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GHz Pentium III class machine",true,[],[],1,2004,[26],[27,28,29,30,31],"nister2004vo",3,null,"all processing at video rates","Sec. 1",{"c":33,"m":34,"d":20,"f":35,"v":36,"n":23,"y":37,"u":38},"other","1 x 1 m white coated plywood board on tripod with goniometer",[],[],2011,[39],[40,41,29,42,43],"soudarissanane2011scanninggeometry",2,"rotatable horizontally with 2° precision; considered almost Lambertian","Sec. 4, Fig. 4",{"c":33,"m":45,"d":46,"f":47,"v":48,"n":23,"y":49,"u":50},"1-DoF gimbal (simulated)",false,[],[],2021,[51],[52,53,54,55,56],"rloam2021",0,"R-LOAM simulated datasets","tilts the LiDAR back and forth between -0.6 and +0.6 rad throughout the flight","Sec. IV-A, Fig. 4",{"c":18,"m":58,"d":20,"f":59,"v":60,"n":23,"y":61,"u":62},"1.4 GHz machine",[],[],2003,[63],[64,28,29,65,66],"biber2003ndt","Java implementation","Sec. VI, VIII",{"c":18,"m":68,"d":20,"f":69,"v":70,"n":23,"y":71,"u":72},"1.6 GHz Pentium M",[],[],2007,[73],[74,28,29,75,76],"monoslam2007","typical 19 ms processing per frame at 30 Hz","Sec. 6.2",{"c":18,"m":78,"d":20,"f":79,"v":80,"n":23,"y":61,"u":81},"1.8GHz Pentium IV PC",[],[],[82],[83,28,29,84,85],"hahnel2003_gridfastslam","768 MB main memory","Sec. IV.B",{"c":18,"m":87,"d":20,"f":88,"v":89,"n":23,"y":49,"u":90},"1080Ti graphics card",[],[],[91],[92,28,29,93,94],"droidslam2021","sufficient for all TUM-RGBD results","Sec. 4 (Timing and Memory)",{"c":18,"m":96,"d":20,"f":97,"v":99,"n":23,"y":100,"u":101},"11th Gen Intel Core i7-11800H",[98],"Intel",[],2024,[102],[103,28,104,105,106],"loopyslam2024","TUM-RGBD","CPU used for the 12 s per registration timing","App. E",{"c":108,"m":109,"d":46,"f":110,"v":111,"n":23,"y":100,"u":112},"lidar","128-channel LiDAR (model not reported)",[],[],[113],[114,23,115,116,117],"dufomap2024","DOALS","highly dynamic train station","Sec. IV-A, Sec. V-B1, Fig. 6",{"c":18,"m":119,"d":20,"f":120,"v":121,"n":23,"y":122,"u":123},"12th Gen Intel Core i9-12900KF",[98],[],2023,[124],[125,28,29,126,127],"dynbench2023","desktop computer; 24 cores","Sec. V",{"c":18,"m":129,"d":20,"f":130,"v":131,"n":23,"y":132,"u":133},"13th Gen Intel Core i9-13900HX (personal laptop)",[98],[],2025,[134],[135,28,29,136,137],"fastlivo2rc2025","x86 platform","Sec. V-A3",{"c":33,"m":139,"d":20,"f":140,"v":141,"n":23,"y":142,"u":143},"16 ground-truth markers with measured inter-marker distances and a tripod",[],[],2012,[144],[145,41,29,146,147],"rgbdmapping2012","consecutive marker distances 3 to 5.5 m; camera placed on a tripod at each marker","Sec. 4.2",{"c":108,"m":149,"d":46,"f":150,"v":151,"n":23,"y":152,"u":153},"16 scan-line LiDAR (model not reported)",[],[],2019,[154],[155,53,29,156,157],"vilslam2019","3D LiDAR on the custom platform","Sec. VIII-A",{"c":108,"m":159,"d":46,"f":160,"v":161,"n":23,"y":122,"u":162},"16-beam LiDAR",[],[],[163],[164,53,165,166,167],"clic2023","LVI-SAM dataset","10 Hz; camera 20 Hz; IMU 500 Hz; handheld and Jackal platforms","Sec. VI-A",{"c":108,"m":169,"d":46,"f":170,"v":171,"n":23,"y":122,"u":172},"16-beam LiDAR (YQ and Vicon Room rig)",[],[],[173],[164,53,174,175,176],"CLIC YQ and Vicon Room datasets (authors)","10 Hz; camera 20 Hz; IMU 400 Hz; rig mounted on an electric car (YQ) or handheld (Vicon Room)","Sec. VI-A; Fig. 6",{"c":108,"m":178,"d":20,"f":179,"v":181,"n":23,"y":122,"u":182},"16-beam Ouster (two units)",[180],"Ouster",[],[183],[164,53,184,185,167],"NTU VIRAL","10 Hz",{"c":108,"m":187,"d":46,"f":188,"v":189,"n":23,"y":100,"u":190},"16-channel LiDAR (model not named)",[],[],[191],[192,23,184,193,194],"lee2024lidarodom_survey","two units on a drone","Sec. 7.1, Table 3",{"c":108,"m":196,"d":46,"f":197,"v":198,"n":23,"y":122,"u":199},"16-channel lidar (simulated)",[],[],[200],[201,53,202,203,204],"balm2_2023","authors' simulation","28,800 points per scan, 100 scans along a 92 m rectangular trajectory in a 30 m x 20 m x 8 m semi-closed space","Sec. V, Fig. 5",{"c":108,"m":206,"d":20,"f":207,"v":208,"n":41,"y":100,"u":209},"16-channel OS1 gen1",[180],[],[210,215],[211,23,212,213,214],"srlivo2024","NTU-VIRAL","horizontal 16-channel spinning LiDAR with internal IMU","Sec. VI",[216,23,212,217,157],"fastlivo2_2025","10 Hz; built-in IMU at 100 Hz",{"c":108,"m":219,"d":20,"f":220,"v":222,"n":23,"y":132,"u":223},"16-channel Velodyne (two units)",[221],"Velodyne",[],[224],[225,53,226,227,228],"mins2025","KAIST Urban","10 Hz each; merged into a synthetic 20 Hz LiDAR for single-LiDAR baselines","Sec. 7",{"c":108,"m":230,"d":20,"f":231,"v":232,"n":23,"y":233,"u":234},"16-line Velodyne LiDAR",[221],[],2022,[235],[236,53,29,237,238],"zou2022lidarslam_indoor","range 100 m, precision +\u002F-3 cm; vertical FOV -15.0 to +15.0 deg at 2.0 deg; horizontal FOV 360 deg at 0.1 to 0.4 deg; rotation 5 to 20 Hz; set to 0.4 deg and 10 Hz. Same sensor on all three vehicles","Sec. IV-A; Sec. IV-C; Sec. IV-D",{"c":18,"m":240,"d":20,"f":241,"v":242,"n":23,"y":61,"u":243},"1GHz Pentium PC",[],[],[244],[245,28,29,246,247],"fastslam2_2003","1 GHz","Sec. 6 (runtime table)",{"c":18,"m":249,"d":20,"f":250,"v":251,"n":23,"y":252,"u":253},"2 GHz Pentium 4 workstation running Linux",[],[],2006,[254],[255,28,29,256,257],"dellaert2006sqrtsam","MATLAB simulations","Sec. 7.1",{"c":18,"m":259,"d":20,"f":260,"v":261,"n":23,"y":252,"u":262},"2 GHz Pentium-M based laptop",[],[],[263],[255,28,29,264,265],"processed the entire real sequence in 11 min 10 s","Sec. 8",{"c":33,"m":267,"d":20,"f":268,"v":269,"n":23,"y":270,"u":271},"2 m long straightedge and precision steel rule on a chalk-line 2 m grid",[],[],2014,[272],[273,41,29,274,275],"bosche2014flatness","manual measurement at the same straightedge positions as the Grid-Square layout","Sec. 7.2",{"c":108,"m":277,"d":20,"f":278,"v":279,"n":23,"y":132,"u":280},"2 x OS1-16 (Table 1); text names two Velodyne VLP-16",[],[],[281],[282,23,212,283,284],"molalo2025","one horizontal and one vertical LiDAR on a drone; motion capture ground truth","Table 1; Sec. 4.5",{"c":18,"m":286,"d":20,"f":287,"v":288,"n":23,"y":289,"u":290},"2.2 GHz Core i7 (ICP timings from Pomerleau et al. [20])",[],[],2015,[291],[292,28,29,293,294],"magnusson2015beyondpoints","used for the Plane-ICP baseline timings","Sec. V-C",{"c":18,"m":296,"d":20,"f":297,"v":298,"n":23,"y":299,"u":300},"2.2 GHz Intel Core i7",[98],[],2013,[301],[302,28,29,303,304],"pomerleau2013comparing","baseline registrations on one core without GPU, four tests in parallel","Sec. 5.2.4",{"c":18,"m":306,"d":20,"f":307,"v":308,"n":23,"y":61,"u":309},"2.4 GHz Pentium 4",[],[],[310],[311,28,29,312,313],"dpslam2003","fast PC used for offline processing of the logged data","Sec. 4",{"c":33,"m":315,"d":46,"f":316,"v":317,"n":23,"y":318,"u":319},"2.4 GHz Wi-Fi RSSI recording (receiver not stated)",[],[],2020,[320],[321,23,322,323,324],"rogers2020subttunnel","SubT-Tunnel","RSSI to all available 2.4 GHz hotspots","Sec. II-A",{"c":18,"m":326,"d":20,"f":327,"v":328,"n":23,"y":233,"u":329},"2.5GHz CPU and 6GB RAM system",[],[],[330],[236,28,29,331,332],"Ubuntu 14.04, ROS Indigo, no GPU used","Sec. IV-A",{"c":33,"m":334,"d":20,"f":335,"v":336,"n":23,"y":270,"u":337},"20 blue LED lights at accurately known positions",[],[],[338],[339,41,29,340,341],"li2014onlinetemporal","mapped visual features and position reference in the lab","Sec. 7.1.1, 7.1.2",{"c":18,"m":343,"d":46,"f":344,"v":345,"n":23,"y":346,"u":347},"20 dedicated workstations (model not reported)",[],[],2018,[348],[349,28,29,350,351],"dso2018","non-real-time sequentialised evaluation runs","Sec. 4 Methodology",{"c":353,"m":354,"d":20,"f":355,"v":356,"n":23,"y":357,"u":358},"wheel_or_leg_odometry","2000 points encoders",[],[],2010,[359],[360,53,29,361,362],"tinyslam2010","on the two free-rotating odometry wheels","Fig. 2 caption",{"c":18,"m":364,"d":20,"f":365,"v":366,"n":23,"y":132,"u":367},"28-core Intel i7 CPU",[98],[],[368],[369,28,29,370,332],"wang2025planarmesh","no GPU acceleration; PlanarMesh used all cores, baselines a single core",{"c":372,"m":373,"d":20,"f":374,"v":375,"n":23,"y":233,"u":376},"platform","280 mm wheelbase quadrotor UAV",[],[],[377],[378,53,29,379,380],"fastlio2_2022","forward-looking Livox Avia, indoor aggressive flight","Fig. 6(a); Sec. VII-A",{"c":108,"m":382,"d":46,"f":383,"v":384,"n":23,"y":385,"u":386},"2D laser range finders, forward-looking and upward-pointed (model not reported)",[],[],2000,[387],[388,53,29,389,390],"thrun2000_3dmapping","two lasers on the 3D-mapping Pioneer: the forward-looking one for 2D mapping and localization, the upward-pointed one for 3D data","Sec. 2.6; Fig. 1b caption",{"c":108,"m":392,"d":46,"f":393,"v":394,"n":23,"y":299,"u":395},"2D laser scanner rolling about a horizontal axis (model not stated)",[],[],[396],[302,53,29,397,398],"stop-and-go 3D scans in front of the robot","Sec. 5.1",{"c":108,"m":400,"d":46,"f":401,"v":402,"n":23,"y":132,"u":403},"2D LiDAR ('e.g., RPLIDAR'; exact model not reported)",[],[],[404],[405,53,29,406,407],"gan2025decoupled","scans at a fixed elevation; data via Ethernet to the system-on-module; used by ROS2 Gmapping","Sec. 3.1, 3.2, 3.4",{"c":108,"m":409,"d":46,"f":410,"v":411,"n":23,"y":152,"u":412},"2D LiDAR, horizontal (model not reported)",[],[],[413],[414,53,29,415,416],"ibrahim2019bimugv","longer range, 16 m; builds the 2D occupancy grid for Hector SLAM navigation","Collection Platform",{"c":108,"m":418,"d":46,"f":419,"v":420,"n":23,"y":152,"u":421},"2D LiDAR, vertical (model not reported)",[],[],[422],[414,53,29,423,424],"shorter range, 10 m; cross-section scans assembled into the 3D point cloud","Collection Platform, Fig. 2",{"c":108,"m":426,"d":46,"f":427,"v":428,"n":23,"y":152,"u":429},"2D line laser scanner, horizontally mounted (model not reported)",[],[],[430],[431,53,29,432,433],"kim2019uavassisted","estimates robot location and pose in 2D (Hector SLAM)","Sec. 4.5; Sec. 5",{"c":108,"m":435,"d":46,"f":436,"v":437,"n":23,"y":152,"u":438},"2D line laser scanners, 4 vertically mounted (model not reported)",[],[],[439],[431,53,29,440,441],"60 m working range at 50 Hz scan speed, 190 deg vertical line","Sec. 5",{"c":108,"m":443,"d":20,"f":444,"v":445,"n":23,"y":61,"u":446},"2D safety laser scanners",[],[],[447],[448,53,29,449,450],"surmann2003_kurt3d","two units, front and rear, 180 deg horizontal plane each; used as bumper substitutes and for dynamic collision avoidance","Sec. 2.1; Sec. 5.3",{"c":33,"m":452,"d":20,"f":453,"v":454,"n":23,"y":455,"u":456},"3 m ball bar with two 6 in. SMRs (spherically mounted retroreflectors)",[],[],2009,[457],[458,41,29,459,460],"gsa2009bimguide03","artefact of known length placed in the area of interest and scanned with the scene settings; about 10 ft long, generally smaller than the structures","Sec. 3.2.1, Fig. 21",{"c":18,"m":462,"d":20,"f":463,"v":464,"n":23,"y":142,"u":465},"3.2 GHz Intel Xeon CPU",[98],[],[466],[467,28,29,468,469],"zebedee2012","MATLAB with C++ MEX; open-loop at about 62 to 73 % of acquisition time","Sec. IV-B",{"c":18,"m":471,"d":20,"f":472,"v":473,"n":23,"y":455,"u":474},"3.2 GHz Pentium 4 processor (MATLAB)",[],[],[475],[476,28,29,477,478],"bosse_zlot2009_ctscan","about 5 s of processing per 1 s of data","Sec. IV",{"c":18,"m":480,"d":20,"f":481,"v":482,"n":23,"y":289,"u":483},"3.40 GHz Intel Core i7",[98],[],[484],[292,28,29,485,294],"used for MUMC",{"c":18,"m":487,"d":46,"f":488,"v":489,"n":23,"y":346,"u":490},"3.5 GHz CPU (model not reported)",[],[],[491],[492,28,29,493,214],"limo2018","Liviodo on 2 cores at 10 Hz, LIMO on 4 cores at 5 Hz",{"c":18,"m":495,"d":20,"f":496,"v":497,"n":23,"y":49,"u":498},"3090 GPU (x2)",[],[],[499],[92,28,29,500,94],"real-time configuration: tracking and local BA on the first GPU, global BA and loop closure on the second",{"c":108,"m":502,"d":46,"f":503,"v":504,"n":23,"y":100,"u":505},"32-beam LiDAR (model not named)",[],[],[506],[507,23,508,509,510],"pinslam2024","Nebula","carried by a Spot1 robot moving back and forth in the Valentine Cave","Fig. 7",{"c":108,"m":512,"d":46,"f":513,"v":514,"n":23,"y":233,"u":515},"32-beam Velodyne LiDAR scanner (model not named)",[221],[],[516],[517,23,518,519,520],"vizzo2022vdbfusion","nuScenes","car roof; Boston and Singapore; scene-0061; qualitative result","Sec. 5.6.3",{"c":522,"m":523,"d":20,"f":524,"v":525,"n":23,"y":132,"u":526},"camera","360 deg panoramic imaging unit (two 1-inch CMOS sensors) of OmniSLAM R6",[],[],[527],[528,53,29,529,530],"qin2025backpackmetro","panoramic lenses; used to colourize the point cloud","Sec. 3.1",{"c":33,"m":532,"d":46,"f":533,"v":534,"n":23,"y":132,"u":535},"360-degree prism (model not_reported)",[],[],[536],[537,41,29,538,539],"schillberg2025quadrupedasbuilt","mounted directly above the LiDAR; may shift sideways by about 3-5 cm when the robot tilts on stairs","Hardware Setup; Results",{"c":108,"m":541,"d":46,"f":542,"v":544,"n":23,"y":71,"u":545},"3D laser range finder built from a SICK 2D laser range finder on a servo-driven pitch mount (SICK model not stated)",[543],"SICK (2D scanner); mount by the authors",[],[546],[547,53,29,548,549],"nuchter2007_6dslam","scans up to 180 deg (h) x 120 deg (v); horizontal resolutions 181, 361, 721 and vertical 128, 225, 420, 500; a 181-point plane takes 13 ms; a 181 x 256 scan takes 3.4 s; stop-scan-go acquisition","Sec. 5.1, Fig. 8",{"c":108,"m":551,"d":46,"f":552,"v":553,"n":23,"y":554,"u":555},"3D laser scanner (model not stated; scans provided courtesy of RIEGL LMS GmbH)",[],[],2008,[556],[557,23,558,559,560],"borrmann2008_6dlum","Horn (Austria) main square","13 high-resolution 3D scans of 240,000 to 300,000 points each; instrument type and model not stated (static terrestrial scanning is an inference from the stop-and-scan formulation and target-based reference)","Sec. 7.1; Fig. 2-3 captions; ref. [26]",{"c":108,"m":562,"d":46,"f":563,"v":564,"n":23,"y":100,"u":565},"3D LiDAR (dataset sensors; models not stated)",[],[],[566],[567,23,568,569,570],"lioekf2024","UrbanNav, M2DGR and Newer College","not_reported","Sec. III; Sec. IV-A",{"c":108,"m":572,"d":46,"f":573,"v":574,"n":23,"y":49,"u":575},"3D LiDAR (KITTI odometry scans; model not named in the paper)",[],[],[576],[577,23,578,569,332],"chebrolu2021adaptive","KITTI odometry",{"c":108,"m":580,"d":46,"f":581,"v":582,"n":23,"y":233,"u":583},"3D LiDAR (KITTI; model not named in the paper)",[],[],[584],[585,23,586,587,588],"artslam2022","KITTI odometry and KITTI raw","point clouds of about 130 K points at about 10 Hz","Sec. III; Sec. III-A",{"c":108,"m":590,"d":46,"f":591,"v":592,"n":41,"y":49,"u":593},"3D LiDAR (model not named in the paper)",[],[],[594,598],[595,23,596,569,597],"erasor2021","SemanticKITTI","Sec. I, Sec. III-A",[599,23,600,601,602],"r3livepp2024","NCLT","10 Hz, about 695k points per second","VoR Sec. VI-A; Sec. VI-G",{"c":108,"m":604,"d":46,"f":605,"v":606,"n":41,"y":49,"u":608},"3D LiDAR (model not reported)",[],[607],"3D LIDAR (model not