 4 datasets of 47 to 57 fragments with simulated depth camera noise; all fragment pairs; recall and precision as defined b…",[18442],"Choi et al. scene benchmark",[23,38],[40,23],[18400,18403,18404,18405,18406],[1788,8957],{"slug":18448,"sourceId":8957,"sourceLabel":18393,"sourceYear":4828,"table":1072,"note":18449,"datasets":18450,"metrics":18451,"families":18452,"methods":18453,"methodIds":18456,"rows":1042,"failures":30},"zhou2016fgr-table-6","Multi-way registration of all fragments (47 to 57 per sequence), lambda = 2; mean distance of integrated surface to ground-truth model and total time",[2939],[75,23],[78,23],[18454,18455],"Choi et al. [7]","Ours (FGR multi-way)",[8957],{"slug":18458,"sourceId":8957,"sourceLabel":18393,"sourceYear":4828,"table":6714,"note":18459,"datasets":18460,"metrics":18461,"families":18462,"methods":18463,"methodIds":18464,"rows":356,"failures":30},"zhou2016fgr-text-sec-5-1","UWA benchmark, 188 tests with clutter, occlusion and overlap down to 21%; 0.05-recall is the fraction of tests with RMSE below 0.05; three methods rep…",[18433],[1933],[1935],[18400,18403,18404,18405],[1788,8957],{"slug":18466,"sourceId":11850,"sourceLabel":18467,"sourceYear":107,"table":2713,"note":18468,"datasets":18469,"metrics":18470,"families":18471,"methods":18472,"methodIds":18476,"rows":274,"failures":63},"zhou2018open3d-text-sec-4","Zhou et al., 2018","Speed claims stated in the Optimization section without hardware, datasets or timing protocol",[2070],[23],[23],[18473,18474,18475],"Open3D ICP versus PCL ICP","Open3D backend with OpenMP parallelisation","Open3D implementation of the Choi et al. reconstruction pipeline",[11850],{"slug":18478,"sourceId":18479,"sourceLabel":18480,"sourceYear":698,"table":91,"note":18481,"datasets":18482,"metrics":18484,"families":18485,"methods":18486,"methodIds":18491,"rows":608,"failures":356},"zhou2021planeadjust-table-i","zhou2021planeadjust","Zhou et al., 2021","Keyframe ATE (m), median of 5 runs, on four indoor NavVis M6 datasets (VLP-16 data only) with large rotations; ground truth = offline fused NavVis tra…",[18483],"own indoor datasets A-D (NavVis M6)",[329],[25],[18487,408,81,18488,18489,18490],"BALM [29]","Ours - GPA","Ours - LPA - GPA","pi-LSAM [22]",[7567,395,18479],{"slug":18493,"sourceId":18479,"sourceLabel":18480,"sourceYear":698,"table":325,"note":18494,"datasets":18495,"metrics":18496,"families":18497,"methods":18498,"methodIds":18508,"rows":102,"failures":30},"zhou2021planeadjust-table-ii","Runtime (ms, mean +\u002F- std) of components on datasets B and C",[18483],[38],[40],[18499,18500,18501,18502,18503,18504,18505,18506,18507],"Ours, Global Mapping: GPA","Ours, Global Mapping: Update ICM","Ours, Local Mapping: Detect Planes","Ours, Local Mapping: GCC","Ours, Local Mapping: LPA","Ours, Local Mapping: Match Planes","Ours, Localization: Forward ICP Flow","Ours, Localization: Keyframe Decision","Ours, Localization: Pose Estimation",[18479],{"slug":18510,"sourceId":18479,"sourceLabel":18480,"sourceYear":698,"table":11864,"note":18511,"datasets":18512,"metrics":18513,"families":18514,"methods":18515,"methodIds":18521,"rows":578,"failures":30},"zhou2021planeadjust-text-sec-viii-b","Runtime statements in text for datasets B and C",[18483],[23,38],[40,23],[18516,18517,18518,18519,18520],"Ours, LPA with reduced residuals","Ours, local mapping","Ours, localization","pi-LSAM [22], localization","plane extraction of Sec. V-A (instead of plane