reported)",[609,614],[610,53,611,612,613],"affan2026semanticmeshing","Oxford Spires; NTU VIRAL","maximum range about 150 m stated for the indoor LiDAR setup","Sec. 3; Sec. 5.2.2",[615,53,29,616,478],"moura2021bimslam","on the ground mobile robot used in the validation tests",{"c":108,"m":618,"d":46,"f":619,"v":620,"n":41,"y":49,"u":621},"3D LiDAR (model not stated)",[],[],[622,626],[623,53,29,624,625],"rflio2021","LiDAR scans at 10 Hz (Fig. 2)","Fig. 2; Sec. IV-A",[627,53,29,628,629],"lion2021","LiDAR odometry computed at 10 Hz","Sec. 2; Sec. 3.1",{"c":108,"m":631,"d":46,"f":632,"v":633,"n":23,"y":318,"u":634},"3D LiDAR of the KITTI raw recordings (model not named in the paper)",[],[],[635],[636,23,637,638,639],"lol2020","KITTI raw drives 18, 27, 28","raw drives 18 (about 2200 m), 27 (about 3660 m) and 28 (about 4125 m)","Sec. IV; Fig. 2",{"c":108,"m":641,"d":46,"f":642,"v":643,"n":23,"y":100,"u":644},"3D solid-state LiDAR (model not stated in paper)",[],[],[645],[646,23,647,648,649],"lee2024conpr","ConPR","3D solid-state LiDAR on the handheld system; LiDAR-IMU extrinsics calibrated with the method of ref. [12] (robust real-time LiDAR-inertial initialization)","Sec. II-A, Sec. II-B, Fig. 2",{"c":651,"m":652,"d":20,"f":653,"v":654,"n":23,"y":132,"u":655},"gnss","3DM RTK INS",[],[],[656],[657,23,658,659,660],"hu2025mapeval","MS-dataset (authors, self-collected)","RTK-aided inertial navigation system on the multi-sensor platform; model not reported","Fig. 3(a)",{"c":662,"m":663,"d":20,"f":664,"v":665,"n":23,"y":233,"u":666},"imu","3DM-CV5",[],[],[667],[668,53,669,670,671],"wildcat2022","DARPA SubT Final Event; QCAT (SpinningPack)","9-DoF, angular velocity and linear acceleration at 100 Hz (SpinningPack); FlatPack IMU model not stated","Sec. VI-A1",{"c":651,"m":673,"d":20,"f":674,"v":675,"n":23,"y":132,"u":676},"3DM-GQ7-GNSS\u002FINS",[],[],[677],[678,41,679,680,681],"wei2025fusionportablev2","FusionPortableV2","dual-antenna RTK-enabled INS; raw GNSS 2 Hz; pose output up to 30 Hz (odometry topic 10 Hz, IMU 200 Hz); RTK-fixed positioning accuracy up to 1.4 cm; 1 to 3 min initialization outdoors","Table 2; Sec. 3.3.3",{"c":662,"m":683,"d":20,"f":684,"v":685,"n":23,"y":318,"u":686},"3DM-GX4-25",[],[],[687],[688,53,689,690,691],"pronto2020","Valkyrie","500 Hz","Table 1",{"c":18,"m":693,"d":46,"f":694,"v":695,"n":23,"y":152,"u":696},"4 GHz computer with 4 physical cores (model not reported)",[],[],[697],[155,28,29,698,157],"onboard computer of the custom platform",{"c":18,"m":700,"d":20,"f":701,"v":703,"n":23,"y":318,"u":704},"4 NVIDIA V100 GPUs",[702],"NVIDIA",[],[705],[706,28,29,707,708],"nerf2020","needed to run the SRN baseline at 512 x 512","App. B",{"c":18,"m":710,"d":20,"f":711,"v":712,"n":23,"y":233,"u":713},"4-core Intel i7 1.30 GHz CPU",[98],[],[714],[715,28,29,716,717],"dlo2022","4-core, 1.30 GHz","Sec. III-A",{"c":18,"m":719,"d":20,"f":720,"v":721,"n":23,"y":122,"u":723},"4-GPU A100 node",[],[722],"4-GPU A100 node (as written)",[724],[725,28,29,726,727],"kerbl2023_3dgs","used to train the Mip-NeRF360 baseline for 12 h","Sec. 7.2, footnote 2",{"c":18,"m":729,"d":20,"f":730,"v":731,"n":23,"y":61,"u":732},"400MHz Pentium II",[],[],[733],[734,28,29,735,441],"gelfand2003stable","400 MHz",{"c":522,"m":737,"d":46,"f":738,"v":739,"n":23,"y":233,"u":740},"4Seasons visual-inertial sensor (model not reported)",[],[],[741],[742,23,743,744,745],"dmvio2022","4Seasons","well time-synchronised; bottom 96 pixels cropped because of the car hood; IMU noise read from the data-sheet Allan variance plot","Sec. IV-C",{"c":372,"m":747,"d":46,"f":748,"v":749,"n":23,"y":233,"u":750},"4WD moving platform (model not reported)",[],[],[751],[752,53,29,753,754],"faizullin2022lidarsync","carries IMU, MCU board, laptop and other equipment inside its body","Fig. 1(b)",{"c":18,"m":756,"d":20,"f":757,"v":758,"n":23,"y":759,"u":760},"550 MHz Pentium III Xeon",[],[],2001,[761],[762,28,29,763,764],"rusinkiewicz2001variants","C++ implementation used for all reported running times","Sec. 2",{"c":522,"m":766,"d":20,"f":767,"v":768,"n":23,"y":318,"u":769},"5D Mark II",[],[],[770],[771,23,772,773,774],"dong2020tlsreview","WHU-TLS (heritage building)","11 images","Sec. 4.9",{"c":33,"m":776,"d":20,"f":777,"v":778,"n":23,"y":233,"u":779},"5G mobile router and network switch",[],[],[780],[781,53,29,782,783],"kim2022scaffoldrobotdog","SSH link from user laptop to on-board computer; UDP link from switch to motor controller board","Sec. 3.1; Fig. 2(b)",{"c":108,"m":785,"d":46,"f":786,"v":787,"n":41,"y":132,"u":788},"64-beam LiDAR (model not named)",[],[],[789,794],[790,23,791,792,793],"pings2025","Oxford Spires","on a handheld system","Sec. IV-A1",[369,23,791,795,796],"beam count given only in the abstract, which ties the about 2 Hz rate to a 64-beam sensor; model not named; experiments used undistorted scans with ground-truth poses from registration to the TLS map (Sec. IV-A)","Abstract; Sec. III-A; Sec. IV-A",{"c":108,"m":798,"d":46,"f":799,"v":800,"n":41,"y":122,"u":801},"64-beam LiDAR (simulated, noise-free)",[],[],[802,806],[803,23,804,805,398],"nerfloam2023","MaiCity","synthetic scans with a provided ground-truth map",[807,23,804,808,332],"shinemapping2023","Synthetic scans of an urban scenario",{"c":108,"m":810,"d":46,"f":811,"v":812,"n":23,"y":233,"u":813},"64-beam Ouster sensor (model not named)",[180],[],[814],[517,23,815,816,817],"Newer College","hand-held device through New College, Oxford; only LiDAR data used; qualitative result","Sec. 5.6.2",{"c":108,"m":819,"d":20,"f":820,"v":821,"n":23,"y":122,"u":822},"64-beam Ouster with internal IMU",[180],[],[823],[164,53,824,825,167],"Newer College Dataset","LiDAR 10 Hz; internal IMU 100 Hz; stereo camera 30 Hz; handheld device",{"c":108,"m":827,"d":46,"f":828,"v":829,"n":23,"y":233,"u":830},"64-beam rotating Velodyne LiDAR (exact model not named)",[221],[],[831],[517,23,832,833,834],"KITTI Odometry","mounted on a car roof; experiments used ranges 2-70 m and 10 cm voxels","Sec. 5; Sec. 5.6.1",{"c":108,"m":836,"d":46,"f":837,"v":838,"n":23,"y":132,"u":839},"64-channel LiDAR sensor (model not named in the paper)",[],[],[840,845],[841,23,842,843,844],"lim2025kissmatcher","KITTI","laser ray pattern differs from MulRan's","Sec. IV-D; Fig. 5 caption",[841,23,846,847,844],"MulRan","laser ray pattern differs from KITTI's",{"c":108,"m":849,"d":20,"f":850,"v":851,"n":23,"y":132,"u":852},"64-channel Ouster",[180],[],[853],[225,53,854,185,228],"UD Husky dataset",{"c":372,"m":856,"d":20,"f":857,"v":858,"n":23,"y":289,"u":859},"7 m monohull watercraft (stand-in for the Lizhbeth vessel)",[],[],[860],[861,53,29,862,863],"pomerleau2015review","no external sensors used; scans registered without pre-alignment","Sec. 3.3",{"c":18,"m":865,"d":20,"f":866,"v":867,"n":23,"y":385,"u":868},"700 MHz Pentium III",[98],[],[869],[870,28,29,871,265],"pfister2000surfels","256 MB SDRAM; unoptimized C",{"c":372,"m":873,"d":20,"f":874,"v":876,"n":23,"y":233,"u":877},"750 mm wheelbase quadrotor UAV (developed by Ambit-Geospatial)",[875],"Ambit-Geospatial",[],[878],[378,53,29,879,880],"down-facing Livox Avia, GPS-navigated waypoint flight","Fig. 6(c); Sec. VII-A; Sec. VII-C",{"c":882,"m":883,"d":20,"f":884,"v":885,"n":23,"y":759,"u":886},"radar","77 GHz FMCW millimetre-wave radar (MMWR)",[],[],[887],[888,53,29,889,332],"dissanayake2001","beam scanned 360 deg in azimuth at 1 to 3 Hz; amplitude returns at about 1.5 deg angular increments, thresholded to range and bearing; range to 250 m with 10 cm range and 1.5 deg bearing resolution; dual-polarisation receiver",{"c":33,"m":891,"d":20,"f":892,"v":893,"n":23,"y":318,"u":894},"8000 mAh LiPo battery",[],[],[895],[896,23,815,897,898],"ramezani2020newercollege","power supply of the handheld device","Sec. III",{"c":662,"m":900,"d":20,"f":901,"v":902,"n":23,"y":100,"u":903},"9-axis IMU (external IMU)",[],[],[904],[905,23,184,906,907],"trajlo2024","385 Hz; used only by the LIO baselines","Sec. IV-A; Table II footnote",{"c":33,"m":909,"d":20,"f":910,"v":912,"n":23,"y":71,"u":913},"900 mm Leica scale bar",[911],"Leica",[],[914],[915,41,29,916,917],"lichti2007amcw","sigma +\u002F-0.002 mm; scale definition of the check-point network","Sec. 4.1",{"c":108,"m":919,"d":20,"f":920,"v":921,"n":23,"y":318,"u":922},"a different version of the Velodyne HDL-64E",[221],[],[923],[924,23,925,569,478],"overlapnet2020","Ford Campus",{"c":18,"m":927,"d":20,"f":928,"v":929,"n":23,"y":100,"u":930},"A100",[702],[],[931],[932,28,29,933,934],"nicerslam2024","single GPU; 496 ms per mapping iteration, 147 ms per tracking iteration","arXiv v1 Sec. 3.4 (implementation details are not in the 3DV main text)",{"c":18,"m":936,"d":20,"f":937,"v":938,"n":23,"y":122,"u":940},"A6000 GPU",[],[939],"A6000 GPU (as written)",[941],[725,28,29,942,275],"used for all reported results except the Mip-NeRF360 baseline",{"c":944,"m":945,"d":20,"f":946,"v":948,"n":23,"y":318,"u":949},"mobile_scanner_device","AC2",[947],"Heron (Table 4 lists AC2-color under Heron)",[],[950],[951,952,29,953,954],"otero2020mobileindoormapping",4,"backpack with low-cost 3D LiDAR; 7.4 kg; 695,000-1.39 million pts\u002Fs; relative accuracy 2 cm; 3 h","Sec. 2.2.2; Tables 1-3, 6; Fig. 5b",{"c":944,"m":956,"d":20,"f":957,"v":959,"n":23,"y":318,"u":960},"AC2-color",[958],"Heron",[],[961],[951,952,29,962,963],"AC2 with one camera (2 Mp, 360° x 58°, 60 fps); 7.4 kg; 695,000-1.39 million pts\u002Fs; relative accuracy 2 cm; 2 h","Sec. 2.2.2; Tables 1-6; Fig. 5c",{"c":372,"m":965,"d":20,"f":966,"v":968,"n":23,"y":357,"u":969},"ActivMedia Pioneer 2",[967],"ActivMedia",[],[970],[971,23,972,973,974],"grisetti2010tutorial","Intel Research Lab","equipped with a SICK-LMS range finder; recorded odometry and 2D laser data at the Intel Research Laboratory","Fig. 8, Sec. V-A",{"c":372,"m":976,"d":20,"f":977,"v":978,"n":23,"y":71,"u":979},"ActivMedia Pioneer 2 AT",[967],[],[980],[981,53,29,982,983],"gmapping2007","equipped with SICK LMS or PLS laser range finders","Sec. VI, Fig. 3",{"c":372,"m":985,"d":20,"f":986,"v":987,"n":23,"y":142,"u":988},"ActivMedia Pioneer 3",[967],[],[989],[990,23,991,992,993],"sturm2012tum","TUM RGB-D","wheeled robot carrying a forward-looking Kinect, joysticked manually (Robot SLAM sequences)","Sec. III, Fig. 1d",{"c":372,"m":995,"d":20,"f":996,"v":997,"n":23,"y":455,"u":998},"ActivMedia Pioneer P3-AT ('Tjorven')",[967],[],[999],[1000,53,1001,1002,1003],"magnusson2009thesis","Straight, Crossing, Kvarntorp-Loop (Kvarntorp mine); Sofa-1, Sofa-2","onboard computer, wheel encoders for 2D odometry, pan\u002Ftilt SICK lidar, omnidirectional camera, differential GPS antenna","Sec. 4.1, 6.4.1, 6.4.3, 7.3.1",{"c":522,"m":1005,"d":46,"f":1006,"v":1007,"n":23,"y":1008,"u":1009},"additional backward-pointing low-resolution high-FOV camera (handheld; model not stated)",[],[],2026,[1010],[1011,53,29,1012,147],"charron2026slamcentric","for full colourization of the lidar point cloud",{"c":662,"m":1014,"d":20,"f":1015,"v":1017,"n":23,"y":122,"u":1018},"ADI ADIS16465",[1016],"ADI",[],[1019],[1020,53,1021,1022,332],"fflins2023","FF-LINS Robot dataset","industrial-grade MEMS IMU, gyroscope bias instability 2 deg\u002Fhr, 200 Hz; hardware-triggered synchronization with the LiDAR",{"c":662,"m":1024,"d":46,"f":1025,"v":1026,"n":23,"y":122,"u":1027},"ADIS IMU",[],[],[1028],[1029,23,1030,569,332],"maplab2_2023","HILTI 2021 SLAM Dataset",{"c":662,"m":1032,"d":20,"f":1033,"v":1035,"n":23,"y":233,"u":1036},"ADIS16445",[1034],"Analog Devices",[],[1037],[1038,23,1039,1040,1041],"helmberger2022hilti","Hilti SLAM Challenge Dataset (2021)","MEMS IMU rigidly mounted to the AlphaSense, relatively low noise and bias drift, 800 Hz, timestamped by the AlphaSense timing system","Sec. III-C; Table II",{"c":662,"m":1043,"d":20,"f":1044,"v":1045,"n":1047,"y":289,"u":1048},"ADIS16448",[],[1046],"ADIS 16448",9,[1049,1052,1057,1062,1066,1070,1073,1077,1082],[1050,53,29,1051,332],"rovio2015","industrial grade, angular random walk 0.66 deg\u002Fsqrt(Hz), velocity random walk 0.11 m\u002Fs\u002Fsqrt(Hz), 200 Hz",[1053,23,1054,1055,1056],"burri2016euroc","EuRoC MAV","200 Hz, MEMS, intrinsically calibrated","Table 1, Sec. 2",[1058,53,1059,1060,1061],"gvins2022","GVINS-Dataset","200 Hz; gyroscope noise density 7.0e-3 deg\u002Fs\u002Fsqrt(Hz); accelerometer noise density 6.6e-4 m\u002Fs^2\u002Fsqrt(Hz)","Table IV",[1063,23,1054,1064,1065],"eckenhoff2019closedform","MEMS IMU, 200 Hz","Sec. VII-A1",[1067,53,29,1068,1069],"forster2017preint","MEMS IMU in the VI-Sensor, 800 Hz","Sec. VIII-B2",[1071,23,1054,1064,1072],"openvins2020","Sec. V-B",[1074,53,29,1075,1076],"okvis2015","MEMS IMU recorded at 800 Hz; noise used: gyro 1.2e-3 rad\u002F(s sqrt(Hz)), accelerometer 8.0e-3 m\u002F(s^2 sqrt(Hz)), gyro bias 2.0e-5, accelerometer bias 5.5e-5 (Table I)","Sec. VII-A1, VII-A2, Table I",[1078,23,1079,1080,1081],"vinsmono2018","EuRoC","200 Hz, synchronized","Sec. IX-A-1",[1083,23,1079,1080,1084],"vinsfusion2019","Sec. V-A",{"c":662,"m":1086,"d":46,"f":1087,"v":1088,"n":23,"y":346,"u":1089},"ADIS16448 (simulated)",[],[],[1090],[1091,23,1092,1093,1094],"lips2018","LIPS simulator (extruded floor plan)","gyro noise density 0.005 rad\u002Fs\u002Fsqrt(Hz), accel noise density 0.01 m\u002Fs2\u002Fsqrt(Hz), 800 Hz","Sec. VI-A; Table I",{"c":662,"m":1096,"d":20,"f":1097,"v":1098,"n":23,"y":1008,"u":1099},"ADIS16465",[],[],[1100],[1101,23,1102,1103,1104],"palvio2026","i2Nav-Robot","200 Hz (Table I)","Table I",{"c":353,"m":1106,"d":20,"f":1107,"v":1108,"n":23,"y":318,"u":1109},"AEDA3300-BE1",[],[],[1110],[688,53,1111,1112,691],"HyQ","joint encoders, 1,000 Hz, resolution \u003C0.0045 deg; leg odometry input",{"c":522,"m":1114,"d":20,"f":1115,"v":1116,"n":23,"y":1117,"u":1118},"aerial camera with 6 in. lens",[],[],1981,[1119],[1120,53,29,1121,1122],"fischler1981ransac","image taken from approximately 4,000 ft; digitized on a 2,000 x 2,000 pixel grid, about 2 ft per pixel ground resolution","Sec. IV.E",{"c":372,"m":1124,"d":46,"f":1125,"v":1126,"n":23,"y":132,"u":1127},"aerial platform",[],[],[1128],[216,23,212,1129,157],"UAV campus flights",{"c":372,"m":1131,"d":46,"f":1132,"v":1133,"n":23,"y":132,"u":1134},"aerial robotic vehicles",[],[],[1135],[1136,23,1137,1138,717],"gslivo2025","MARS-LVIG","MARS-LVIG data collection over mountains and seas",{"c":108,"m":1140,"d":46,"f":1141,"v":1142,"n":23,"y":132,"u":1143},"Aeva",[],[],[1144],[1145,23,1146,1147,1148],"kissslam2025","HeLiPR","HeLiPR sensor with different ranging technology and scan pattern","Sec. IV-A; Table III; Table VI caption",{"c":108,"m":1150,"d":46,"f":1151,"v":1152,"n":23,"y":1008,"u":1153},"Aeva Aeries II",[1140],[],[1154],[1155,23,1146,1156,1157],"rkolio2026","low field-of-view solid-state LiDAR, one of four LiDARs in the dataset","Sec. IV-A; Sec. IV-B",{"c":372,"m":1159,"d":46,"f":1160,"v":1161,"n":23,"y":49,"u":1162},"Agile X Scout mini",[],[],[1163],[1164,53,29,1165,1166],"camvox2021","moving robot platform carrying the CamVox hardware","Sec. III-A; Fig. 