tracking)",[18479],{"slug":18523,"sourceId":18524,"sourceLabel":18525,"sourceYear":562,"table":91,"note":18526,"datasets":18527,"metrics":18529,"families":18530,"methods":18531,"methodIds":18532,"rows":6101,"failures":52},"fastlivo2rc2025-table-i","fastlivo2rc2025","Zhou et al., 2025","ATE RMSE on 16 Hilti'22 and Hilti'23 sequences computed with the official Hilti evaluation tools; parameters of all methods tuned by the authors; x =…",[18321,18528,18322],"Hilti'22 and Hilti'23",[568],[25],[1437,811,14056,2380,81,2381,18331],[321,815,797,18524,2386,2387],{"slug":18534,"sourceId":18524,"sourceLabel":18525,"sourceYear":562,"table":325,"note":18535,"datasets":18536,"metrics":18537,"families":18538,"methods":18539,"methodIds":18540,"rows":224,"failures":30},"fastlivo2rc2025-table-ii","Mean per-frame time over the 16 Hilti sequences, standard error given in metric_as_written; ARM runtime measured on CPU only",[18528],[38],[40],[14056,81],[797,18524],{"slug":18542,"sourceId":18524,"sourceLabel":18525,"sourceYear":562,"table":279,"note":18543,"datasets":18544,"metrics":18545,"families":18546,"methods":18547,"methodIds":18548,"rows":618,"failures":30},"fastlivo2rc2025-table-iii","Memory usage on the x86 laptop; only the three Hilti'22 construction sequences and the Average row kept for the row cap",[18321,18528],[219],[40],[14056,81],[797,18524],{"slug":18550,"sourceId":18524,"sourceLabel":18525,"sourceYear":562,"table":18551,"note":18552,"datasets":18553,"metrics":18555,"families":18556,"methods":18557,"methodIds":18558,"rows":154,"failures":154},"fastlivo2rc2025-text-sec-v-c1","Text Sec.V-C1","Return-to-origin drift on private sequences that physically return to the start; value is an upper bound",[18554],"private sequences",[1551],[1553],[81],[18524],{"slug":18560,"sourceId":18524,"sourceLabel":18525,"sourceYear":562,"table":18561,"note":18562,"datasets":18563,"metrics":18564,"families":18565,"methods":18566,"methodIds":18567,"rows":154,"failures":30},"fastlivo2rc2025-text-sec-v-c2","Text Sec.V-C2","Real-time localization on the RK3588 board in an underground parking lot and a nighttime street",[18554],[38],[40],[81],[18524],{"slug":18569,"sourceId":18524,"sourceLabel":18525,"sourceYear":562,"table":18570,"note":18571,"datasets":18572,"metrics":18573,"families":18574,"methods":18575,"methodIds":18578,"rows":63,"failures":30},"fastlivo2rc2025-text-sec-v-e1","Text Sec.V-E1","Map-structure ablation on MARS-LVIG HKIsland03 with RTK trajectory as ground truth, RMSE via evo",[5857],[568],[25],[18576,18577],"Ours with long-term visual map","Ours without long-term visual map",[18524],{"slug":18580,"sourceId":18581,"sourceLabel":18582,"sourceYear":698,"table":91,"note":18583,"datasets":18584,"metrics":18586,"families":18587,"methods":18588,"methodIds":18591,"rows":1315,"failures":30},"camvox2021-table-i","camvox2021","Zhu et al., 2021","SUSTech dataset collected by the authors (outdoor campus route around the SUSTech expert apartment area) evaluated with evo against GPS-RTK (Inertial…",[18585],"SUSTech dataset (CamVox)",[329,568,23],[25,23],[4758,18589,18590],"VINS-mono","livox_horizon_loam",[18581,251],{"slug":18593,"sourceId":18581,"sourceLabel":18582,"sourceYear":698,"table":325,"note":18594,"datasets":18595,"metrics":18596,"families":18597,"methods":18598,"methodIds":18599,"rows":224,"failures":274},"camvox2021-table-ii","Timing analysis: CamVox on the SUSTech dataset (1520 x 568 images, 10 Hz, 1500 ORB features) versus ORB-SLAM2 on TUM (640 x 480, 30 Hz, 1000 ORB