3",{"c":33,"m":1168,"d":46,"f":1169,"v":1171,"n":23,"y":152,"u":1172},"Agisoft PhotoScan Professional",[1170],"Agisoft",[],[1173],[1174,41,29,1175,1176],"acharya2019bimtracker","bundle adjustment reference trajectory for real data with manually provided 3D coordinates; reprojection error 0.36 px, reconstruction error 4.65 mm","Sec. 4.3",{"c":353,"m":1178,"d":46,"f":1179,"v":1180,"n":23,"y":318,"u":1181},"AGV wheel odometer (not described)",[],[],[1182],[1183,53,29,1184,469],"iscloam2020","fused with PCL feature-based LiDAR odometry for the front-end trajectory",{"c":662,"m":1186,"d":46,"f":1187,"v":1188,"n":23,"y":122,"u":1189},"AHRS IMU (model not stated)",[],[],[1190],[1191,23,1192,569,1193],"dliom2023","Complex Urban Dataset","Sec. IV-A-2",{"c":33,"m":1195,"d":20,"f":1196,"v":1198,"n":23,"y":233,"u":1199},"AirSim (Building_99 environment)",[1197],"Microsoft",[],[1200],[1201,23,1202,1203,1204],"viralfusion2022","AirSim Building_99 (authors' simulation)","simulated sensors: 10 Hz stereo camera, two 10 Hz LiDARs, 400 Hz IMU, two UWB nodes with two antennae each, four anchors at 11 m height","Sec. VI-B",{"c":944,"m":1206,"d":20,"f":1207,"v":1208,"n":23,"y":61,"u":1209},"AIS 3D laser range finder",[],[],[1210],[448,53,29,1211,1212],"2D laser range finder on a servo-driven pitch mount; 180 deg (h) x 120 deg (v); horizontal 181, 361 or 721 and vertical 128 or 256 points; 181 x 256 scan in 3.4 s; reflectance measured; scanner 17 W, servo 0.85 W","Sec. 2.2",{"c":372,"m":1214,"d":46,"f":1215,"v":1216,"n":23,"y":122,"u":1217},"All-Terrain-Vehicle (ATV)",[],[],[1218],[1219,53,1220,1221,745],"slict2023","SLICT in-house NTU campus dataset","about 1.5 km loop, up to 30 km\u002Fh",{"c":944,"m":1223,"d":20,"f":1224,"v":1226,"n":23,"y":318,"u":1227},"ALPHA AL3-32",[1225],"Phoenix LiDAR Systems",[],[1228],[951,952,29,1229,1230],"backpack with 3D LiDAR; 3.2 kg; 700,000 pts\u002Fs; relative accuracy 3.5-5.5 cm; operating time N\u002FA; Sony A6000 camera, 24.3 Mp","Sec. 2.2.2; Tables 1-6; Fig. 5f",{"c":522,"m":1232,"d":20,"f":1233,"v":1235,"n":23,"y":233,"u":1236},"Alphasense (5-camera module)",[1234],"Sevensense",[],[1237],[1038,23,1039,1238,1239],"5 rigidly mounted wide-FoV 1.3 MP global-shutter cameras, about 270 deg continuous field of view, including one stereo pair, synchronous at 10 Hz; FPGA time sync to mid-exposure, below 1 ms to the IMUs","Sec. III-A, III-E; Fig. 1",{"c":522,"m":1241,"d":46,"f":1242,"v":1244,"n":41,"y":49,"u":1245},"Alphasense Core Development Kit",[1243],"Sevensense Robotics AG",[],[1246,1251],[1247,23,1248,1249,1250],"zhang2021ncext","Newer College multi-camera extension","four grayscale global-shutter fisheye cameras (front stereo pair, 11 cm baseline, two lateral; top camera removed), 30 Hz, 720 x 540 px, FoV 126 x 92.4 deg, about 36 deg front-side overlap; FPGA synchronizes cameras and IMU","Table I; Sec. II",[1252,23,1253,1254,1255],"zhang2023hiltioxford","Hilti-Oxford (Hilti SLAM Challenge 2022)","five grayscale global-shutter fisheye cameras (front stereo pair 11 cm baseline, two lateral, one upward), 40 Hz, 720 x 540 px, FoV 126 x 92.4 deg, about 36 deg front-side overlap; FPGA hardware sync with IMU; Kalibr calibration with 7 x 12 AprilTag target","Table I; Sec. III, III-B",{"c":522,"m":1257,"d":46,"f":1258,"v":1260,"n":23,"y":100,"u":1261},"Alphasense development kit (forward-facing monochrome camera)",[1259],"Sevensense Robotics",[],[1262],[1263,53,1264,1265,745],"hatleskog2024probdegen","Seemühle Mine dataset","0.4 MP grayscale, 20 Hz; used for ROVIO visual-inertial prior",{"c":662,"m":1267,"d":46,"f":1268,"v":1269,"n":23,"y":100,"u":1270},"Alphasense development kit IMU",[1259],[],[1271],[1263,53,1264,1272,745],"200 Hz",{"c":522,"m":1274,"d":20,"f":1275,"v":1276,"n":23,"y":122,"u":1277},"Alvium 1800 U-501 (5.0 MP NIR)",[],[],[1278],[1279,23,1280,1281,1282],"trzeciak2023conslam","ConSLAM","2592 x 1944 px near-infrared; driver at 70 Hz, synchronised to LiDAR at about 10 Hz","Introduction; Sensors and devices; Fig. 2",{"c":522,"m":1284,"d":20,"f":1285,"v":1286,"n":23,"y":122,"u":1287},"Alvium U-319c (3.2 MP RGB)",[],[],[1288],[1279,23,1280,1289,1282],"2064 x 1544 px; driver at 60 Hz, synchronised to LiDAR at about 10 Hz",{"c":522,"m":1291,"d":20,"f":1292,"v":1293,"n":23,"y":132,"u":1294},"Alvium U-319c, 3.2 MP camera",[],[],[1295],[1296,23,1280,1297,1298],"stuhrenberg2025liobim","colour camera of the ConSLAM rig; AprilTags visible in its images","Table 2; Sec. 4.6",{"c":18,"m":1300,"d":20,"f":1301,"v":1303,"n":23,"y":71,"u":1304},"AMD Athlon 1950 MHz with 512 MB memory",[1302],"AMD",[],[1305],[1306,28,29,1307,1308],"magnusson2007ndt3d","runs all timing experiments","Sec. 5 baseline",{"c":18,"m":1310,"d":20,"f":1311,"v":1312,"n":23,"y":100,"u":1313},"AMD EPYC 7742",[1302],[],[1314],[103,28,104,1315,1316],"processor used for the global-registration ablation timings","App. E, Table 9",{"c":18,"m":1318,"d":20,"f":1319,"v":1320,"n":23,"y":1008,"u":1321},"AMD EPYC 9654 96-Core Processor",[1302],[],[1322],[1323,28,29,1324,1325],"yuan2026_adaptive3dgsslam","16 cores allocated within a virtual machine, 60 GB memory, Ubuntu 20.04","Sec. 4.3, Sec. 5.2",{"c":18,"m":1327,"d":20,"f":1328,"v":1329,"n":23,"y":122,"u":1330},"AMD R7-3700X",[1302],[],[1331],[1020,28,29,1332,1333],"desktop PC, ROS, multi-threaded","Sec. IV-A; Sec. IV-D",{"c":18,"m":1335,"d":20,"f":1336,"v":1337,"n":23,"y":233,"u":1338},"AMD R7-5800X",[1302],[],[1339],[1340,28,29,1341,1342],"fasterlio2022","8 cores, 3.8 GHz desktop ('iVox AMD')","Sec. V; Fig. 1",{"c":18,"m":1344,"d":20,"f":1345,"v":1346,"n":23,"y":122,"u":1347},"AMD Ryzen 5950X",[1302],[],[1348],[1349,28,29,1350,1351],"loner2023","CPU","Sec. IV-E",{"c":18,"m":1353,"d":20,"f":1354,"v":1355,"n":41,"y":49,"u":1357},"AMD RYZEN 9 3900X",[1302],[1356],"AMD Ryzen 9 3900x",[1358,1362],[1359,28,29,1360,1361],"locus2021","12 cores, 3.8 GHz, onboard Husky","Sec. III-C1",[1363,28,29,1364,332],"yang2024lifelong","CPU of the authors' PC used for the dynamic removal timing",{"c":18,"m":1366,"d":20,"f":1367,"v":1368,"n":23,"y":100,"u":1369},"AMD Ryzen 9 5900x",[1302],[],[1370],[1371,28,1372,1373,1374],"zhao2024subtmrs","SubT-MRS","Zhong et al. (DLO, Scan-Context++) runtime platform","Table 2",{"c":18,"m":1376,"d":20,"f":1377,"v":1378,"n":23,"y":100,"u":1379},"AMD Ryzen 9 5900X CPU",[1302],[],[1380],[905,28,29,1381,1382],"all experiments; runtime and memory in Table V","VoR Sec. IV",{"c":18,"m":1384,"d":20,"f":1385,"v":1386,"n":23,"y":1008,"u":1387},"AMD Ryzen 9 9950X CPU",[1302],[],[1388],[1101,28,29,1389,1333],"desktop PC",{"c":18,"m":1391,"d":20,"f":1392,"v":1393,"n":23,"y":100,"u":1394},"AMD Ryzen Threadripper 3990X",[1302],[],[1395],[1396,28,29,1397,1398],"ebadi2024subt","CoSTAR base station, 64 cores and 128 threads at 2.9 GHz","Footnote 4 (Sec. V-C)",{"c":18,"m":1400,"d":20,"f":1401,"v":1402,"n":23,"y":233,"u":1403},"AMD Ryzen Threadripper 3990x (64 cores)",[1302],[],[1404],[1405,28,29,1406,717],"lamp2_2022","portable base-station workstation during the SubT Challenge",{"c":372,"m":1408,"d":46,"f":1409,"v":1410,"n":23,"y":1411,"u":1412},"AMOS robot",[],[],1997,[1413],[1414,23,1415,569,1416],"lu_milios1997","FAW Ulm cafeteria and corridor scans (30 scans, collected by FAW staff)","Sec. 5.2",{"c":662,"m":1418,"d":20,"f":1419,"v":1420,"n":23,"y":1421,"u":1422},"Analog Devices ADIS16448",[1034],[],2016,[1423],[1424,53,29,1425,1426],"rehder2016spatiotemporal","Setup II; 200 Hz; noise parameters from Allan variance","Sec. IV-A, IV-B",{"c":662,"m":1428,"d":20,"f":1429,"v":1430,"n":41,"y":299,"u":1431},"Analog Devices ADIS16488",[1034],[],[1432,1436],[1433,53,29,1434,1435],"furgale2013unifiedcalib","tactical grade; 200 Hz; noise parameters from Allan variance","Sec. V, Sec. V-B",[1424,53,29,1437,1426],"Setup I; 200 Hz; noise parameters from Allan variance",{"c":372,"m":1439,"d":46,"f":1440,"v":1442,"n":23,"y":100,"u":1443},"ANYbotics ANYmal C",[1441],"ANYbotics",[],[1444],[1263,53,1445,1446,1447],"Rümlang Construction Site dataset; Seemühle Mine dataset","legged robot; legged odometry used as prior in exp. 1","Sec. IV-B, IV-C",{"c":353,"m":1449,"d":46,"f":1450,"v":1451,"n":23,"y":318,"u":1452},"ANYdrive",[],[],[1453],[688,53,1454,1455,691],"ANYmal","joint encoders (resolution \u003C0.025 deg) and torque at 400 Hz; leg odometry input",{"c":353,"m":1457,"d":46,"f":1458,"v":1459,"n":23,"y":122,"u":1460},"ANYdrive joint encoder",[],[],[1461],[1462,53,29,1463,1104],"vilens2023","400 Hz, resolution \u003C 0.025 deg",{"c":353,"m":1465,"d":46,"f":1466,"v":1467,"n":23,"y":122,"u":1468},"ANYdrive torque sensor",[],[],[1469],[1462,53,29,1470,1104],"400 Hz, resolution \u003C 0.1 N m",{"c":372,"m":1454,"d":46,"f":1472,"v":1473,"n":23,"y":1008,"u":1474},[],[],[1475],[1476,53,1477,1478,1479],"holisticfusion2026","ANYmal hike, parkour and indoor missions (authors)","quadruped with IMU, LiDAR, leg kinematics and a single GNSS antenna","Table III; Sec. VI-A",{"c":372,"m":1481,"d":20,"f":1482,"v":1483,"n":23,"y":122,"u":1484},"ANYmal B300",[1441],[],[1485],[1462,53,29,1486,1487],"quadruped, 4 legs, 12 active DoF; stock and DARPA SubT-modified versions (SMR, FSC, SUB)","Sec. VI-A, Fig. 1",{"c":372,"m":1489,"d":46,"f":1490,"v":1491,"n":23,"y":132,"u":1492},"ANYmal C legged robot",[],[],[1493],[282,23,1494,1495,1496],"DARPA Subterranean final event","four robots, one sequence each","Sec. 4.8",{"c":372,"m":1498,"d":20,"f":1499,"v":1500,"n":23,"y":122,"u":1501},"ANYmal C100",[1441],[],[1502],[1462,53,29,1503,1487],"quadruped (LSM, SMM)",{"c":372,"m":1505,"d":46,"f":1506,"v":1507,"n":23,"y":132,"u":1508},"ANYmal D quadruped robot with Boxi rig",[],[],[1509],[1510,23,1511,1512,1513],"resple2025","GrandTour","71 Swiss environments, 15 km over 8 hours in the dataset","Sec. V-B3",{"c":353,"m":1515,"d":46,"f":1516,"v":1517,"n":23,"y":132,"u":1518},"ANYmal kinematic leg odometry (TSIF estimator)",[],[],[1519],[1520,53,29,1521,1072],"tuna2025informed","performs poorly on soft terrain",{"c":372,"m":1523,"d":46,"f":1524,"v":1525,"n":23,"y":132,"u":1526},"ANYmal legged robot",[],[],[1527],[1520,53,29,1528,1529],"simulated body velocities up to 0.85 m\u002Fs and 40°\u002Fs","Sec. V-A-3, V-B; Fig. 5-A",{"c":372,"m":1531,"d":46,"f":1532,"v":1533,"n":23,"y":49,"u":1534},"ANYmal quadrupedal robot",[],[],[1535],[1536,53,29,1537,1538],"nubert2021delora","learning-based locomotion controller; autonomous exploration missions of about 250 m on average in the ETH Zurich CLA basement","Sec. IV-A; Fig. 1",{"c":372,"m":1540,"d":46,"f":1541,"v":1542,"n":28,"y":233,"u":1544},"ANYmal-C",[],[1543],"ANYmal C",[1545,1548,1552],[1396,53,29,1546,1547],"i7-class processors; four ANYmal robots covered 1.75 km in the Final","Sec. IV-A, IV-C",[1549,53,29,1550,1551],"nubert2022learninglocalizability","quadrupedal robot; kinematic leg odometry fused in CompSLAM along non-localizable directions","Sec. I, VI-B",[1553,53,29,1554,1555],"tuna2024xicp","legged robot","Sec. VII-A, Fig. 6",{"c":372,"m":1557,"d":46,"f":1558,"v":1559,"n":23,"y":233,"u":1560},"Apollo autonomous vehicle",[],[],[1561],[1562,23,1563,1564,1565],"jiao2022fusionportable","FusionPortable","planar motion at constant speed; campus road sequence","Fig. 1d; Table III",{"c":662,"m":1567,"d":20,"f":1568,"v":1570,"n":23,"y":71,"u":1571},"Applanix 510",[1569],"Applanix",[],[1572],[1573,952,29,1574,691],"glennie2007rigorous","0.005 deg roll and pitch, 0.008 deg heading (typical post-processed)",{"c":662,"m":1576,"d":20,"f":1577,"v":1578,"n":23,"y":71,"u":1579},"Applanix 610",[1569],[],[1580],[1573,952,29,1581,691],"0.0025 deg roll and pitch, 0.005 deg heading (typical post-processed)",{"c":651,"m":1583,"d":46,"f":1584,"v":1585,"n":23,"y":132,"u":1586},"Applanix GNSS\u002FINS (model not stated)",[1569],[],[1587],[1588,41,1589,1590,1591],"steamlio2025","Boreas","post-processed ground truth with GPS corrections, IMU and wheel encoders; raw 200 Hz IMU extracted without bias correction for the method","Sec. V-D; Fig. 14",{"c":662,"m":1593,"d":46,"f":1594,"v":1595,"n":23,"y":132,"u":1596},"Applanix IMU (raw measurements from the GNSS\u002FINS logs)",[1569],[],[1597],[1588,23,1589,1272,1598],"Sec. V-D; Fig. 3",{"c":662,"m":1600,"d":20,"f":1601,"v":1603,"n":23,"y":299,"u":1604},"APPLANIX IMU-31",[1602],"APPLANIX",[],[1605],[1606,952,29,1607,1608],"puente2013mobilemapping","inertial unit of POS LV 510 used in RIEGL VMX-250","Sec. 3.5",{"c":651,"m":1610,"d":46,"f":1611,"v":1612,"n":23,"y":357,"u":1613},"Applanix IMU\u002FGPS",[1569],[],[1614],[1615,53,29,1616,1617],"olson2010passivesync","deployment context on the DARPA Urban Challenge vehicle; not evaluated","Sec. I",{"c":662,"m":1619,"d":20,"f":1620,"v":1621,"n":23,"y":299,"u":1622},"APPLANIX POS 520",[1602],[],[1623],[1606,53,29,1624,1625],"navigation system used for the authors' example trajectory and accuracy plots; Z accuracy poorer than X and Y","Sec. 2.1, Figs. 4 and 5",{"c":662,"m":1627,"d":20,"f":1628,"v":1629,"n":23,"y":299,"u":1630},"APPLANIX POS LV 420",[1602],[],[1631],[1606,952,29,1632,863],"tightly coupled GNSS\u002FIMU with two TRIMBLE GNSS receivers and DMI, up to 200 Hz; DGPS, RTK or POSPac post-processing",{"c":662,"m":1634,"d":20,"f":1635,"v":1636,"n":23,"y":299,"u":1637},"APPLANIX POS LV 520",[1602],[],[1638],[1606,952,29,1639,1640],"GNSS\u002FINS with two GNSS antennas (TRIMBLE receivers) and DMI, fused with LYNX LiDAR data","Sec. 3.7",{"c":944,"m":1642,"d":20,"f":1643,"v":1645,"n":23,"y":289,"u":1646},"Apple iPad Air 2",[1644],"Apple",[],[1647],[1648,28,29,1649,1650],"infinitam2015","runs the full pipeline at about 20 Hz","Sec. 1; Table 1",{"c":33,"m":1652,"d":20,"f":1653,"v":1654,"n":23,"y":233,"u":1655},"AprilTag 36h11 tags (6)",[],[],[1656],[1657,53,29,1658,1659],"kayhani2022tagvio","0.165 m x 0.165 m, letter-size paper, global pose in BIM frame known a priori","Table 3",{"c":33,"m":1661,"d":46,"f":1662,"v":1663,"n":23,"y":132,"u":1664},"AprilTag fiducial tags",[],[],[1665],[1296,53,29,1666,1667],"printed tags placed at identical locations in the building and in the BIM (Revit AprilTag family exported as IfcBuildingElementProxy)","Sec. 3.2",{"c":33,"m":1669,"d":46,"f":1670,"v":1671,"n":23,"y":152,"u":1672},"AprilTag markers (15)",[],[],[1673],[1674,41,29,1675,1676],"xu2019ogmvslam","fixed on the corridor floor; poses detected by the AprilTag algorithm","Sec. 5.1-5.2",{"c":522,"m":1678,"d":20,"f":1679,"v":1681,"n":41,"y":299,"u":1682},"Aptina MT9V034",[1680],"Aptina",[],[1683,1685],[1433,53,29,1684,1435],"global shutter image sensors (multiple) in a custom-made sensor; 20 Hz frame rate; four fixed exposure times; equidistant intrinsic model; 0.5 px isotropic landmark noise assumed",[1424,53,29,1686,1687],"WVGA global-shutter image sensor, 20 Hz, fixed exposure; single camera used in Setup I","Sec. IV-A, IV-B, Fig. 4(a)",{"c":1689,"m":1678,"d":20,"f":1690,"v":1691,"n":41,"y":346,"u":1692},"stereo_camera",[1680],[],[1693,1695],[1078,23,1079,1694,1081],"global shutter, WVGA monochrome, 20 FPS; only the left camera used",[1083,23,1079,1696,1084],"global shutter, 752x480 monochrome, 20 FPS stereo",{"c":1689,"m":1698,"d":20,"f":1699,"v":1700,"n":23,"y":1421,"u":1701},"Aptina MT9V034 (two cameras)",[1680],[],[1702],[1424,53,29,1703,1704],"both cameras used in Setup II, 20 Hz","Sec. IV-A, Fig. 4(b)",{"c":372,"m":1706,"d":46,"f":1707,"v":1708,"n":23,"y":61,"u":1709},"Ariadne robot",[],[],[1710],[448,53,29,1711,1712],"industrial DTV, about 80 cm x 60 cm, 90 cm high, payload 200 kg, up to 0.8 m\u002Fs, 250 kg, about 8 h per battery charge","Sec. 2.1",{"c":882,"m":1714,"d":20,"f":1715,"v":1717,"n":23,"y":122,"u":1718},"ARS548",[1716],"Continental",[],[1719],[1720,53,1721,1722,332],"iriom4d2023","4D iRIOM in-house radar dataset","4D imaging radar; 15 Hz; 76-77 GHz; elevation AOV +-20 deg, azimuth AOV +-60 deg; azimuth resolution 0.2 deg, elevation resolution 0.1 deg; range about 300 m; distance accuracy 0.3 m",{"c":372,"m":1724,"d":46,"f":1725,"v":1727,"n":23,"y":270,"u":1728},"ARTOR",[1726],"Black-I Robotics (LandShark base); custom modifications by RUAG Land Systems",[],[1729],[1730,53,29,1731,1342],"pomerleau2014_icpmapper","maximum speed 3.5 m\u002Fs, typically 1 m\u002Fs in crowded environments; large sensor suite",{"c":372,"m":1733,"d":46,"f":1734,"v":1736,"n":23,"y":289,"u":1737},"Artor (modified LandShark from Black-I Robotics)",[1735],"Black-I Robotics",[],[1738],[861,53,29,1739,1740],"six wheels, driven at about 1.2 m\u002Fs (maximum 4.5 m\u002Fs)","Sec. 3.1.5",{"c":33,"m":1742,"d":46,"f":1743,"v":1744,"n":41,"y":233,"u":1745},"ArUco marker board",[],[],[1746,1750],[1747,41,29,1748,1749],"r3live2022","relative pose between start and end poses","VoR Sec. VI-A1; Sec. VI-B; Sec. VI-C",[599,41,1751,1752,1753],"R3LIVE-dataset","reference pose when the device returns to the start","VoR Sec. VI-B1; Fig. 8",{"c":372,"m":1755,"d":46,"f":1756,"v":1758,"n":41,"y":1421,"u":1759},"AscTec Firefly",[1757],"AscTec",[],[1760,1763],[1053,23,1054,1761,1762],"hex-rotor MAV; VI sensor mounted front-down looking","Sec. 1-2, Fig. 1",[1764,53,29,1765,1766],"oleynikova2017voxblox","MAV; all estimation, mapping, planning and control on board","Sec. VII, Fig. 1",{"c":372,"m":1768,"d":46,"f":1769,"v":1770,"n":23,"y":289,"u":1771},"AscTec Firefly (three micro-helicopters)",[1757],[],[1772],[861,53,29,1773,1774],"collaborative mapping over a collapsed-building training site","Sec. 3.1.4",{"c":1776,"m":1777,"d":46,"f":1778,"v":1780,"n":41,"y":299,"u":1781},"rgbd","Asus Xtion",[1779],"Asus",[],[1782,1785],[1783,53,29,1784,717],"orbslam2_2017","structured-light projector to infrared camera baseline approximated to 8 cm",[1786,53,29,1787,1788],"voxelhashing2013","RGB-D data at 30 Hz; used for the scenes in Fig. 10","Sec. 9",{"c":1776,"m":1790,"d":46,"f":1791,"v":1792,"n":23,"y":152,"u":1793},"Asus Xtion Live Pro",[1779],[],[1794],[1795,23,1796,1797,1798],"badslam2019","ETH3D SLAM benchmark (this paper)","mounted on the rig and used only as infrared pattern emitter for active stereo; its own depth estimation not used","Sec. 5; Supp. Sec. 3.1, footnote 1",{"c":1776,"m":1800,"d":46,"f":1801,"v":1803,"n":41,"y":270,"u":1805},"Asus Xtion Pro Live",[1802,1779],"ASUS",[1804],"ASUS Xtion Pro LIVE",[1806,1810],[1807,23,1808,1809,332],"rgbdslamv2_2014","TUM RGB-D benchmark","structured light",[1811,53,1812,1813,1814],"refusion2019","Bonn RGB-D Dynamic Dataset (this paper)","recorded depth always within the valid sensor range for the Bonn sequences","Sec. IV-B; Sec. III-E",{"c":1776,"m":1816,"d":46,"f":1817,"v":1818,"n":41,"y":1819,"u":1820},"Asus Xtion sensor",[1802,1779],[],2017,[1821,1824],[1822,23,1823,569,257],"bundlefusion2017","SUN3D",[1825,53,29,1826,127],"flashfusion2018","live scanning at 5 mm voxel resolution",{"c":18,"m":1828,"d":46,"f":1829,"v":1830,"n":23,"y":1008,"u":1831},"Autolabor-PC (control console)",[],[],[1832],[1833,53,1834,1835,1104],"yan2026_underground3dgsslam","Underground_RGB-D (authors' field test dataset)","CPU AMD Ryzen3 3200G, DDR4 8GB; listed in Table I as the control console of the data-collection platform, while all SLAM experiments ran on the desktop computer (Sec. IV-A)",{"c":372,"m":1837,"d":20,"f":1838,"v":1839,"n":23,"y":1008,"u":1840},"Autolabor-Pro1",[],[],[1841],[1833,53,1834,1842,1843],"Mobile robot, four-wheel drive; displacement speed 0.5 to 1.5 m\u002Fs; angular velocity 0.56 rad\u002Fs","Table I; Fig. 4",{"c":372,"m":1845,"d":46,"f":1846,"v":1847,"n":23,"y":49,"u":1848},"Automated Guided Vehicle for smart manufacturing (model not stated)",[],[],[1849],[1850,53,29,569,1851],"floam2021","Sec. IV-C2; Fig. 5a",{"c":372,"m":1853,"d":46,"f":1854,"v":1855,"n":23,"y":24,"u":1856},"autonomous ground vehicle (mobile robotic platform)",[],[],[1857],[27,53,29,1858,1859],"two stereo heads mounted in the front","Fig. 4; Sec. 5",{"c":372,"m":1861,"d":46,"f":1862,"v":1863,"n":23,"y":318,"u":1864},"autonomous guided vehicle for warehouse manipulation",[],[],[1865],[1183,53,29,1866,1867],"maximum speed 1 m\u002Fs","Sec. IV-A; Fig. 4",{"c":372,"m":1869,"d":20,"f":1870,"v":1871,"n":23,"y":233,"u":1872},"autonomous vehicle equipped with differential wheel and Velodyne LiDAR (Experiment III)",[],[],[1873],[236,53,29,1874,1875],"same 16-line Velodyne LiDAR; driven at about 0.2 m\u002Fs and 0.8 m\u002Fs (0.8 m\u002Fs limited by the vehicle)","Fig. 7(b); Sec. IV-D; Sec. IV-F",{"c":372,"m":1877,"d":20,"f":1878,"v":1879,"n":23,"y":233,"u":1880},"autonomous vehicle equipped with McLam wheel and Velodyne LiDAR (Experiment II)",[],[],[1881],[236,53,29,1882,1883],"wheel encoder, IMU and LiDAR same as Experiment I; GPS block on vehicle not used","Fig. 7(a); Sec. IV-C",{"c":1776,"m":1885,"d":20,"f":1886,"v":1887,"n":41,"y":122,"u":1888},"Azure Kinect",[],[],[1889,1894],[1890,23,1891,1892,1893],"coslam2023","NICE-SLAM apartment sequence","apartment sequence captured by the NICE-SLAM authors; run with the ScanNet setting, qualitative only","Supp. 2.2; Supp. Fig. 11",[932,53,1895,1896,1897],"SCO (self-captured outdoor)","used to capture the self-captured outdoor (SCO) dataset of 6 scenes with 800 to 2700 frames; only RGB images are input, the depth is shown for visualization and is unreliable outdoors","Sec. 4 Datasets; Sec. 4.1; Fig. 6",{"c":1776,"m":1899,"d":20,"f":1900,"v":1901,"n":23,"y":132,"u":1902},"Azure Kinect (IR depth camera)",[],[],[1903],[1904,53,29,1905,1906],"chung2025aspar","IR depth point cloud in a 3 m x 1.5 m x 2 m box converted to a 300 x 150 BEV image for obstacle detection","Sec. 3.1, Sec. 3.5, Table 2",{"c":522,"m":1908,"d":46,"f":1909,"v":1910,"n":23,"y":132,"u":1911},"B\u002FW camera with a fisheye lens",[],[],[1912],[135,53,1913,1914,1915],"private dataset","equidistant projection model","Sec. V-A2, V-A3",{"c":372,"m":1917,"d":20,"f":1918,"v":1919,"n":23,"y":1920,"u":1921},"B21",[],[],1999,[1922],[1923,53,29,569,1924],"gutmann_konolige1999_lrgc","Sec. 3",{"c":33,"m":1926,"d":46,"f":1927,"v":1928,"n":23,"y":61,"u":1929},"B21r simulator",[],[],[1930],[83,53,29,1931,85],"simulator of a B21r robot used to generate the Wean Hall data (32 m x 10 m, 251 m, noise added to the ground truth)",{"c":372,"m":1933,"d":46,"f":1934,"v":1935,"n":23,"y":49,"u":1936},"backpack",[],[],[1937],[1938,53,1939,1940,1941],"liliom2021","KA-Urban","carries the Livox-Xsens suite; KA-Urban sequences 0.20 to 3.70 km","Sec. 5.3.2; Fig. 9",{"c":372,"m":1943,"d":46,"f":1944,"v":1945,"n":23,"y":346,"u":1946},"backpack (carried through the museum)",[],[],[1947],[1948,23,1949,1950,1072],"droeschel2018ctslam","Deutsches Museum dataset (Google Cartographer team)","parts of the data contain moving persons",{"c":372,"m":1952,"d":46,"f":1953,"v":1954,"n":23,"y":152,"u":1955},"backpack carried by a human observer",[],[],[1956],[1957,53,29,1958,1959],"koide2019_hdlgraphslam","observer walked (about 1.5 m\u002Fs) or ran (about 3.0 m\u002Fs) in localization tests","Fig. 1; Sec. Sensor localization evaluation",{"c":372,"m":1961,"d":46,"f":1962,"v":1963,"n":23,"y":132,"u":1964},"backpack device",[],[],[1965],[1966,53,1967,1968,1969],"lamm2025","Shenzhen (self-collected)","carries a Hesai 128-line LiDAR and four Hikvision cameras","Sec. IV-C2",{"c":372,"m":1971,"d":46,"f":1972,"v":1973,"n":23,"y":49,"u":1974},"Backpack mapping system (model not stated)",[],[],[1975],[1976,23,1977,1978,1979],"mulls2021","ISPRS MIMAP","carries VLP32C and HDL32E","Sec. IV-B1",{"c":372,"m":1981,"d":46,"f":1982,"v":1983,"n":41,"y":100,"u":1984},"backpack mount",[],[],[1985,1989],[1986,53,29,1987,1988],"kremen2024hovermap750m","Hovermap ST-X carried on a backpack; five single-pass (about 10 min) and five forth-and-back (about 20 min) runs","The used devices and software; Measurement and processing with the Emesent Hovermap ST-X",[1990,53,29,1991,1992],"kremen2025earthworks","carries Hovermap ST-X and Trimble R12i","Fig. 2c",{"c":522,"m":1994,"d":20,"f":1995,"v":1997,"n":23,"y":132,"u":1998},"Basler Ace (four units)",[1996],"Basler",[],[1999],[790,53,2000,2001,2002],"in-house car dataset","360 deg visual coverage, 10 Hz; images used at 512 x 1,032","Sec. IV-A1; Sec. IV-A2",{"c":522,"m":2004,"d":20,"f":2005,"v":2007,"n":23,"y":299,"u":2008},"BASLER SCOUT",[2006],"BASLER",[],[2009],[1606,952,29,2010,2011],"set of 8 cameras, 1 Mpx, up to 30 fps; images at a constant 1 to 5 m distance step","Sec. 3.1, Fig. 10",{"c":33,"m":2013,"d":46,"f":2014,"v":2015,"n":23,"y":1008,"u":2016},"battery",[],[],[2017],[2018,53,2019,2020,2021],"li2026tunneldt","authors' tunnel dataset (~300 m)","14.8 V, 6000 mAh, in the handle compartment","Sec. 2.1.1",{"c":522,"m":2023,"d":20,"f":2024,"v":2026,"n":23,"y":233,"u":2027},"Bebop2 forward-looking camera",[2025],"Parrot",[],[2028],[1657,53,29,2029,2030],"rectified 856 x 480 images at about 30 Hz; focal length about 520 px","Sec. 5.1.1, Sec. 6.2",{"c":662,"m":2032,"d":20,"f":2033,"v":2034,"n":23,"y":233,"u":2035},"Bebop2 onboard IMU (odometry velocities)",[2025],[],[2036],[1657,53,29,2037,2038],"onboard odometry about 5 Hz, boosted to the image rate","Sec. 5.1, 5.1.1",{"c":353,"m":2040,"d":20,"f":2041,"v":2043,"n":23,"y":299,"u":2044},"BEI HS35",[2042],"BEI",[],[2045],[1606,952,29,2046,530],"odometer with 1 mm distance measurement accuracy; DMI located inside the wheel",{"c":522,"m":2048,"d":20,"f":2049,"v":2051,"n":23,"y":100,"u":2052},"BFS-U3-31S4C (written 'FILR BFS-U3-31S4C')",[2050],"FLIR (written 'FILR')",[],[2053],[2054,23,1563,2055,1104],"livgaussmap2024","global shutter, 1024 x 768, FoV 66.5 x 82.9 deg",{"c":372,"m":2057,"d":20,"f":2058,"v":2060,"n":23,"y":233,"u":2061},"BIA5 ATR tracked robot (two robots)",[2059],"BIA5",[],[2062],[668,53,2063,2064,671],"DARPA SubT Final Event","tracked robots carrying SpinningPacks",{"c":33,"m":2066,"d":46,"f":2067,"v":2068,"n":23,"y":132,"u":2069},"black and white (B&W) targets",[],[],[2070],[2071,41,29,2072,2073],"xu2025dualmlsuncertainty","49 targets distributed through the test area with tachymetric coordinates","Sec. 3.1.2, 4.1; Fig. 4",{"c":522,"m":2075,"d":20,"f":2076,"v":2077,"n":23,"y":152,"u":2078},"Blackfly BFLY-PGE-23S6M",[],[],[2079],[2080,53,2081,2082,2083],"licfusion2019","self-collected indoor and outdoor sequences","monochrome global-shutter camera","Sec. III; Fig. 2",{"c":522,"m":2085,"d":20,"f":2086,"v":2088,"n":23,"y":1819,"u":2089},"Blackmagic Production Camera with Rokinon 10mm f\u002F2.8 lens",[2087],"Blackmagic",[],[2090],[2091,952,2092,2093,2094],"knapitsch2017tnt","Tanks and Temples","global shutter; 8.6 MP video with 12 stops of dynamic range; tested against rolling-shutter cameras but not used for benchmark video because of lower low-light sensitivity","Sec. 3; Sec. 4.2",{"c":522,"m":2096,"d":20,"f":2097,"v":2098,"n":23,"y":100,"u":2099},"BLK2GO panoramic vision system (3 cameras)",[911],[],[2100],[2101,53,29,2102,2103],"kelly2024blk2go","4.8 Mpixel, 300 x 135 deg, global shutter; covered with paper for lidar-only runs","Table 1; Sec. 3.3",{"c":372,"m":2105,"d":46,"f":2106,"v":2108,"n":23,"y":152,"u":2109},"Bluefin Hovering Autonomous Underwater Vehicle (HAUV)",[2107],"General Dynamics Mission Systems (per ref. [5])",[],[2110],[2111,53,29,2112,2113],"hinduja2019degeneracy","five thrusters controlling all DoF except roll and pitch; onboard odometry from fused navigation sensors","Sec. IV-A, Fig. 3",{"c":662,"m":2115,"d":20,"f":2116,"v":2117,"n":23,"y":100,"u":2119},"BM1088",[],[2118],"BM1088 (as written)",[2120],[2054,23,2121,569,1104],"FAST-LIVO dataset",{"c":662,"m":2123,"d":20,"f":2124,"v":2125,"n":23,"y":100,"u":2126},"BMI085",[],[],[2127],[2054,53,29,569,2128],"Table I (Our Device I)",{"c":662,"m":2130,"d":20,"f":2131,"v":2132,"n":28,"y":100,"u":2133},"BMI088",[],[],[2134,2139,2141,2143,2144],[2135,23,2136,2137,2138],"chen2025geode","GEODE","built-in IMU of the Livox AVIA, 200 Hz","Table 2, Table 4",[2054,53,29,569,2140],"Table I (Our Device II)",[1101,53,2142,1103,1104],"HandNav (private)",[1101,23,1137,1103,1104],[1101,23,2145,1103,1104],"R3LIVE dataset",{"c":662,"m":2147,"d":20,"f":2148,"v":2149,"n":23,"y":233,"u":2150},"BMI088 (built into Livox Avia)",[],[],[2151],[378,53,29,2152,2153],"built-in IMU of Livox Avia","Sec. VII-A",{"c":662,"m":2155,"d":20,"f":2156,"v":2157,"n":23,"y":122,"u":2158},"BMI088 (built into the LiDAR)",[],[],[2159],[2160,53,29,2161,2162],"feng2023bridgeslam","built-in IMU of the Livox Avia","Sec. 6.1",{"c":662,"m":2164,"d":20,"f":2165,"v":2166,"n":41,"y":122,"u":2167},"BMI088 (built-in IMU of Livox Avia)",[],[],[2168,2172],[2169,53,29,2170,2171],"pointlio2023","200 Hz; measuring range 35 rad\u002Fs and about 30 m\u002Fs2 (Sec. 5.5.1); range set to 17.5 rad\u002Fs on the self-rotating UAV (Sec. 7.2)","Sec. 5.2; Sec. 5.5.1; Sec. 7.2",[2173,23,1137,1272,2174],"voxelslam2026","Sec. 10",{"c":944,"m":2176,"d":20,"f":2177,"v":2179,"n":23,"y":318,"u":2180},"bMS3D",[2178],"Viametris",[],[2181],[951,952,29,2182,2183],"backpack with 2D LiDARs; 13.5 kg; 600,000 pts\u002Fs; relative accuracy 5 cm; operating time N\u002FA; Ladybug 5+ camera","Sec. 2.2.1; Tables 1-4, 6; Fig. 4b",{"c":372,"m":2185,"d":20,"f":2186,"v":2187,"n":23,"y":455,"u":2188},"Bobcat S185 skid-steer loader",[],[],[2189],[476,53,29,2190,2191],"spinning laser mounted above the cab, spin axis facing forward; skid-steer, can turn in place","Sec. II; Fig. 1",{"c":372,"m":2193,"d":46,"f":2194,"v":2195,"n":23,"y":132,"u":2196},"Boreas data collection vehicle",[],[],[2197],[1588,23,1589,2198,1591],"repeated route at the University of Toronto over one year; 102 km or 4.3 h test set",{"c":662,"m":2200,"d":20,"f":2201,"v":2203,"n":23,"y":318,"u":2204},"Bosch BMI055 (camera IMU)",[2202],"Bosch",[],[2205],[896,23,815,2206,2207],"gyroscope 400 Hz, accelerometer 250 Hz inside the D435i","Table II",{"c":662,"m":2209,"d":20,"f":2210,"v":2211,"n":2212,"y":49,"u":2213},"Bosch BMI085",[2202],[],6,[2214,2217,2220,2224,2225,2227,2230],[1038,23,1039,2215,2216],"IMU embedded in the AlphaSense module, modest noise and bias stability, 200 