featu…",[18585,2872],[23,38],[40,23],[4758,6668],[18581,1511],{"slug":18601,"sourceId":3184,"sourceLabel":18602,"sourceYear":306,"table":69,"note":18603,"datasets":18604,"metrics":18605,"families":18606,"methods":18607,"methodIds":18611,"rows":1042,"failures":30},"niceslam2022-table-1","Zhu et al., 2022a","Replica, average over 8 scenes and 5 runs; 3D metrics computed after removing regions outside every camera frustum; TSDF-Fusion uses NICE-SLAM poses a…",[3223],[75,76,219,23],[40,78,23],[18608,3174,18609,18610],"DI-Fusion [16]","TSDF-Fusion [11] (with NICE-SLAM poses)","iMAP* [47] (re-implementation)",[2826,6378,3184],{"slug":18613,"sourceId":3184,"sourceLabel":18602,"sourceYear":306,"table":108,"note":18614,"datasets":18615,"metrics":18616,"families":18617,"methods":18618,"methodIds":18623,"rows":1315,"failures":30},"niceslam2022-table-2","TUM RGB-D camera tracking, best of 5 runs for all methods; iMAP, BAD-SLAM, Kintinuous and ORB-SLAM2 values taken from the iMAP paper",[2872],[568],[25],[18619,18608,18620,3174,18621,18622,18610],"BAD-SLAM [43]","Kintinuous [60]","ORB-SLAM2 [27]","iMAP [47]",[9325,6378,2448,3184,1511],{"slug":18625,"sourceId":3184,"sourceLabel":18602,"sourceYear":306,"table":17,"note":18626,"datasets":18627,"metrics":18628,"families":18629,"methods":18630,"methodIds":18631,"rows":1315,"failures":30},"niceslam2022-table-3","ScanNet camera tracking on 6 large scenes; unit not stated in the caption (Table 2 of the same paper uses cm)",[2963],[568],[25],[18608,3174,18610],[6378,3184],{"slug":18633,"sourceId":3184,"sourceLabel":18602,"sourceYear":306,"table":33,"note":18634,"datasets":18635,"metrics":18636,"families":18637,"methods":18638,"methodIds":18639,"rows":120,"failures":30},"niceslam2022-table-4","Computation and runtime with equal pixel samples (Mt = 200 for tracking, M = 1000 for mapping); whether times are per iteration or per frame is not st…",[2070],[23],[23],[3174,18622],[6378,3184],{"slug":18641,"sourceId":3184,"sourceLabel":18602,"sourceYear":306,"table":244,"note":18642,"datasets":18643,"metrics":18644,"families":18645,"methods":18646,"methodIds":18651,"rows":618,"failures":30},"niceslam2022-table-5","Ablation on ScanNet: mean and standard deviation of ATE RMSE over 5 runs per scene, averaged over 6 scenes",[2963],[568],[25],[18647,18648,18649,18650],"NICE-SLAM Full","NICE-SLAM w\u002F iMAP keyframes","NICE-SLAM w\u002Fo L_p (no photometric loss)","NICE-SLAM w\u002Fo Local BA",[3184],{"slug":18653,"sourceId":3184,"sourceLabel":18602,"sourceYear":306,"table":4863,"note":18654,"datasets":18655,"metrics":18657,"families":18658,"methods":18659,"methodIds":18661,"rows":63,"failures":30},"niceslam2022-text-sec-4-3","Co-Fusion sequence with a dynamic object; camera tracking ATE RMSE",[18656],"Co-Fusion",[568],[25],[3174,18660],"iMAP* (re-implementation)",[6378,3184],{"slug":18663,"sourceId":18664,"sourceLabel":18665,"sourceYear":306,"table":325,"note":18666,"datasets":18667,"metrics":18670,"families":18671,"methods":18672,"methodIds":18674,"rows":102,"failures":30},"zhu2022liinit-table-ii","zhu2022liinit","Zhu et al., 2022b","Temporal initialization with artificial IMU timestamp shifts on Livox LiDARs with built-in IMUs; 5 laboratory sequences per LiDAR; true offset about 0…",[18668,18669],"authors' handheld sequences (Livox Avia, built-in IMU)","authors' handheld sequences (Livox Mid360, built-in IMU)",[23],[23],[18673],"LI-Init (proposed)",[18664],{"slug":18676,"sourceId":18664,"sourceLabel":18665,"sourceYear":306,"table":279,"note":18677,"datasets":18678,"metrics":18680,"families":18681,"methods":18682,"methodIds":18683,"rows":224,"failures":30},"zhu2022liinit-table-iii","Extrinsic