Hz; body-frame origin at its centre","Sec. III-C, III-F; Table II",[2173,23,2218,2219,2174],"Hilti (handheld sequences, Table C1)","400 Hz",[2221,23,2222,2223,1104],"potokar2025lo_eval","Hilti 2022","handheld",[2221,23,791,1933,1104],[1247,23,1248,2226,1250],"cellphone-grade IMU in the Alphasense, 200 Hz, synchronized with cameras",[1252,23,1253,2228,2229],"cellphone-grade IMU in the Alphasense, 400 Hz, synchronized with cameras; Allan variance from a 90 min static recording","Table I; Sec. III-A",[216,23,2231,2232,157],"Hilti'22 and Hilti'23 (handheld)","external IMU at 400 Hz",{"c":662,"m":2234,"d":20,"f":2235,"v":2236,"n":23,"y":233,"u":2237},"Bosch BMI088",[2202],[],[2238],[2239,53,29,2240,2241],"zhu2022liinit","6-axis IMU inside both the Pixhawk flight controller and the Livox LiDARs; raw data 200 Hz","Sec. IV (setup)",{"c":662,"m":2243,"d":20,"f":2244,"v":2245,"n":23,"y":318,"u":2246},"Bosch BNO055",[2202],[],[2247],[2248,53,29,2249,2250],"asadi2020ugvuav","9 DOF (accelerometer, gyroscope, magnetometer); absolute orientation, angular velocity and linear acceleration at 100 Hz; ARM Cortex-M0","Sec. 3.2.1",{"c":372,"m":2252,"d":46,"f":2253,"v":2255,"n":2256,"y":233,"u":2257},"Boston Dynamics Spot",[2254],"Boston Dynamics",[],5,[2258,2261,2264,2268,2271],[715,53,29,2259,2260],"legged robot with custom payload","Fig. 1B",[1396,53,29,2262,2263],"MARBLE's Spot robot in the CU Boulder Engineering Center and parking-garage run","Sec. IV-B, Fig. 14, Sec. V-A",[2265,53,29,2266,2267],"schaub2022pc2bim","LiDAR and payload computer powered by the robot; demonstration only","Sec. 3.3, Figs. 1, 5",[537,53,29,2269,2270],"quadruped; Autowalk missions defined with fiducials; powers LiDAR and Raspberry Pi from its internal battery","Hardware Setup; Data Collection; Fig. 1",[2272,53,29,2273,2274],"tuomisto2026quadrupedbim","quadruped with arm; built-in sensors support its internal obstacle avoidance; driven through the Boston Dynamics SDK with waypoints","Sec. 3.3.1; Sec. 3.5.2; Fig. 2",{"c":372,"m":2276,"d":20,"f":2277,"v":2278,"n":23,"y":100,"u":2279},"Boston Dynamics Spot (modified, RA2)",[2254],[],[2280],[2281,53,29,2282,2283],"prieto2024mars","four-legged robot; six-DOF robotic arm with a two-finger gripper","VoR Sec. 4.2.2, Fig. 10",{"c":372,"m":2285,"d":46,"f":2286,"v":2287,"n":23,"y":233,"u":2288},"Boston Dynamics Spot (two robots)",[2254],[],[2289],[668,53,2063,2290,671],"legged robots carrying SpinningPacks",{"c":108,"m":2292,"d":46,"f":2293,"v":2295,"n":23,"y":132,"u":2296},"Bpearl",[2294],"Robosense",[],[2297],[2298,53,29,2299,2300],"kinematicicp2025","90 x 360 deg hemispherical 32-beam LiDAR, 10 Hz","Sec. V-A-1",{"c":662,"m":2302,"d":46,"f":2303,"v":2304,"n":23,"y":122,"u":2305},"built-in consumer-grade IMU of a ZED camera (model not stated)",[],[],[2306],[1191,53,2307,2308,2309],"TONGJI dataset (authors' handheld device)","400 Hz; noise statistics from Woodman's approach","Sec. IV-A-2; Fig. 4(a)",{"c":522,"m":2311,"d":46,"f":2312,"v":2313,"n":23,"y":346,"u":2314},"built-in DSLR camera (model not reported)",[],[],[2315],[2316,53,29,2317,2318],"kim2018slamdriven","RGB mapping of static scans via pinhole model with checkerboard intrinsic calibration","Sec. 4.1; Sec. 4.4",{"c":662,"m":2320,"d":20,"f":2321,"v":2322,"n":23,"y":132,"u":2323},"built-in IMU of Intel RealSense L515",[98],[],[2324],[2325,23,2326,2327,2328],"li2025hcic","HCIC Construction VSLAM dataset","IMU messages aligned to image messages by closest timestamp (ROS default)","Sec. 2.3, Sec. 2.4, Table 2",{"c":662,"m":2330,"d":20,"f":2331,"v":2332,"n":23,"y":132,"u":2333},"built-in IMU of Livox Mid360",[],[],[2334,2336],[1510,23,1511,2335,1104],"used as I in GrandTour",[1510,53,2337,2338,1104],"HelmDyn (own experiment)","used for R-LIO on HelmDyn",{"c":662,"m":2340,"d":20,"f":2341,"v":2342,"n":23,"y":132,"u":2343},"built-in IMU of Ouster OS0-128",[180],[],[2344],[2325,23,2326,2345,2328],"synchronized with the LiDAR; LiDAR not time-aligned with images",{"c":662,"m":2347,"d":20,"f":2348,"v":2349,"n":23,"y":132,"u":2350},"built-in IMU of the Intel RealSense D455",[98],[],[2351],[2352,53,29,2219,2353],"chen2025quadrupedinspection","Sec. 4.1, Table 3",{"c":662,"m":2355,"d":46,"f":2356,"v":2357,"n":23,"y":233,"u":2358},"built-in IMU of the LiDAR (label 'Build-in IMU')",[],[],[2359],[1747,53,29,569,2360],"VoR Fig. 5",{"c":662,"m":2161,"d":20,"f":2362,"v":2363,"n":23,"y":1008,"u":2364},[],[],[2365],[2366,53,29,2367,2368],"litgs2026","200 Hz (Fig. 2)","Sec. IV-A; Fig. 2",{"c":662,"m":2370,"d":46,"f":2371,"v":2373,"n":23,"y":1008,"u":2374},"built-in InvenSense IMU of the LiDAR",[2372],"InvenSense",[],[2375],[1155,53,2376,2377,1538],"own car dataset","100 Hz",{"c":33,"m":2379,"d":20,"f":2380,"v":2381,"n":23,"y":318,"u":2382},"Burster 8417",[],[],[2383],[688,53,1111,2384,691],"force sensing, 1,000 Hz, resolution \u003C25 N",{"c":372,"m":2386,"d":46,"f":2387,"v":2388,"n":23,"y":318,"u":2389},"bus",[],[],[2390],[2391,53,29,2392,1867],"lins2020","LiDAR on the roof, IMU inside the bus",{"c":33,"m":2394,"d":46,"f":2395,"v":2396,"n":23,"y":1819,"u":2397},"calibrated motion capture system",[],[],[2398],[1822,41,991,2399,2400],"ground-truth camera poses of TUM RGB-D","Sec. 6 Quantitative Comparison",{"c":33,"m":2402,"d":20,"f":2403,"v":2404,"n":23,"y":270,"u":2405},"calibration board with 30 lamps (12 V, 4 mm bulbs) and chessboard pattern",[],[],[2406],[2407,53,29,2408,2409],"borrmann2014thermalmapping","500 mm x 570 mm board on a tripod for intrinsic and extrinsic thermal and colour camera calibration","Sec. 3.2.1-3.2.2",{"c":522,"m":522,"d":46,"f":2411,"v":2412,"n":23,"y":100,"u":2413},[],[],[2414],[211,23,2415,2416,214],"R3Live dataset","RGB images used for colorization",{"c":522,"m":2418,"d":46,"f":2419,"v":2420,"n":41,"y":132,"u":2421},"camera (model not reported)",[],[],[2422,2425,2428],[2423,23,212,2424,917],"gslivm2025","image resolution 752 x 480",[2423,23,2426,2427,917],"Botanic Garden","image resolution 480 x 300",[2429,23,2430,2431,2432],"bimloc2026","CityU construction benchmark","part of the CityU sensor suite, used for visualization only (BIM overlay checks)","Sec. 4.2.1, Sec. 4.2.4, Fig. 17",{"c":522,"m":2434,"d":46,"f":2435,"v":2436,"n":23,"y":132,"u":2437},"camera (model not stated; pinhole projection model)",[],[],[2438],[1136,53,29,2439,2440],"intrinsics calibrated with a checkerboard","Sec. II; Sec. III-A",{"c":522,"m":2442,"d":46,"f":2443,"v":2444,"n":41,"y":122,"u":2445},"camera (model not stated)",[],[],[2446,2451],[2447,23,2448,2449,2450],"feng2025_construction_lidar_eval","Feng et al. real construction-site dataset (Xi'an hospital)","image topic \u002Fcamera\u002Fcolor\u002Fimage_raw at 30 Hz; recorded but not used by the evaluated LiDAR SLAM methods","Sec. 3.2(2); Sec. 3.3; Sec. 6",[2452,23,2453,2454,1979],"cocolic2023","R3LIVE and FAST-LIVO challenging sequences","15 Hz",{"c":522,"m":2456,"d":46,"f":2457,"v":2458,"n":23,"y":1008,"u":2459},"camera of i2Nav-Robot (model not reported)",[],[],[2460],[1101,23,1102,2461,2462],"1600x1200, 10 Hz; pixel size 5.86 um, focal length 6 mm (Table I; Sec. IV-C1)","Table I; Sec. IV-C1",{"c":522,"m":2464,"d":46,"f":2465,"v":2466,"n":23,"y":1008,"u":2467},"camera of MARS-LVIG (model not reported)",[],[],[2468],[1101,23,1137,2469,1104],"2448x2048, 10 Hz (Table I)",{"c":522,"m":2471,"d":46,"f":2472,"v":2473,"n":23,"y":1008,"u":2474},"camera of the R3LIVE dataset (model not reported)",[],[],[2475],[1101,23,2145,2476,1104],"1280x1024, 15 Hz (Table I)",{"c":522,"m":2478,"d":46,"f":2479,"v":2480,"n":23,"y":132,"u":2481},"camera of the R3LIVE handheld device (model not reported)",[],[],[2482],[2483,23,2484,2485,2486],"livgs2025","R3LIVE dataset (hku_park_00)","1280 x 1024 at 30 Hz","Sec. IV-G",{"c":522,"m":2488,"d":46,"f":2489,"v":2490,"n":23,"y":233,"u":2491},"camera of the robot payload (model not stated)",[],[],[2492],[2493,53,29,569,2494],"elasticity_ct2022","VoR Sec. VII-A",{"c":522,"m":2496,"d":46,"f":2497,"v":2498,"n":23,"y":318,"u":2499},"Camera on the hand-held device (model not stated)",[],[],[2500],[2501,23,29,2502,2503],"loamlivox2020","shown on the hand-held rig; not used by the algorithm, which uses no IMU, GPS or camera","Fig. 8d; Sec. I",{"c":522,"m":2505,"d":46,"f":2506,"v":2507,"n":23,"y":233,"u":2508},"camera streams (models not reported)",[],[],[2509],[2510,23,2511,2512,332],"locus2_2022","NeBula odometry dataset (DARPA SubT, Team CoSTAR)","recorded in the dataset",{"c":522,"m":2514,"d":46,"f":2515,"v":2516,"n":23,"y":233,"u":2517},"camera system (footnote links the ueye_cam ROS driver)",[],[],[2518],[1201,53,2519,2520,2521],"VIRAL field flight tests (authors)","camera system processed by VINS-Fusion as an OSL input; stereo configuration not stated for the field platform","Sec. VI-C; footnote",{"c":372,"m":2523,"d":46,"f":2524,"v":2525,"n":23,"y":1421,"u":2526},"camera tripod with adjustable pan and tilt mount",[],[],[2527],[2528,53,29,2529,2530],"glennie2016vlp16","rigid mount for 12 headings and tilts (0, 22.5, 45 deg) at each of two stations","Sec. 3.2, Table 2",{"c":522,"m":2532,"d":46,"f":2533,"v":2534,"n":23,"y":1008,"u":2535},"cameras (models not_reported)",[],[],[2536],[2537,23,2538,2539,917],"yan2026tunnel","WHU-Helmet (WHUH)","Tunnel sequence 12,304 images in 1403 s; Subway 15,685 images in 1580 s",{"c":372,"m":2541,"d":46,"f":2542,"v":2543,"n":23,"y":100,"u":2544},"Canary drone (model not reported)",[],[],[2545],[1371,23,1372,2546,2547],"real aerial platform named in locomotion analysis; highest acceleration and angular-velocity peaks","Supp. B.2",{"c":372,"m":2549,"d":46,"f":2550,"v":2551,"n":2552,"y":71,"u":2553},"car",[],[],7,[2554,2558,2563,2566,2570,2572,2576],[2555,23,842,2556,2557],"rtabmap2019","autonomous-driving recording car","Sec. 1, Sec. 4.1",[2559,23,2560,2561,2562],"in2laama2021","MC2SLAM dataset (campus drive)","driven around a university campus","Sec. VII-E-2",[2564,53,29,2565,478],"mourikis2007msckf","camera\u002FIMU system placed on a car driving in a residential area of Minneapolis",[2567,53,2568,2569,441],"mc2slam2019","MC2SLAM own datasets (campus run 1-2, campus drive, field)","car-mounted sensor (campus drive, field)",[2391,53,29,2571,1979],"port test in Guangdong",[2573,23,2574,2575,398],"pointnetvlad2018","Oxford RobotCar; in-house sets","routes of 10, 10, 8 and 5 km per round for Oxford, U.S., R.A., B.D.",[567,23,2577,2578,1538],"UrbanNav","autonomous driving sequences in Hong Kong",{"c":372,"m":2580,"d":46,"f":2581,"v":2582,"n":23,"y":122,"u":2583},"car (Complex Urban vehicle-mounted platform)",[],[],[2584],[1191,23,1192,2585,2586],"two inclined LiDARs and an IMU; each sequence about 1 hour and nearly 2 square kilometres","Sec. IV-A-2; Sec. IV-B-4; Fig. 4(c)",{"c":372,"m":2588,"d":46,"f":2589,"v":2590,"n":41,"y":289,"u":2591},"car (KITTI)",[],[],[2592,2594],[1783,23,578,2593,332],"urban and highway driving",[2595,23,578,2596,2597],"orbslam2015","driven around a residential area; 11 sequences","Sec. VIII-E",{"c":372,"m":2599,"d":46,"f":2600,"v":2601,"n":23,"y":299,"u":2602},"car (roof-mounted IMU and camera)",[],[],[2603],[2604,53,29,2605,1788],"msckf2_2013","about 21.5 km in 37 min",{"c":372,"m":2607,"d":46,"f":2608,"v":2609,"n":23,"y":346,"u":2610},"car (vehicle roof mount, model not reported)",[],[],[2611],[2612,53,29,2613,2614],"imlsslam2018","vehicle driven through Paris (two 2 km loops) and a square in Lille","Sec. VI-A; Fig. 1",{"c":1689,"m":2616,"d":46,"f":2617,"v":2619,"n":23,"y":318,"u":2620},"Carnegie Robotics MultiSense SL",[2618],"Carnegie Robotics",[],[2621],[321,23,322,2622,2623],"stereo vision with high-intensity illuminators; also contains a second LiDAR; depth image available","Sec. II-A, Sec. IV",{"c":372,"m":2625,"d":46,"f":2626,"v":2627,"n":23,"y":270,"u":2628},"cart pushed by a person",[],[],[2629],[2630,53,29,2631,2153],"loam2014","carries lidar, battery and laptop; indoor tests at 0.5 m\u002Fs",{"c":372,"m":2633,"d":46,"f":2634,"v":2635,"n":23,"y":1421,"u":2636},"Cartographer backpack",[],[],[2637],[2638,53,29,2639,2640],"cartographer2016","sensor-equipped backpack with a horizontally mounted LIDAR and an IMU used to estimate gravity for projecting scans; sensor models not reported; 2D grid maps at 5 cm resolution","Sec. III, IV",{"c":944,"m":2642,"d":46,"f":2643,"v":2645,"n":23,"y":100,"u":2646},"CatPack",[2644],"CSIRO",[],[2647],[1396,53,29,2648,2649],"IMU plus spinning Velodyne VLP-16 and four RGB cameras (cameras used for artifact detection, not SLAM); LIDAR-IMU and LIDAR-camera calibration at production","Sec. III-D-1, III-D-6",{"c":662,"m":2651,"d":20,"f":2652,"v":2653,"n":23,"y":1008,"u":2654},"cellphone-grade IMU (Fig. 1 caption names an Alphasense IMU)",[],[],[2655],[1155,23,791,2219,1538],{"c":662,"m":2657,"d":46,"f":2658,"v":2659,"n":23,"y":132,"u":2660},"cellphone-grade IMU integrated in the Alphasense Core Development Kit (model not stated)",[1243],[],[2661],[2662,23,791,2663,2664],"tao2025oxfordspires","400 Hz; Allan variance from an eight-hour sequence","Sec. 3.1, Sec. 4.2",{"c":1689,"m":2666,"d":46,"f":2667,"v":2668,"n":23,"y":346,"u":2669},"centre stereo camera of the Oxford RobotCar platform (model not stated)",[],[],[2670],[2573,23,2671,569,2672],"Oxford RobotCar","Sec. 5.2 Image based comparisons",{"c":662,"m":2674,"d":20,"f":2675,"v":2677,"n":23,"y":346,"u":2678},"CH Robotics UM6 Orientation Sensor",[2676],"CH Robotics",[],[2679],[2680,53,29,2681,2682],"legoloam2018","low-cost IMU on the Jackal; supplies the identical initial translational and rotational guess to LOAM and LeGO-LOAM","Sec. II; Sec. IV-A",{"c":651,"m":2684,"d":20,"f":2685,"v":2687,"n":23,"y":132,"u":2688},"CHCNAV CGI610 (written CG610 in Sec. 4.4.2)",[2686],"CHCNAV",[],[2689],[2135,41,2136,2690,2691],"GNSS-RTK\u002FINS, NMEA output, 100 Hz, RTK accuracy 1 cm; dual antennas with differential base station service; 6-DoF ground truth outdoors","Table 2, Sec. 4.4.2",{"c":33,"m":2693,"d":46,"f":2694,"v":2695,"n":23,"y":122,"u":2696},"Checkerboard reference targets with metal tip on floor crosshairs (model not reported)",[],[],[2697],[1252,41,1253,2698,2699],"crosshairs drawn on adhesive blue markers, levelled with bubble level, numbered, scanned by Z+F; 5 to 10 control points per sequence; manual tip placement error below 1 mm","Sec. V-B; Fig. 8",{"c":33,"m":2701,"d":46,"f":2702,"v":2703,"n":23,"y":132,"u":2704},"checkerboards",[],[],[2705],[2706,53,29,2707,2708],"jeon2025_nerf_construction","squares of 400 mm, installed at known positions and replicated in BIM","Sec. 3.1.1; Sec. 3.2.1; Figs. 3, 6",{"c":372,"m":2710,"d":46,"f":2711,"v":2713,"n":23,"y":132,"u":2714},"Clearpath Dingo",[2712],"Clearpath",[],[2715],[1145,952,29,2716,2717],"second robot localized on the sliced 2D map; also recorded data for the GMapping baseline map","Sec. IV-D; Fig. 3",{"c":372,"m":2719,"d":46,"f":2720,"v":2722,"n":28,"y":318,"u":2723},"Clearpath Husky",[2712,2721],"Clearpath Robotics",[],[2724,2726,2729],[1145,53,29,2725,2717],"mapping