initialization: absolute error of the relative pose between two Pixhawk IMU mounting poses (from two LI-Init calibrations) against the CAD d…",[18679],"authors' handheld sequences (Pixhawk IMU at poses I1 and I2)",[23],[23],[18673],[18664],{"slug":18685,"sourceId":18664,"sourceLabel":18665,"sourceYear":306,"table":731,"note":18686,"datasets":18687,"metrics":18689,"families":18690,"methods":18691,"methodIds":18694,"rows":52,"failures":154},"zhu2022liinit-table-iv","Extrinsic calibration comparison on two Hesai PandarXT plus Pixhawk sequences, first 40 s (400 scans) each; average relative IMU pose error against CA…",[18688],"two PandarXT plus Pixhawk sequences from Sec. IV-B.1",[23],[23],[18692,4271,18693],"LI-Calib [14]","Target-Free [15]",[9730,18664],{"slug":18696,"sourceId":18664,"sourceLabel":18665,"sourceYear":306,"table":1182,"note":18697,"datasets":18698,"metrics":18700,"families":18701,"methods":18702,"methodIds":18705,"rows":63,"failures":30},"zhu2022liinit-text-sec-iv-a","FAST-LIO2 rerun on an unsynchronized Hesai PandarXT plus Pixhawk handheld sequence (55.8 s, device waved and returned to origin; first 20 s used for L…",[18699],"authors' handheld sequence (Hesai PandarXT plus Pixhawk IMU)",[1551],[1553],[18703,18704],"FAST-LIO2 with its internal time synchronization","FAST-LIO2 with time offset calibrated by LI-Init",[321,18664],{"slug":18707,"sourceId":18664,"sourceLabel":18665,"sourceYear":306,"table":1208,"note":18708,"datasets":18709,"metrics":18710,"families":18711,"methods":18712,"methodIds":18715,"rows":63,"failures":63},"zhu2022liinit-text-sec-iv-c","Runtime statements in the time consumption evaluation; desktop Intel i7-10700",[2070],[23,38],[40,23],[18713,18714],"LI-Init LiDAR odometry","LI-Init initialization solver",[18664],{"slug":18717,"sourceId":18718,"sourceLabel":18719,"sourceYear":2267,"table":69,"note":18720,"datasets":18721,"metrics":18722,"families":18723,"methods":18724,"methodIds":18733,"rows":2882,"failures":30},"nicerslam2024-table-1","nicerslam2024","Zhu et al., 2024","3DV Table 1: Replica mesh reconstruction averaged over 8 scenes (per-scene values not extracted); RGB-D group NICE-SLAM and Vox-Fusion, RGB group COLM…",[3223],[75,76,23],[78,23],[18725,18726,18727,18728,18729,18730,18731,18732],"COLMAP (RGB input)","DIM-SLAM* (RGB input, authors' reimplementation)","DROID-SLAM (RGB input, TSDF fusion of keyframe depths)","NICE-SLAM (RGB-D input)","NICER-SLAM (RGB input)","NeRF-SLAM (RGB input)","TANDEM (RGB input)","Vox-Fusion (RGB-D input)",[6178,3184],{"slug":18735,"sourceId":18718,"sourceLabel":18719,"sourceYear":2267,"table":108,"note":18736,"datasets":18737,"metrics":18738,"families":18739,"methods":18740,"methodIds":18742,"rows":224,"failures":30},"nicerslam2024-table-2","3DV Table 2: Replica novel view synthesis averaged over 8 scenes, for extrapolated views far from the training views and for interpolated views; only…",[3223],[23],[23],[18726,18741,18728,18729,18730,18732],"DROID-SLAM (RGB input)",[6178,3184],{"slug":18744,"sourceId":18718,"sourceLabel":18719,"sourceYear":2267,"table":17,"note":18745,"datasets":18746,"metrics":18747,"families":18748,"methods":18749,"methodIds":18754,"rows":6820,"failures":63},"nicerslam2024-table-3","3DV Table 3: Replica ATE RMSE; trajectories aligned to ground truth with evo (transform not stated); DIM-SLAM* is the authors