robot carrying the Hesai XT-32",[1359,53,29,2727,2728],"skid-steer wheeled ground rover","Sec. III; Fig. 1",[321,23,322,2730,2731],"wheeled robot for Tunnel Circuit; teleoperated over Wi-Fi","Sec. II-A, Fig. 3",{"c":372,"m":2733,"d":20,"f":2734,"v":2736,"n":28,"y":346,"u":2737},"Clearpath Husky A200",[2712,2735,2721],"Clearpath (as written in the platform name 'Clearpath Husky A200')",[],[2738,2742,2744],[2739,53,29,2740,2741],"asadi2018visionrobot","wheeled UGV; joystick-driven for initial mapping; kill switch via Raspberry Pi","Sec. 1, Sec. 3, Fig. 3",[2248,53,29,2743,530],"custom-built UGV; 24 V 20 Ah battery; 3 h typical operation, up to 8 h standby; pan-tilt units for cameras",[2298,53,29,2745,2300],"four-wheeled skid-steering robot with wheel-encoder odometry",{"c":372,"m":2747,"d":46,"f":2748,"v":2749,"n":28,"y":346,"u":2750},"Clearpath Jackal",[2712],[],[2751,2754,2757],[414,53,29,2752,2753],"ground rover with wheel odometer and IMU; battery lasting 2 to 3 hours; stop in-place fail-safe","Collection Platform, Fig. 1",[2680,53,29,2755,2756],"UGV, 270 Wh lithium battery, maximum speed 2.0 m\u002Fs, maximum payload 20 kg","Sec. II; Fig. 1a",[2758,53,29,2759,2760],"liosam2020","UGV without suspension; Park dataset on a forested hiking trail","Sec. IV; Sec. IV-D; Fig. 2b",{"c":372,"m":2762,"d":46,"f":2763,"v":2764,"n":23,"y":1008,"u":2765},"Clearpath Jackal mobile robots (eight)",[2712],[],[2766],[2537,23,2767,2768,917],"Kimera-Multi Campus-Tunnel (KMCT)","collectively travelled 6753 m in about 30 min in the MIT campus tunnel",{"c":372,"m":2770,"d":46,"f":2771,"v":2772,"n":23,"y":233,"u":2773},"Clearpath Jackal UGV",[2712],[],[2774],[2775,53,2776,569,2777],"kimeramulti2022","Medfield and Stata outdoor datasets (authors' own)","Sec. VII-C",{"c":372,"m":2779,"d":46,"f":2780,"v":2782,"n":23,"y":49,"u":2783},"Clearpath Jackal unmanned ground vehicle",[2781],"Clearpath (as named)",[],[2784],[2785,53,2786,2787,2788],"lvisam2021","Jackal","manually driven","Sec. III-B",{"c":33,"m":2790,"d":46,"f":2791,"v":2792,"n":23,"y":346,"u":2793},"close-range photogrammetry (SfM) model of the tower (camera not reported)",[],[],[2794],[2795,41,29,2796,2797],"sammartano2018zeb","about 1 cm accuracy; GCP total RMSE 0.007 m, CP total 0.014 m","Table 3; Sec. Tower (A)",{"c":651,"m":2799,"d":46,"f":2800,"v":2801,"n":23,"y":152,"u":2802},"commercial GPS\u002FINS (model not stated)",[],[],[2803],[2567,41,578,2804,2805],"ground truth for the training sequences","Sec. 5 KITTI Dataset",{"c":2807,"m":2808,"d":46,"f":2809,"v":2810,"n":23,"y":152,"u":2811},"tls_scanner","commercial terrestrial laser scanner (model not reported)",[],[],[2812],[431,41,29,2813,2814],"measurement error about +-3 mm as stated; target-based workflow with manual registration (10 min pre-processing, 48 min operation, 50 min post-processing for six scans)","Sec. 5; Table 4",{"c":18,"m":2816,"d":46,"f":2817,"v":2818,"n":23,"y":37,"u":2819},"commodity GPU (model not reported)",[],[],[2820],[2821,28,29,2822,2823],"kinectfusion2011","all tracking and mapping on GPU; TSDF update above 65 gigavoxels per second","Abstract; Sec. 3.3",{"c":1776,"m":2825,"d":46,"f":2826,"v":2827,"n":23,"y":289,"u":2828},"commodity RGB-D camera (model not stated)",[],[],[2829],[2830,53,2831,2832,2833],"kintinuous2015","authors' seven hand-held datasets","640x480 frames at 30 Hz; auto exposure and auto white balance enabled in all real datasets","Sec. 2.5.1, 5.1, 5.3.1",{"c":33,"m":2835,"d":46,"f":2836,"v":2838,"n":23,"y":100,"u":2839},"Communication Module from Persistent System (MANET radio)",[2837],"Persistent System",[],[2840],[2281,53,29,2841,2842],"data rates up to 120 Mbps; 2.420 GHz, 100 mW, 20 MHz bandwidth, 3 x 3 MIMO, CTR-AES-256","VoR Sec. 4.2.1, Table 2, Fig. 9",{"c":18,"m":2844,"d":20,"f":2845,"v":2846,"n":23,"y":100,"u":2847},"Computation Unit, x64 architecture (RA1)",[],[],[2848],[2281,28,29,2849,2850],"runs exploration, navigation and data acquisition under ROS","VoR Sec. 4.2.1",{"c":18,"m":2852,"d":46,"f":2853,"v":2854,"n":23,"y":152,"u":2855},"Compute Canada computing clusters",[],[],[2856],[2857,28,29,2858,1084],"landry2019cello3d","about 5 CPU-years for about 5,100,000 training registrations (offline data generation, not online runtime)",{"c":372,"m":2860,"d":46,"f":2861,"v":2862,"n":23,"y":1008,"u":2863},"construction robot experimental platform (Ackermann-steered wheeled base)",[],[],[2864],[2865,53,29,2866,2867],"feng2026integratedslam","24 V\u002F30 Ah battery; front steering and rear drive via PWM motor controller; max 3.0 m\u002Fs; CAN bus to the Jetson; teleoperated with an Xbox 360 controller at 0.50 m\u002Fs on site","Sec. 3.2, Sec. 5.1, Fig. 2",{"c":372,"m":2869,"d":46,"f":2870,"v":2871,"n":23,"y":132,"u":2872},"construction robot experimental platform (custom)",[],[],[2873],[2447,23,2448,2874,2875],"wheeled (rubber tyres), drive and steering motors, 24 V 30 Ah LiFePO4 battery, max 3.0 m\u002Fs, 62 kg; teleoperated with an Xbox 360 wireless controller at 0.5 m\u002Fs on site","Sec. 3.1; Sec. 3.2.1; Sec. 3.3; Fig. 3",{"c":18,"m":2877,"d":46,"f":2878,"v":2879,"n":23,"y":122,"u":2880},"consumer laptop",[],[],[2881],[1720,28,1721,2882,2883],"real-time processing of about 15 Hz radar scans","Sec. IV; Table II",{"c":651,"m":2885,"d":46,"f":2886,"v":2887,"n":23,"y":132,"u":2888},"consumer-grade GNSS receiver",[],[],[2889],[282,53,846,2890,2891],"used for georeferencing and loop-closure candidate search, not by LO","Sec. 4.1; Sec. 3.11; Sec. 7.5",{"c":662,"m":2893,"d":46,"f":2894,"v":2895,"n":23,"y":100,"u":2896},"consumer-grade MEMS IMUs (models not stated)",[],[],[2897],[567,23,568,569,332],{"c":944,"m":2899,"d":46,"f":2900,"v":2902,"n":23,"y":346,"u":2903},"Contour",[2901],"custom-built",[],[2904],[2905,53,29,2906,2907],"zhang2018lvio","handheld device with spinning 2D scanner, wide-angle and HD cameras, Xsens IMU, embedded computer and touch-screen monitor","Sec. 10.2, Fig. 27",{"c":372,"m":2909,"d":46,"f":2910,"v":2911,"n":23,"y":759,"u":2912},"conventional utility vehicle",[],[],[2913],[888,53,29,2914,2915],"standard road vehicle fitted with the MMWR as primary sensor; driven manually, stationary about 30 s then loops at up to 10 m\u002Fs","Sec. IV-A, IV-B, Fig. 2",{"c":18,"m":2917,"d":20,"f":2918,"v":2919,"n":23,"y":346,"u":2920},"Core i7 CPU (Ubuntu, ROS)",[],[],[2921],[2922,28,29,2923,332],"pang2018ndticp","vehicle computer; not restated as the timing hardware",{"c":18,"m":2925,"d":46,"f":2926,"v":2927,"n":23,"y":122,"u":2928},"Core i7 PC (model not stated)",[98],[],[2929],[1219,28,29,569,332],{"c":18,"m":2931,"d":20,"f":2932,"v":2933,"n":23,"y":152,"u":2934},"Core i7-7820HK",[98],[],[2935],[2936,28,29,2937,313],"openvslam2019","2.90GHz, 4C8T; 32GB RAM; laptop used for all evaluations",{"c":372,"m":2939,"d":46,"f":2940,"v":2941,"n":23,"y":270,"u":2942},"Cosero mobile manipulation robot",[],[],[2943],[2944,53,29,2945,2946],"mrsmap2014","used the tracking method at RoboCup@Home 2011 and 2012","Sec. 6.4",{"c":18,"m":2948,"d":46,"f":2949,"v":2950,"n":23,"y":318,"u":2951},"CPU (model not reported)",[],[],[2952],[2953,28,29,2954,2955],"kimera2020","real-time CPU execution with four threads","Abstract; Sec. II; Sec. III-D",{"c":18,"m":2957,"d":46,"f":2958,"v":2959,"n":23,"y":122,"u":2960},"CPU with 8 parallel threads (model not named)",[],[],[2961],[2962,28,29,2963,2964],"hba2023","n = 8 threads for parallel processing","Table I; Table VIII",{"c":18,"m":2966,"d":46,"f":2967,"v":2968,"n":23,"y":100,"u":2969},"CPU-only PC, Intel i7-9700K CPU, 64 GB RAM",[98],[],[2970],[599,28,29,2971,2972],"no GPU acceleration","VoR Sec. VI-G; Table VII",{"c":18,"m":2974,"d":46,"f":2975,"v":2976,"n":23,"y":233,"u":2977},"CPU, single thread (model not reported)",[],[],[2978],[2979,28,29,569,2980],"cticp2022","Abstract; Sec. III-A",{"c":372,"m":2982,"d":46,"f":2983,"v":2984,"n":23,"y":152,"u":2985},"custom Ackermann robot platform",[],[],[2986],[2080,53,2987,2988,717],"self-collected outdoor sequence","outdoor carrier of the sensor rig",{"c":372,"m":2990,"d":46,"f":2991,"v":2992,"n":23,"y":122,"u":2993},"custom aerial vehicle (hand-carried for data collection, no flight)",[],[],[2994],[2995,53,2996,569,2997],"dlio2023","UCLA Campus (self-collected)","Fig. 1; Sec. IV-B-2",{"c":18,"m":2999,"d":20,"f":3000,"v":3001,"n":23,"y":1008,"u":3002},"Custom desktop computer (Intel i7-14700K, NVIDIA GeForce RTX 4090D)",[],[],[3003],[1833,28,29,3004,3005],"CPU Intel i7-14700K; GPU NVIDIA GeForce RTX 4090D; DDR5 64GB; Ubuntu 18.04","Table I; Sec. IV-A; Sec. IV-C",{"c":372,"m":3007,"d":46,"f":3008,"v":3010,"n":23,"y":318,"u":3011},"custom handheld device",[3009],"Oxford Robotics Institute (custom)",[],[3012],[896,23,815,3013,3014],"sensors rigidly attached to a 3D-printed base; held above the shoulder at about 1 m\u002Fs; URDF released","Sec. III, Sec. IV, Fig. 2",{"c":372,"m":3016,"d":20,"f":3017,"v":3019,"n":23,"y":49,"u":3020},"Custom handheld device (3D-printed base, URDF released)",[3018],"custom (Oxford Robotics Institute)",[],[3021],[1247,23,1248,3022,2191],"sensors rigidly attached to a precisely 3D-printed base",{"c":33,"m":3024,"d":46,"f":3025,"v":3026,"n":23,"y":152,"u":3027},"custom microcontroller-based time synchronization circuit",[],[],[3028],[155,53,29,3029,157],"synchronizes cameras, LiDAR, IMU and computer by simulating GPS time signals",{"c":33,"m":3031,"d":46,"f":3032,"v":3033,"n":23,"y":152,"u":3034},"custom PCB trigger board and Arduino microcontroller",[],[],[3035],[3036,53,29,3037,3038],"charron2019bridgerobot","distributes the GPS PPS to Ximea and lidars and generates a PWM trigger for the IR camera","Sec. Platform Development; Fig. 2",{"c":372,"m":3040,"d":46,"f":3041,"v":3042,"n":23,"y":233,"u":3043},"custom quadrotor (Team CoSTAR)",[],[],[3044],[715,53,29,3045,3046],"carries Ouster OS1","Fig. 1A",{"c":1776,"m":3048,"d":46,"f":3049,"v":3050,"n":23,"y":1008,"u":3051},"custom RGB-D rig with synchronized IMU, RGB, IR and wide-angle grayscale sensors",[],[],[3052],[3053,23,3054,3055,3056],"tosi2026survey","Replica","18 photorealistic indoor scenes with dense meshes","Sec. II-C",{"c":522,"m":3058,"d":46,"f":3059,"v":3060,"n":23,"y":270,"u":3061},"custom-built camera",[],[],[3062],[3063,53,3064,3065,3066],"demo2014","DEMO author-collected tests","up to 60 Hz, 744 x 480, 83 deg horizontal FoV","Sec. III; Fig. 2(b)",{"c":372,"m":3068,"d":46,"f":3069,"v":3070,"n":23,"y":318,"u":3071},"custom-built handheld device",[],[],[3072],[2758,53,29,3073,3074],"Rotation, Walking and Campus datasets on the MIT campus","Sec. IV; Fig. 2a",{"c":372,"m":3076,"d":46,"f":3077,"v":3078,"n":23,"y":318,"u":3079},"custom-built indoor blimp",[],[],[3080],[2248,53,29,3081,3082],"ellipsoid polyurethane (vinyl) envelope 1.83 m long; 300 g payload; 3D-printed payload carrier; three 3.7 V Hubsan-X4 DC motors; four single-cell lithium-ion '750-milliampere' batteries (as written); about 30 min flight with all motors running","Sec. 3.2, 3.2.2, 3.2.3",{"c":372,"m":3084,"d":46,"f":3085,"v":3086,"n":23,"y":152,"u":3087},"custom-built RGB-D capture rig with IR projector (model not reported)",[],[],[3088],[3089,23,3054,3090,3091],"straub2019replica","collects time-aligned raw IMU, RGB, IR and wide-angle greyscale data","Sec. III, Fig. 4",{"c":372,"m":3093,"d":46,"f":3094,"v":3095,"n":23,"y":233,"u":3096},"custom-built wooden handheld frame with dual hold",[],[],[3097],[2265,53,29,3098,3099],"24 V battery; Ethernet to laptop; used for all evaluation recordings","Sec. 3.3, Fig. 5",{"c":372,"m":3101,"d":46,"f":3102,"v":3103,"n":23,"y":49,"u":3104},"custom-made platform with mecanum wheels",[],[],[3105],[3106,53,29,3107,3108],"hendrikx2021semanticbimloc","teleoperated on three routes of about 100 m each","Sec. V; Fig. 6a",{"c":372,"m":3110,"d":20,"f":3111,"v":3113,"n":23,"y":152,"u":3114},"customized DJI Matrice 100 quadrotor",[3112],"DJI",[],[3115],[3116,53,29,3117,1547],"zhen2019tunnellocalizability","manually flown from the map origin to the far end of the tunnel at about 0.7 m\u002Fs; GPS, compass and gimbal camera not used",{"c":372,"m":3119,"d":46,"f":3120,"v":3121,"n":23,"y":122,"u":3122},"customized multi-sensor vehicle platform of [8]",[],[],[3123],[201,53,3124,3125,3126],"multi-lidar calibration data of [8]","rotated one full cycle to create co-visible features","Supplementary I-B, Fig. 13",{"c":372,"m":3128,"d":46,"f":3129,"v":3130,"n":23,"y":49,"u":3131},"customized small-scale quadrotor UAV",[],[],[3132],[3133,53,29,3134,3135],"fastlio2021","280 mm wheelbase","Fig. 1; Sec. IV-B",{"c":33,"m":3137,"d":20,"f":3138,"v":3140,"n":23,"y":3141,"u":3142},"Cyberware 3030 MS",[3139],"Cyberware",[],1996,[3143],[3144,53,29,3145,3146],"curless1996volumetric","laser stripe optical triangulation scanner; laser sheet from a cylindrical lens with an off-axis CCD; object translates through the laser plane; modified to permit spacetime triangulation","Sec. 5.1, Fig. 1(b), Fig. 12 caption",{"c":651,"m":3148,"d":46,"f":3149,"v":3150,"n":23,"y":49,"u":3151},"D-GPS RTK system (footnote links DJI D-RTK)",[],[],[3152],[3153,41,29,3154,3155],"r2live2021","base station and mobile station labelled in arXiv v1 Fig. 6 (b) ('D-GPS RTK based station', 'D-GPS RTK mobile station'); the system is installed on the device (VoR Sec. VI-A)","VoR Sec. VI-A; Fig. 6 (b); Sec. VI-E; arXiv v1 Fig. 6",{"c":651,"m":3157,"d":46,"f":3158,"v":3159,"n":23,"y":132,"u":3160},"D-RTK (DJI Differential Real-Time Kinematic GNSS system)",[3112],[],[3161],[1136,41,1137,3162,3163],"precise ground truth for odometry","Sec. III-A (T-RO wording)",{"c":18,"m":3165,"d":46,"f":3166,"v":3167,"n":23,"y":233,"u":3168},"data logging computer (model not reported)",[],[],[3169],[1038,23,1039,3170,3171],"dedicated computer connected to the Phasma stick, Ubuntu 18.04 with ROS, hosts the PTP master clock (module alignment below 1 ms), records rosbags; Sec. VI attributes dropped LiDAR, camera and IMU frames to high load on 'the controller' without naming the device","Sec. III-E; Sec. VI",{"c":3173,"m":3174,"d":20,"f":3175,"v":3176,"n":23,"y":233,"u":3177},"event_camera","DAVIS346",[],[],[3178],[1562,23,1563,3179,3180],"stereo pair; 346 x 240 (Table II) or 346 x 260 (Sec. III-A3); FOV 67 deg vertical, 83 deg horizontal; internal IMU; infrared filters; hardware-synchronized events but 10 to 20 ms image acquisition offset","Sec. III-A3; Table II",{"c":3173,"m":3182,"d":20,"f":3183,"v":3184,"n":23,"y":132,"u":3185},"DAVIS346 (stereo pair)",[],[],[3186],[678,23,679,3187,3188],"67 deg vertical x 83 deg horizontal FOV; 346 x 240 (Table 2) or 346 x 260 frame images (Sec. 3.1.4); events 30 Hz topic, frames 20 Hz; master-slave sync; infrared filters; baseline 73 cm on vehicle","Table 2; Sec. 3.1.4; Sec. 3.2.3",{"c":18,"m":3190,"d":20,"f":3191,"v":3193,"n":23,"y":37,"u":3194},"Dell Precision 390",[3192],"Dell",[],[3195],[40,28,29,3196,3197],"dual-core Intel CPU 2.13 GHz, 3 GB RAM, NVIDIA Quadro FX 3500, Windows 7, Matlab","Sec. 