reimplementation; DROID-…",[3223],[568],[25],[18725,18750,18726,18741,18751,18752,18728,18729,18730,18753,18731,18732],"DIM-SLAM (RGB input)","DROID-SLAM* (RGB input, no global BA or loop closure)","DSO (RGB input)","Orbeez-SLAM (RGB input)",[6178,1507,3184],{"slug":18756,"sourceId":18718,"sourceLabel":18719,"sourceYear":2267,"table":18757,"note":18758,"datasets":18759,"metrics":18760,"families":18761,"methods":18762,"methodIds":18764,"rows":120,"failures":30},"nicerslam2024-arxiv-v1-table-4","arXiv v1 Table 4","7-Scenes (real, low resolution, motion blur) ATE RMSE averaged over 7 scenes; per-scene values not extracted; from arXiv v1 only",[9555],[568],[25],[18725,18741,18763,18728,18729,18732],"DROID-SLAM* (RGB input)",[6178,3184],{"slug":18766,"sourceId":18767,"sourceLabel":18768,"sourceYear":562,"table":91,"note":18769,"datasets":18770,"metrics":18771,"families":18772,"methods":18773,"methodIds":18784,"rows":1042,"failures":30},"zhu2025meshloam-table-i","zhu2025meshloam","Zhu et al., 2025","KITTI odometry 00-10, KITTI relative error protocol; DLO, KISS-ICP and FLOAM re-run by the authors, other rows taken from published papers; Ours (poin…",[4494],[438,439],[441],[18774,18775,18776,18777,18778,18779,18780,18781,18782,18783],"DLO [41] (point cloud)","FLOAM [4] (point cloud)","KISS-ICP [5] (point cloud)","Litamin2 [40] (NDT)","Ours (mesh)","Ours (point-to-plane) (point cloud)","Puma [13] (mesh)","SLAMesh [14] (mesh)","SuMa [8] (surfel)","SuMa++ [9] (surfel)",[2088,393,577,11635,432,1968,3264,18767],{"slug":18786,"sourceId":18767,"sourceLabel":18768,"sourceYear":562,"table":325,"note":18787,"datasets":18788,"metrics":18789,"families":18790,"methods":18791,"methodIds":18792,"rows":618,"failures":30},"zhu2025meshloam-table-ii","KITTI odometry 00-10 absolute trajectory error (m); SuMa and Litamin2 ATE exported from the Litamin2 paper, others run by the authors; statistic and a…",[4494],[329],[25],[18775,18776,18777,18778,18779,18780,18781,18782],[393,577,11635,432,3264,18767],{"slug":18794,"sourceId":18767,"sourceLabel":18768,"sourceYear":562,"table":279,"note":18795,"datasets":18796,"metrics":18797,"families":18798,"methods":18799,"methodIds":18804,"rows":721,"failures":654},"zhu2025meshloam-table-iii","Hilti SLAM Challenge 2021, Ouster OS0-64 data only; all methods run by the authors with their own implementations; caption gives ATE in m while the te…",[9423],[329],[25],[18800,1370,81,18801,18802,18803],"FLOAM [4]","Puma [13]","SLAMesh [14]","SuMa [8]",[393,577,11635,432,3264,18767],{"slug":18806,"sourceId":18767,"sourceLabel":18768,"sourceYear":562,"table":731,"note":18807,"datasets":18808,"metrics":18809,"families":18810,"methods":18811,"methodIds":18812,"rows":224,"failures":63},"zhu2025meshloam-table-iv","Odometry ATE (m) on Mai City sequence Mai01 (virtual Velodyne HDL-64) and Newer College NCD-QUAD (Ouster OS-1); 'x' = failed registration; statistic a…",[12808,3253],[329],[25],[18800,1370,81,18801,18802,18803],[393,577,11635,432,3264,18767],{"slug":18814,"sourceId":18767,"sourceLabel":18768,"sourceYear":562,"table":818,"note":18815,"datasets":18816,"metrics":18817,"families":18818,"methods":18819,"methodIds":18822,"rows":396,"failures":30},"zhu2025meshloam-table-v","Mesh quality with ground-truth poses for all methods, voxel size 0.1 m, settings of SHINE-Mapping; distances in cm; completion ratio and F-score in %…",[12808,3253],[2343,74,75,76],[78],[81,18801,18820,18802,18821],"SHINE-Mapping [30]","VDB Fusion [28]",[11635,3263,3264,3265,18767],{"slug":18824,"sourceId":18767,"sourceLabel":18768,"sourceYear":562,"table":827,"note":18825,"datasets":18826,"metrics":18827,"families":18828,"methods":18829,"methodIds":18832,"rows":578,"failures":30},"zhu2025meshloam-table-vi","Ablation on KITTI odometry: per-frame computational cost (ms) of each module with the passive (proposed) versus active SDF estimation model; both GPU-…",[4494],[38],[40],[18830,18831],"Mesh-LOAM with active SDF computational model","Mesh-LOAM with passive SDF computational model",[18767],{"slug":18834,"sourceId":18767,"sourceLabel":18768,"sourceYear":562,"table":852,"note":18835,"datasets":18836,"metrics":18837,"families":18838,"methods":18839,"methodIds":18840,"rows":102,"failures":30},"zhu2025meshloam-table-viii","KITTI sequence 07: per-frame computational time (ms) and peak memory (MB); Puma and SLAMesh are CPU-based; Mesh-LOAM allocates extra GPU memory for pa…",[4494],[219,38],[40],[81,18801,18802],[11635,3264,18767],{"slug":18842,"sourceId":18843,"sourceLabel":18844,"sourceYear":374,"table":91,"note":18845,"datasets":18846,"metrics":18848,"families":18849,"methods":18850,"methodIds":18855,"rows":415,"failures":356},"iriom4d2023-table-i","iriom4d2023","Zhuang et al., 2023","In-house ground-robot sequences with ARS548 radar and EPSON G345 IMU; reference: Bynav X1-5H GNSS\u002FINS for sequences 1-2 and FastLIO-SLAM (LiDAR-IMU) f…",[18847],"4D iRIOM in-house radar dataset",[568,2411,330,1551,23],[25,1553,23,332],[18851,18852,18853,18854],"EKFRIO","FastLIO (FastLIO-SLAM with loop closure)","iRIO (without loop closure)","iRIOM",[18843],{"slug":18857,"sourceId":18843,"sourceLabel":18844,"sourceYear":374,"table":325,"note":18858,"datasets":18859,"metrics":18860,"families":18861,"methods":18862,"methodIds":18863,"rows":721,"failures":30},"iriom4d2023-table-ii","Timing statistics of iRIOM (max \u002F min and mean in ms) per radar scan on a consumer laptop",[18847],[38],[40],[18854],[18843],{"slug":18865,"sourceId":18843,"sourceLabel":18844,"sourceYear":374,"table":18866,"note":18867,"datasets":18868,"metrics":18869,"families":18870,"methods":18871,"methodIds":18877,"rows":641,"failures":30},"iriom4d2023-table-iii-failure-counts","Table III (failure counts)","Ablation on in-house sequences, number of failures F in 10 repetitions; Vel.: ego-velocity update only; S2M: scan-to-submap update only; Both: both up…",[18847],[23],[23],[18872,18873,18874,18875,18876],"iRIO variant: Both","iRIO variant: CVM","iRIO variant: S2M","iRIO variant: S2S","iRIO variant: Vel.",[18843],{"slug":18879,"sourceId":18843,"sourceLabel":18844,"sourceYear":374,"table":731,"note":18880,"datasets":18881,"metrics":18883,"families":18884,"methods":18885,"methodIds":18886,"rows":608,"failures":30},"iriom4d2023-table-iv","ColoRadar dataset sequences; closure error and APE RMSE of the radar methods against the dataset reference trajectory",[18882],"ColoRadar",[568,1551,23],[25,1553,23],[18851,18853,18854],[18843],{"slug":18888,"sourceId":18889,"sourceLabel":18890,"sourceYear":921,"table":91,"note":18891,"datasets":18892,"metrics":18894,"families":18895,"methods":18896,"methodIds":18903,"rows":120,"failures":30},"zlot-bosse2014-mine-table-i","zlot_bosse2014_mine","Zlot & Bosse, 2014","Computation time per processing stage from raw data to the survey-registered model; acquisition time 113 min",[18893],"Northparkes Mine deployment, April 2011 (self-collected)",[23],[23],[18897,18898,18899,18900,18901,18902],"Coarse alignment to mine survey data (place recognition)","Global registration to mine survey