5.2.3",{"c":882,"m":3199,"d":20,"f":3200,"v":3202,"n":23,"y":357,"u":3203},"Delphi ACC Radar (15 units)",[3201],"Delphi",[],[3204],[1615,53,29,1616,1617],{"c":1776,"m":3206,"d":46,"f":3207,"v":3208,"n":23,"y":132,"u":3209},"depth camera (model not named)",[],[],[3210],[3211,41,3212,3213,147],"mast3rslam2025","7-Scenes","depth images back-projected with dataset poses to form the reference cloud; default factory intrinsics",{"c":18,"m":3215,"d":46,"f":3216,"v":3217,"n":23,"y":318,"u":3218},"desktop computer",[],[],[3219],[3220,28,29,3221,3222],"yang2020gnc","not_reported (no model, CPU or memory given)","Sec. V-C (SOS SDP solved with GloptiPoly 3 in Matlab in about 80 ms)",{"c":18,"m":3224,"d":20,"f":3225,"v":3226,"n":23,"y":152,"u":3227},"desktop computer, i7 processor at 2.6 GHz, 16 GB memory",[98],[],[3228],[1174,28,29,3229,3230],"MATLAB implementation without hardware optimisation","Sec. 4.4",{"c":18,"m":3232,"d":46,"f":3233,"v":3234,"n":23,"y":318,"u":3235},"desktop CPU (model not reported)",[],[],[3236],[3237,28,29,3238,1069],"voxgraph2020","used to process the RGB-D industrial dataset",{"c":18,"m":3240,"d":46,"f":3241,"v":3242,"n":23,"y":289,"u":3243},"desktop machine (model not reported)",[],[],[3244],[3245,28,29,3246,3247],"chisel2015","used only for the fusion-time comparison in Fig. 9e","Sec. IV-C; Fig. 9e",{"c":18,"m":3249,"d":20,"f":3250,"v":3251,"n":23,"y":233,"u":3253},"desktop PC with 8-core Intel Core i7-10700U",[98],[3252],"desktop PC with 8-core Intel Core i7-10700U (as written)",[3254],[3255,28,29,569,3256],"fastlivo2022","Sec. VI-C",{"c":18,"m":3258,"d":20,"f":3259,"v":3261,"n":23,"y":346,"u":3262},"desktop PC with Intel Core i7-7820X CPU at 3.60 GHz and Nvidia Titan Xp GPU",[3260],"Intel, Nvidia",[],[3263],[3264,28,29,3265,1084],"cblox2018","used for the ICL-NUIM reconstruction comparison; the GPU was used only by ElasticFusion",{"c":18,"m":3267,"d":20,"f":3268,"v":3269,"n":23,"y":132,"u":3270},"desktop PC with Intel i7-10700K CPU",[98],[],[3271],[216,28,29,3272,3273],"32 GB RAM","Sec. IX-A",{"c":18,"m":3275,"d":20,"f":3276,"v":3277,"n":23,"y":122,"u":3278},"desktop PC with Intel i7-7700K",[],[],[3279],[164,28,29,3272,3280],"Sec. VI-E",{"c":18,"m":3282,"d":20,"f":3283,"v":3284,"n":23,"y":122,"u":3285},"desktop PC with Intel i7-8700 CPU",[98],[],[3286],[2452,28,29,3287,478],"3.2 GHz, 32 GB RAM",{"c":18,"m":3289,"d":20,"f":3290,"v":3291,"n":23,"y":318,"u":3292},"Desktop PC with Intel i7-9700K",[98],[],[3293],[2501,28,29,3294,3295],"4.0-4.8 GHz; 3 threads in parallel mode","Sec. V-C, Table I",{"c":18,"m":3297,"d":20,"f":3298,"v":3299,"n":41,"y":49,"u":3300},"desktop PC, Intel i7-9700K CPU, 32 GB RAM",[98],[],[3301,3303],[1747,28,29,569,3302],"VoR Sec. VI-E; Table IV",[3153,28,29,569,3304],"VoR Table II",{"c":18,"m":3306,"d":20,"f":3307,"v":3308,"n":23,"y":152,"u":3309},"desktop with Intel i7 processor",[98],[],[3310],[3311,28,29,3312,3313],"asadi2019imagebimslam","3.4 GHz, 6 cores","Sec. Discussion",{"c":18,"m":3315,"d":20,"f":3316,"v":3318,"n":23,"y":132,"u":3319},"desktop with Intel i9-13900KF CPU and NVIDIA RTX-4090 GPU",[3317],"Intel; NVIDIA",[],[3320],[1136,28,29,3321,898],"128 GB RAM",{"c":372,"m":3323,"d":46,"f":3324,"v":3326,"n":23,"y":132,"u":3327},"Dexory robot",[3325],"Dexory",[],[3328],[2298,53,29,3329,3330],"differential drive with front and back caster wheels; extendable 12 m tower; about 500 kg; provides wheel-encoder odometry","Sec. V-A-1; Sec. V-B",{"c":651,"m":3332,"d":46,"f":3333,"v":3334,"n":23,"y":24,"u":3335},"DGPS in RT-2 mode (model not reported)",[],[],[3336],[27,41,29,3337,3338],"up to 2 cm relative accuracy; part of the integrated INS\u002FDGPS vehicle navigation system","Secs. 5, 5.1",{"c":372,"m":3340,"d":46,"f":3341,"v":3342,"n":23,"y":132,"u":3343},"DIABLO wheeled bipedal robot",[],[],[3344],[1510,53,3345,3346,3347],"R-Campus (own experiment)","about 1400 m campus route at 1.2 m\u002Fs","Sec. V-C; Fig. 4B",{"c":33,"m":3349,"d":46,"f":3350,"v":3352,"n":23,"y":152,"u":3353},"DIDSON (dual-frequency identification sonar)",[3351],"SoundMetrics Corporation (per ref. [15], DIDSON 300)",[],[3354],[2111,53,29,3355,332],"96 beams, 2D transducer array; concentrator lens reduces vertical FOV to 1 degree (profiling mode); fixed number of profile scans per submap",{"c":651,"m":3357,"d":46,"f":3358,"v":3359,"n":23,"y":61,"u":3360},"differential GPS",[],[],[3361],[245,41,3362,3363,3364],"Victoria Park (Sydney)","used for evaluation only","Sec. 6",{"c":651,"m":3366,"d":46,"f":3367,"v":3368,"n":23,"y":554,"u":3369},"Differential GPS (receiver not stated)",[],[],[3370],[3371,41,3372,3373,3374],"kaess2008isam","Sydney Victoria Park","shown in Fig. 8 only for visual comparison; not used to obtain the results; unavailable in many places","Fig. 8 caption",{"c":651,"m":3376,"d":46,"f":3377,"v":3378,"n":23,"y":233,"u":3379},"Differential-GPS (D-GPS) real-time kinematic (RTK) system",[],[],[3380],[1747,41,29,3381,3382],"base and mobile stations (Fig. 5 b)","VoR Sec. VI-A1; Sec. VI-D",{"c":651,"m":3384,"d":20,"f":3385,"v":3386,"n":23,"y":152,"u":3387},"DJI A3 controller GPS receiver",[3112],[],[3388],[1083,41,29,3389,3390],"GPS position treated as ground truth","Sec. V-B, Fig. 6",{"c":662,"m":3392,"d":20,"f":3393,"v":3394,"n":23,"y":152,"u":3395},"DJI A3 controller inbuilt IMU",[3112],[],[3396],[1083,53,29,1272,3390],{"c":662,"m":3398,"d":20,"f":3399,"v":3400,"n":23,"y":346,"u":3401},"DJI A3 flight controller (ADXL278 and ADXRS290)",[3112],[],[3402],[1078,53,29,3403,3404],"100 Hz; also used for attitude stabilization control","Sec. IX-C-1, Fig. 21",{"c":662,"m":3406,"d":20,"f":3407,"v":3408,"n":23,"y":346,"u":3409},"DJI A3 flight controller built-in IMU (ADXL278 and ADXRS290)",[3112],[],[3410],[1078,53,29,2377,3411],"Sec. IX-B-1, Fig. 18",{"c":522,"m":3413,"d":46,"f":3414,"v":3415,"n":23,"y":346,"u":3416},"DJI compatible gimbal camera",[],[],[3417],[3418,952,29,3419,3420],"shang2018_uav_vslam","attached to the aircraft for data comparison; model not reported","Experimental setup",{"c":108,"m":3422,"d":20,"f":3423,"v":3424,"n":23,"y":1008,"u":3425},"DJI L1 LiDAR sensor",[3112],[],[3426],[3427,41,1137,3428,3429],"lemon2026","high-precision point cloud processed with DJI Terra","Sec. VII-D",{"c":372,"m":3431,"d":20,"f":3432,"v":3433,"n":23,"y":1008,"u":3434},"DJI M210 v2",[3112],[],[3435],[1155,23,3436,3437,332],"DRZ Living Lab","drone platform",{"c":372,"m":3439,"d":20,"f":3440,"v":3441,"n":23,"y":122,"u":3442},"DJI M300",[3112],[],[3443],[3444,53,29,3445,3446],"lin2023immesh","drone carrying Livox Avia and Hikvision camera","Sec. VIII-E, Fig. 11",{"c":372,"m":3448,"d":20,"f":3449,"v":3450,"n":23,"y":132,"u":3451},"DJI M300 RTK quadrotor",[3112],[],[3452],[216,23,1137,3453,157],"high-altitude aerial data collection",{"c":372,"m":3455,"d":20,"f":3456,"v":3457,"n":23,"y":233,"u":3458},"DJI M600 Pro",[3112],[],[3459],[3460,23,184,3461,3462],"nguyen2022ntuviral","hexacopter carrying a damped payload bay; triple GPS, compass and prism on top are not rigidly connected to the payload","Sec. 2, Fig. 2, Sec. 5.3",{"c":372,"m":3464,"d":20,"f":3465,"v":3466,"n":23,"y":122,"u":3467},"DJI M600 UAV",[3112],[],[3468],[1191,23,184,569,3469],"Sec. IV-A-2; Fig. 4(b)",{"c":18,"m":3471,"d":20,"f":3472,"v":3473,"n":23,"y":318,"u":3474},"DJI Manifold 2 onboard computer (i7-8550U)",[3112],[],[3475],[2501,28,29,3476,3295],"3.0-3.5 GHz; 3 threads in parallel mode",{"c":18,"m":3478,"d":20,"f":3479,"v":3480,"n":23,"y":49,"u":3481},"DJI Manifold 2-C4",[3112],[],[3482],[3483,28,29,3484,1072],"cai2021ikdtree","1.8 GHz quad-core Intel i7-8550U CPU, 8 GB RAM",{"c":18,"m":3486,"d":20,"f":3487,"v":3488,"n":23,"y":122,"u":3489},"DJI Manifold 2-C7",[3112],[],[3490],[2169,28,29,3491,3492],"1.8 GHz quad-core Intel i7-8550U, 8 GB RAM","Sec. 5.6; Sec. 6",{"c":18,"m":3494,"d":20,"f":3495,"v":3496,"n":2552,"y":49,"u":3500},"DJI Manifold 2C",[3112],[3497,3498,3499],"DJI Manifold 2-C","DJI Manifold-2C","DJI manifold-2c",[3501,3503,3505,3507,3508,3512,3516],[2352,28,29,3502,917],"additional onboard processor in the external sensor set",[3460,23,184,3504,917],"onboard computer storing rosbags; ground truth recorded on a separate computer and merged afterwards",[3133,28,29,3484,3506],"Fig. 1; Sec. IV-B; Sec. IV-D",[378,28,29,3484,167],[3255,23,3509,3510,3511],"FAST-LIVO private dataset","onboard computer of the data rig; Intel i7-8550u CPU and 8 GB RAM","Sec. VI-B1",[216,23,3513,3514,3515],"FAST-LIVO2 private dataset","onboard computer; Intel i7-8550u CPU, 8 GB RAM","Sec. VIII-B1",[1164,28,29,3517,3518],"4-core Intel Core i7-8550U; real-time CamVox","Sec. IV-D",{"c":18,"m":3520,"d":46,"f":3521,"v":3522,"n":23,"y":152,"u":3523},"DJI Manifold computer",[3112],[],[3524],[3116,28,29,3525,332],"2.32 GHz",{"c":18,"m":3527,"d":20,"f":3528,"v":3530,"n":41,"y":49,"u":3532},"DJI manifold-2c computation platform (Intel i7-8550u CPU, 8 GB RAM)",[3529],"DJI (as named)",[3531],"DJI manifold-2c computation platform (Intel i7-8550 u CPU, 8 GB RAM)",[3533,3536],[1747,28,29,3534,3535],"'OB' platform in Table IV","VoR Sec. VI-A1; Sec. VI-E; Table IV",[3153,28,29,3537,3538],"onboard runtime platform","VoR Sec. VI-A; Table II",{"c":18,"m":3540,"d":20,"f":3541,"v":3542,"n":23,"y":100,"u":3543},"DJI manifold-2c onboard computer (Intel i7-8550u CPU, 8GB RAM)",[3529],[],[3544],[599,23,1751,3545,3546],"onboard computer of the data-collection device","VoR Sec. VI-B1",{"c":372,"m":3548,"d":20,"f":3549,"v":3550,"n":23,"y":346,"u":3551},"DJI Matrice 600",[3112],[],[3552],[1948,53,3553,3554,1084],"courtyard MAV flight (own data)","MAV flown by a human operator along a building front at different heights; 2000 scans in 200 s",{"c":372,"m":3556,"d":20,"f":3557,"v":3558,"n":23,"y":346,"u":3559},"DJI Matrice 600 (written 'DJI Matrices 600')",[3112],[],[3560],[3418,53,29,3561,3562],"hexacopter; about 35 min flight without payload and 15 min with full payload; flown at 4 to 5 m height and 0.5 m\u002Fs","System architecture; Experimental setup",{"c":372,"m":3564,"d":20,"f":3565,"v":3566,"n":23,"y":132,"u":3568},"DJI Mini 2 Pro",[3112],[3567],"DJI Mini 2 Pro (as written)",[3569],[2706,53,29,3570,3571],"UAV video 1920 x 1080 pixels; flown at about 3.5 m in scene_1","Sec. 4.1; Sec. 4.2.1",{"c":522,"m":3573,"d":20,"f":3574,"v":3575,"n":23,"y":1008,"u":3576},"DJI Mini 4 Pro high-definition camera",[3112],[],[3577],[3578,53,29,3579,3580],"qian2026_tunnel2dgs","82.1 deg field of view, 24 mm equivalent focal length, f\u002F1.7; 40 s video at 1920x1080 over an 80 m section; 286 frames extracted, 210 retained with 70% to 80% interframe overlap","Materials, Experimental Data",{"c":372,"m":3582,"d":20,"f":3583,"v":3584,"n":23,"y":1008,"u":3585},"DJI Mini 4 Pro UAV",[3112],[],[3586],[3578,53,29,3587,3588],"Flown longitudinally near the tunnel central axis at 2.0 m\u002Fs, camera optical axis aligned with the flight direction","Materials, Experimental Data; Methods; Fig. 3",{"c":372,"m":3590,"d":46,"f":3591,"v":3592,"n":23,"y":1819,"u":3593},"DJI Osmo gimbal",[3112],[],[3594],[2091,23,2092,3595,147],"stabilizes the DJI X5R",{"c":372,"m":3597,"d":20,"f":3598,"v":3599,"n":23,"y":346,"u":3600},"DJI S1000",[3112],[],[3601],[2905,53,29,3602,3603],"drone carrying a sensor suite identical to Fig. 16b; flown at 2 to 3 m\u002Fs","Sec. 10.3, Fig. 32",{"c":522,"m":3605,"d":20,"f":3606,"v":3607,"n":23,"y":1819,"u":3608},"DJI Zenmuse X5R with Olympus M.Zuiko 12mm f\u002F2.0 lens",[3112],[],[3609],[2091,23,2092,3610,3611],"rolling shutter; 4K (> 8 MP) video; 84 deg diagonal FOV; stabilized by a DJI Osmo gimbal; used for scenes marked D in Table 1","Sec. 4.2; Table 1",{"c":522,"m":3613,"d":46,"f":3614,"v":3615,"n":23,"y":1819,"u":3616},"down-facing perspective camera (model not reported)",[],[],[3617],[3618,53,3619,3620,3621],"svo2017","own circle dataset","circle flight on a micro aerial vehicle","Sec. XI-B-4",{"c":944,"m":3623,"d":20,"f":3624,"v":3626,"n":23,"y":318,"u":3627},"DPI-8S",[3625],"DotProduct",[],[3628],[951,952,29,3629,3630],"handheld tablet with external RGB-D sensor; working range 0.6-3.7 m; 1.36 kg; acquisition rate N\u002FA; accuracy >98.6%; 2-3 h","Sec. 2.1.2; Tables 1-3, 6; Fig. 2b",{"c":944,"m":3632,"d":20,"f":3633,"v":3634,"n":23,"y":318,"u":3635},"DPI-8SR",[3625],[],[3636],[951,952,29,3637,3630],"handheld tablet with external RGB-D sensor; working range 0.3-2 m; 1.36 kg; accuracy >98.6%; 2-3 h",{"c":353,"m":3639,"d":46,"f":3640,"v":3641,"n":23,"y":759,"u":3642},"drive-shaft encoders",[],[],[3643],[888,53,29,3644,332],"measure vehicle speed",{"c":372,"m":3646,"d":46,"f":3647,"v":3648,"n":23,"y":100,"u":3649},"drone",[],[],[3650],[211,23,212,3651,214],"drone-collected dataset",{"c":372,"m":3653,"d":46,"f":3654,"v":3655,"n":23,"y":49,"u":3656},"drone (EuRoC micro aerial vehicle)",[],[],[3657],[3658,23,1079,3659,3660],"orbslam3_2021","11 sequences in a machine hall and two Vicon rooms","Abstract; Sec. VII-C",{"c":372,"m":3662,"d":46,"f":3663,"v":3664,"n":23,"y":1008,"u":3665},"drone (UAS; model not reported)",[],[],[3666],[3667,53,29,3668,3669],"chowdhury2026_gema","two helical paths (façade level and elevated roof view), slow controlled flight of 2 to 4 min per building; 60 to 130 low-blur frames used","Sec. 3.6; Sec. 5",{"c":33,"m":3671,"d":46,"f":3672,"v":3673,"n":23,"y":233,"u":3674},"drone internal barometer and IMU",[],[],[3675],[3460,41,184,3676,3677],"internal barometer and IMU of the custom DJI M600 Pro drone, independent of the payload sensors; altitude estimate used in hand-eye calibration to align the ground truth","Sec. 5.3",{"c":522,"m":3679,"d":46,"f":3680,"v":3681,"n":23,"y":1008,"u":3682},"drone video camera (model not reported)",[],[],[3683],[3667,53,29,3684,3685],"original videos between 1980x1020 and 3840x2160 (4K 30 fps or 1080p 30 fps), downscaled to 1920x1080; COLMAP needed a radial camera model, a pinhole model degraded results","Sec. 3.6; Sec. 4.1.3; Sec. 5",{"c":372,"m":3687,"d":46,"f":3688,"v":3689,"n":23,"y":49,"u":3690},"DS drone (Team Explorer)",[],[],[3691],[3692,53,29,3693,3694],"superodom2021","deployed in the DARPA Subterranean Challenge; hand-carried for four sequences, autonomous flight in Dust","Sec. V-A, Fig. 4(e)",{"c":522,"m":3696,"d":46,"f":3697,"v":3698,"n":23,"y":100,"u":3699},"DSLR (model not stated in the paper)",[],[],[3700],[3701,23,3702,3703,313],"splatam2024","ScanNet++","ScanNet++ DSLR captures with complete dense trajectories and a second capture loop for novel views",{"c":522,"m":3705,"d":46,"f":3706,"v":3707,"n":23,"y":152,"u":3708},"DSLR camera (model not reported)",[],[],[3709],[431,53,29,3710,433],"RGB data for panoramic texture mapping of scans",{"c":18,"m":3712,"d":20,"f":3713,"v":3714,"n":23,"y":299,"u":3715},"dual quad-core Intel Nehalem-EP W5590",[98],[],[3716],[3717,28,29,3718,441],"museth2013vdb","4x32KB L1, 4x256KB L2, 8MB L3, 48 GB DDR3-1333 RAM, Red Hat Enterprise Linux 5.4",{"c":372,"m":3720,"d":20,"f":3721,"v":3722,"n":23,"y":318,"u":3723},"Duffy 