data (non-rigid registration)","Global trajectory registration (non-rigid registration)","Loop closure and coarse global alignment (place recognition)","Open-loop trajectory generation (non-rigid registration)","Total pipeline",[18889],{"slug":18905,"sourceId":18889,"sourceLabel":18890,"sourceYear":921,"table":7969,"note":18906,"datasets":18907,"metrics":18908,"families":18909,"methods":18910,"methodIds":18912,"rows":63,"failures":30},"zlot-bosse2014-mine-text-sec-4-3","Map surfels against 35,612 surfels built from the mine survey after the point cloud was registered to that same survey (fixed surfels); a post-registr…",[18893],[23],[23],[18911],"Survey-registered solution",[18889],{"slug":18914,"sourceId":18889,"sourceLabel":18890,"sourceYear":921,"table":5342,"note":18915,"datasets":18916,"metrics":18917,"families":18918,"methods":18919,"methodIds":18921,"rows":63,"failures":30},"zlot-bosse2014-mine-text-sec-4-4","Open-loop drift from segment-wise comparison with the survey-registered trajectory (segments of 10-150 m aligned at the same start time); bias values…",[18893],[23],[23],[18920],"Open-loop trajectory (non-rigid registration with IMU)",[18889],{"slug":18923,"sourceId":18924,"sourceLabel":18925,"sourceYear":306,"table":325,"note":18926,"datasets":18927,"metrics":18929,"families":18930,"methods":18931,"methodIds":18933,"rows":618,"failures":30},"zou2022lidarslam-indoor-table-ii","zou2022lidarslam_indoor","Zou et al., 2022","Exp. I in-place rotation, six turns (ground truth 6 x 360 deg); offline replay of recorded data; signed orientation error",[18928],"authors' own indoor recordings (rosbag)",[23],[23],[14377,18932,461,5119],"EKF (wheel odometry + IMU)",[2118],{"slug":18935,"sourceId":18924,"sourceLabel":18925,"sourceYear":306,"table":279,"note":18936,"datasets":18937,"metrics":18938,"families":18939,"methods":18940,"methodIds":18942,"rows":1042,"failures":30},"zou2022lidarslam-indoor-table-iii","Exp. I straight line P1 to P3, ground-truth end point (18.007, 0); start point set as origin; three runs; error (m) as listed (signed); AMCL uses a Ca…",[18928],[1551],[1553],[14377,4759,10453,18941,461],"Gmapping",[2116,4972,2118],{"slug":18944,"sourceId":18924,"sourceLabel":18925,"sourceYear":306,"table":731,"note":18945,"datasets":18946,"metrics":18947,"families":18948,"methods":18949,"methodIds":18950,"rows":641,"failures":30},"zou2022lidarslam-indoor-table-iv","Exp. 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inspection","Adaptive robust kernels","Point-to-plane ICP (Chen-Medioni)","LIO-mapping (LIOM)","4D-BIM quadruped reality capture","Robust Reconstruction of Indoor Scenes (Redwood)","Switchable Constraints","ASPAR","Metro","Cole-Newman 3D laser SLAM with an oscillating 2D scanner","maplab 2.0","Volumetric range-image integration (TSDF origin; VRIP)","Voxel Hashing","Square Root SAM","GTSAM","MCGS SLAM","MonoGS (Gaussian Splatting SLAM)","SHINE-Mapping","PUMA","Efficient ICP variants","MRS continuous-time surfel SLAM (Droeschel and Behnke)","DUFOMap","Dynablox","DARE-SLAM","Closed-form preintegration","On-manifold IMU preintegration","LiDAR sync by GNSS-clock emulation","Multi-sensor fusion SLAM for bridge crack inspection","Integrated LiDAR SLAM for public-building sites","MAD-ICP","Kalibr (unified temporal-spatial calibration)","Temporal basis functions (continuous-time batch)","Quadruped decoupled mapping","In situ Fabricator BIM-referenced localization","Geometrically stable 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