21",[],[],[3724],[2758,53,29,3725,3726],"electric boat; Amsterdam canal dataset (about 3 h)","Sec. IV; Sec. IV-E; Fig. 2c",{"c":33,"m":3728,"d":46,"f":3729,"v":3730,"n":23,"y":122,"u":3731},"dynamic reference plane kit: printed image plane, two bubble-tube leveling devices, adjustable tripod",[],[],[3732],[3733,53,29,3734,3735],"hsieh2023slamarbim","image plane of the same size as the BIM benchmark plane; used with leveling and ranging tools to measure dx, dy, dz","Sec. 4.2.3, 6.1, Figs. 10, 11, 15, 16",{"c":33,"m":3737,"d":46,"f":3738,"v":3739,"n":23,"y":233,"u":3740},"Dynamixel actuator (model not stated)",[],[],[3741],[3742,53,29,3743,3744],"roloam2022","rotates the LiDAR about the roll axis between +\u002F-40 deg; readings used for the LiDAR-to-robot transform","Sec. III-A, IV-A",{"c":33,"m":3746,"d":20,"f":3747,"v":3748,"n":23,"y":289,"u":3749},"Dynamixel MX-28",[],[],[3750],[3751,53,3752,3753,3754],"razlaw2015evaluation","Razlaw et al. MAV laser datasets","servo actuator (72 g) continuously rotating the LRF via a slip ring; whole 3D scanner about 400 g, mounted pitched down 45 deg","Sec. III, Fig. 2",{"c":33,"m":3756,"d":46,"f":3757,"v":3758,"n":23,"y":122,"u":3759},"Dynamixel servomotor (model not_reported)",[],[],[3760],[3761,952,29,3762,3763],"trybala2023lowcosttunnel","rotates the VLP-16 in the in-house actuated rig","Sec. 2.3",{"c":882,"m":3765,"d":20,"f":3766,"v":3767,"n":23,"y":132,"u":3769},"Eagle Ocuill G7",[],[3768],"Eagle Ocuill G7 (as written)",[3770],[2483,23,3771,3772,3773],"NTU4DRadLM","4D millimeter-wave radar; used only for cross-modal relocalization on the Gaussian map","Sec. IV-B; Sec. IV-F",{"c":522,"m":3775,"d":46,"f":3776,"v":3777,"n":23,"y":132,"u":3778},"eight omnidirectional cameras (front-facing used)",[],[],[3779],[216,23,3780,185,157],"Hilti (robot-mounted)",{"c":662,"m":3782,"d":46,"f":3783,"v":3784,"n":23,"y":233,"u":3785},"Ellipse-A",[],[],[3786],[3787,53,29,3788,3789],"nubert2022constructionfusion","mounted at the bottom of the cabin of the first machine; 100 Hz","Sec. V-a",{"c":18,"m":3791,"d":20,"f":3792,"v":3793,"n":23,"y":346,"u":3794},"embedded computer with 1.8 GHz i7 dual-core processor",[],[],[3795],[2905,28,29,3796,3797],"four threads, on Contour","Sec. 10.2, Table 5",{"c":662,"m":3799,"d":20,"f":3800,"v":3802,"n":23,"y":100,"u":3803},"embedded IMU of the Livox Avia",[3801],"Livox",[],[3804],[3805,53,3806,569,3807],"ltaom2024","multilevel building (authors' own data)","Sec. 5.4",{"c":944,"m":3809,"d":20,"f":3810,"v":3812,"n":28,"y":233,"u":3813},"Emesent Hovermap",[3811],"Emesent",[],[3814,3817,3820],[1396,53,29,3815,3816],"payload on the aerial robots with a spinning Velodyne VLP-16; runs Wildcat onboard","Sec. III-D-1",[3818,53,29,3819,2530],"fahle2022geotechmls","Velodyne VLP-16 rotated 360 deg about the long axis at 0.5 Hz, MEMS IMU; implementation of WILDCAT SLAM; near 360 x 360 deg FOV",[3821,53,29,3822,3823],"hawley2022tunnelleakage","point clouds accurate to +\u002F-30 mm, +\u002F-15 mm achievable with post-processing; local alignment repeatability +\u002F-10 mm; handheld, vehicle or drone mounted","Sec. 4.1, Fig. 5",{"c":944,"m":3825,"d":20,"f":3826,"v":3828,"n":2256,"y":100,"u":3829},"Emesent Hovermap ST-X",[3811,3827],"Emesent Pty Ltd., Australia",[],[3830,3833,3835,3839,3842],[1986,53,29,3831,3832],"32-channel LiDAR; LiDAR precision 10 mm; mapping accuracy 15 mm general, 10 mm indoor and underground; local accuracy 5 mm; range 0.5-300 m; FOV 290 x 360 deg; up to 640,000 pts\u002Fs (single return) or 1.92 M pts\u002Fs (3 returns); 1.57 kg; processed in Emesent Aura","The used devices and software; Fig. 2c",[1990,53,29,3834,3832],"32-channel LiDAR, precision 10 mm per channel; mapping accuracy 15 mm general, 10 mm indoor and underground; range 0.5-300 m; up to 640,000 pts\u002Fs single return or 1.92 M pts\u002Fs multi-return; FOV 290 x 360 deg; 1.57 kg; backpack-mounted; four passes of about 15 min; Emesent Aura 2.0.1",[3836,53,29,3837,3838],"xu2025pointleveluncertainty","accuracy ±15 mm (manufacturer specification in typical environment)","Sec. 3.1; Table 1",[3840,53,29,3837,3841],"xu2026propagationprediction","Sec. 4.1; Table 4",[3843,952,29,3844,3845],"stroner2025minetunnel","32 channels, 905 nm, 640,000 pts\u002Fs, 0.5-300 m, range accuracy 10 mm; declared mapping accuracy 15 mm (10 mm indoors); 1.57 kg; processed in Emesent Aura; run one-way and two-way","Sec. 2.2; Sec. 2.4; Table 1",{"c":33,"m":3847,"d":46,"f":3848,"v":3849,"n":23,"y":132,"u":3850},"emulated pulse-per-second (PPS) synchronization",[],[],[3851],[1136,53,29,3852,3853],"temporal alignment of LiDAR, IMU and camera","Sec. II",{"c":33,"m":3855,"d":46,"f":3856,"v":3857,"n":23,"y":1421,"u":3858},"encoder (model not reported)",[],[],[3859],[3860,53,29,3861,1084],"zhang2016degeneracy","measures motor rotation angle with 0.25 deg resolution",{"c":33,"m":3863,"d":46,"f":3864,"v":3865,"n":23,"y":346,"u":3866},"encoder (spinning mechanism)",[],[],[3867],[3868,53,29,3869,3870],"elasticlidarfusion2018","part of the hand-held spinning LiDAR","Sec. VII; Fig. 2a caption",{"c":33,"m":3872,"d":46,"f":3873,"v":3874,"n":23,"y":233,"u":3875},"encoder (spinning single-beam device)",[],[],[3876],[2493,53,29,569,2494],{"c":33,"m":3878,"d":46,"f":3879,"v":3880,"n":23,"y":455,"u":3881},"encoder on the spinning mount",[],[],[3882],[476,53,29,3883,3853],"described as accurate; gives laser scan poses relative to the vehicle",{"c":372,"m":3885,"d":46,"f":3886,"v":3887,"n":23,"y":132,"u":3888},"ENWIDE hand-held sensor pack",[],[],[3889],[1520,23,3890,3891,3892],"ENWIDE (Ulmberg bicycle tunnel)","no kinematic measurements; COIN-LIO used as pose prior","Sec. V, V-C; Fig. 5-C",{"c":662,"m":3894,"d":20,"f":3895,"v":3897,"n":23,"y":122,"u":3898},"Epson G365",[3896],"Epson",[],[3899],[1462,53,29,3900,1104],"400 Hz; initial bias 0.1 deg\u002Fs and 3 mg; bias stability 1.2 deg\u002Fh and 15 mg (ANYmal C100: LSM, SMM)",{"c":662,"m":3902,"d":20,"f":3903,"v":3904,"n":23,"y":100,"u":3905},"Epson M-G365 (Fig. 2: 'Epson-G365')",[3896],[],[3906],[1371,23,1372,3907,3908],"200 Hz; Allan-variance noise calibration; LiDAR-IMU extrinsics from CAD","Sec. 3.1.1; Fig. 2",{"c":662,"m":3910,"d":46,"f":3911,"v":3912,"n":23,"y":152,"u":3913},"EuRoC IMU (model not reported)",[],[],[3914],[2555,23,1079,3915,1176],"synchronized with the cameras; used by OKVIS and MSCKF odometry",{"c":662,"m":3917,"d":46,"f":3918,"v":3919,"n":23,"y":49,"u":3920},"EuRoC IMU (model not stated)",[],[],[3921],[3658,23,1079,1272,3922],"Table VI",{"c":522,"m":3924,"d":46,"f":3925,"v":3926,"n":23,"y":152,"u":3927},"EuRoC MAV camera (model not reported; monocular use)",[],[],[3928],[2936,23,1054,3929,917],"11 sequences with ground truth",{"c":522,"m":3931,"d":46,"f":3932,"v":3933,"n":23,"y":233,"u":3934},"EuRoC MAV camera and IMU (models not reported in this paper)",[],[],[3935],[742,23,1054,3936,3937],"monocular images and IMU data recorded by a flying drone","Secs. I, IV-A",{"c":662,"m":3939,"d":46,"f":3940,"v":3941,"n":952,"y":318,"u":3942},"EuRoC MAV IMU (model not reported in this paper)",[],[],[3943,3946,3949,3952],[3944,23,1054,3945,1204],"okvis2x2025","IMU measurements recorded with the stereo images by a drone",[3947,23,1054,3948,332],"cioffi2022ctvsdt","camera-IMU time offset assumed zero",[2953,23,1054,3950,3951],"high-rate inertial measurements; state estimates output at IMU rate (above 200 Hz)","Sec. II; Sec. III-D",[3953,23,1054,3954,3955],"basalt2020","not_reported (IMU measurements preintegrated between consecutive frames)","Secs. IV-B3, VI",{"c":1689,"m":3957,"d":46,"f":3958,"v":3959,"n":2256,"y":346,"u":3960},"EuRoC MAV stereo camera (model not reported in this paper)",[],[],[3961,3963,3966,3968,3970],[3947,23,1054,3962,332],"hardware-synchronized with the IMU; only right-camera images used",[349,23,1054,3964,3965],"11 stereo-inertial sequences, 19 minutes; left and right videos used separately as monocular input; no photometric calibration or exposure times; shaky initialisation segments cropped","Sec. 4 datasets and Methodology",[1071,23,1054,3967,1072],"20 Hz stereo images",[2953,23,1054,3969,2440],"stereo frames used as Kimera input (monocular mode also supported)",[3953,23,1054,3971,3972],"MH_05 has 2273 stereo frames over 114 s; V2_03 has more than 400 missing frames for one camera","Sec. VI-d; Table I note",{"c":1689,"m":3974,"d":46,"f":3975,"v":3976,"n":23,"y":346,"u":3977},"EuRoC MAV stereo camera (model not reported; used monocularly)",[],[],[3978],[3979,23,1054,3980,469],"ldso2018","stereo images with synchronised IMU and ground-truth trajectories; 11 sequences",{"c":1689,"m":3982,"d":46,"f":3983,"v":3984,"n":23,"y":132,"u":3985},"EuRoC MAV stereo camera (model not reported)",[],[],[3986],[3944,23,1054,3987,1204],"stereo images with IMU on a drone",{"c":1689,"m":3989,"d":46,"f":3990,"v":3991,"n":23,"y":152,"u":3992},"EuRoC stereo camera (model not reported)",[],[],[3993],[2555,23,1079,3994,1176],"stereo images at 20 Hz on a drone; exposure not synchronized between cameras, exposure compensation applied",{"c":1689,"m":3996,"d":46,"f":3997,"v":3998,"n":41,"y":1819,"u":3999},"EuRoC stereo camera (model not stated)",[],[],[4000,4002],[3658,23,1079,4001,3922],"752x480 at 20 Hz; 1000 to 1200 ORB features per image in the experiments",[1783,23,1079,4003,469],"baseline about 11 cm, WVGA images at 20 Hz",{"c":33,"m":4005,"d":46,"f":4006,"v":4007,"n":23,"y":318,"u":4008},"EuRoC V1 and V2 ground-truth point cloud (acquisition device not stated in this paper)",[],[],[4009],[2953,41,1054,4010,2788],"used for mesh accuracy and completeness after ICP registration",{"c":33,"m":4012,"d":46,"f":4013,"v":4014,"n":23,"y":132,"u":4015},"EuRoC Vicon-room reference point clouds",[],[],[4016],[3944,41,1054,4017,1204],"mm-level accurate point clouds used for mesh accuracy and completeness",{"c":651,"m":4019,"d":20,"f":4020,"v":4022,"n":23,"y":132,"u":4023},"EVK-7P",[4021],"u-blox",[],[4024],[225,53,226,185,228],{"c":944,"m":4026,"d":46,"f":4027,"v":4028,"n":23,"y":346,"u":4029},"experimental handheld 3D spinning LiDAR (as written; builder not stated)",[],[],[4030],[3868,53,29,4031,4032],"integrates 2D laser, encoder, IMU, colour camera and thermal camera","Fig. 2a; Sec. VII",{"c":372,"m":4034,"d":46,"f":4035,"v":4036,"n":23,"y":37,"u":4037},"experimental vehicle AnnieWay",[],[],[4038],[4039,53,4040,4041,4042],"velodyneslam2011","Velodyne SLAM dataset (AnnieWay, two scenarios)","scanner mounted on top of the vehicle; scenario 1 about 1.3 km and scenario 2 about 1.1 km, both with a bridge","Sec. I; Sec. III; Fig. 4",{"c":33,"m":4044,"d":46,"f":4045,"v":4046,"n":23,"y":122,"u":4047},"external motion capture system (model not named)",[],[],[4048],[4049,41,104,4050,4051],"pointslam2023","provides TUM-RGBD ground-truth poses","Sec. 4 Datasets",{"c":33,"m":4053,"d":46,"f":4054,"v":4055,"n":41,"y":299,"u":4056},"external motion capture system (model not reported)",[],[],[4057,4060],[1050,41,29,4058,4059],"pose ground truth; some bad tracking filtered out in the fast-motion run","Secs. IV-A, IV-C",[4061,41,991,4062,127],"dvoslam2013","accurate ground-truth trajectory",{"c":33,"m":4064,"d":46,"f":4065,"v":4066,"n":23,"y":289,"u":4067},"external motion capture system (model not stated)",[],[],[4068],[2595,41,991,4069,4070],"provides TUM RGB-D ground truth","Sec. VIII-B",{"c":33,"m":4072,"d":46,"f":4073,"v":4074,"n":23,"y":100,"u":4075},"external motion capture system (not named)",[],[],[4076],[103,41,104,4077,4051],"source of TUM-RGBD ground-truth poses",{"c":33,"m":4079,"d":46,"f":4080,"v":4081,"n":23,"y":270,"u":4082},"external optical motion capture systems (models not reported)",[],[],[4083],[2944,41,4084,4085,3364],"TUM RGB-D and authors' object tracking dataset","ground-truth camera poses of the TUM benchmark and the authors' object dataset",{"c":651,"m":4087,"d":20,"f":4088,"v":4089,"n":23,"y":100,"u":4090},"F9P",[4021],[],[4091],[4092,53,4093,4094,478],"glio2024","UrbanNav (Hong Kong)","low-cost receiver; raw single-frequency GPS, BeiDou, Galileo and GLONASS at 10 Hz",{"c":372,"m":4096,"d":46,"f":4097,"v":4098,"n":23,"y":346,"u":4099},"FALCON quadrotor",[],[],[4100],[4101,53,29,4102,4103],"smsckf2018","3 kg; synchronised stereo cameras and IMU, a laser scanner and a downward-facing lidar; only stereo and IMU used for estimation","Fig. 1",{"c":2807,"m":4105,"d":20,"f":4106,"v":4108,"n":23,"y":71,"u":4109},"Faro 880",[4107],"Faro",[],[4110],[915,53,29,4111,4112],"AM-CW phase-difference rangefinder with nominal unit lengths 1.2, 9.6 and 76.8 m; 320 deg vertical and 180 deg horizontal field of view (monogon deflection); 1\u002F4 resolution setting with 0.044 deg angular sampling; manufacturer linearity error \u003C= 3 mm at 10 m; formerly sold as iQsun 880","Sec. 1.1, 3.1, 3.2",{"c":2807,"m":4114,"d":20,"f":4115,"v":4117,"n":41,"y":318,"u":4119},"FARO Focus",[4116],"FARO",[4118],"FaroFocus",[4120,4123],[4121,41,29,4122,1204],"glim2024","survey-grade LiDAR; environment point cloud to which sensor trajectories are aligned for ground truth",[4124,23,4125,4126,691],"nikoohemat2020indoor3d","Penthouse (Mura et al.)","terrestrial laser scanner; e \u003C 0.01 m; 2.5 M points",{"c":2807,"m":4128,"d":20,"f":4129,"v":4130,"n":28,"y":299,"u":4133},"FARO Focus 3D",[4116,4107],[4131,4132],"Faro Focus 3D","Faro Focus3D",[4134,4137,4140],[273,53,29,4135,4136],"range 0.6 m to 120 m; up to 976,000 points per second; ranging systematic error +\u002F-2 mm at 10 m and 25 m; random error 0.5 mm to 2 mm at 10 m and 25 m (indicative values); scans saved as PTX with FARO Scene; high-accuracy settings made scanning about five times slower than standard","Sec. 2.2, 7.3, 8.1.2",[771,23,4138,569,4139],"ETH PRS TLS data set (reviewed)","Table 1; Sec. 3.1",[4141,41,29,4142,1667],"thomson2013mlsindoor","phase-shift; resolution 1\u002F8; about 6 m standoff; about 3:44 min and 10 million points per scan; 12 scans in about 5 h including control survey; 32 tie points",{"c":2807,"m":4144,"d":20,"f":4145,"v":4146,"n":23,"y":100,"u":4147},"FARO Focus 3D S120",[4116],[],[4148],[1371,41,1372,4149,4150],"range up to 120 m, up to 976K points\u002Fs, range error within +\u002F-2 mm; loop closure applied, 96% of scans below 2 mm position uncertainty; Supp. B.3 states ground-truth models accurate within 10 cm; maps of urban (350 x 350 m), caves (150 x 200 m), tunnels (100 x 200 m), CMU campus (200 x 200 m)","Sec. 3.2; Supp. B.3",{"c":2807,"m":4152,"d":20,"f":4153,"v":4154,"n":41,"y":318,"u":4156},"FARO Focus 3D X 330",[4116],[4155],"FARO Focus3D X330",[4157,4160],[3818,41,29,4158,4159],"range up to 330 m; range error and noise equivalent to the S70; stations 10 m apart at Edgar, aligned to mine survey data with spherical targets","Sec. 3.2, Sec. 3.3",[4161,41,29,4162,2530],"salgues2020mmsindoor","static scanner, target-based georeferenced reference clouds for all three sites; plane-fit noise 3.0 mm",{"c":2807,"m":4164,"d":20,"f":4165,"v":4166,"n":23,"y":346,"u":4167},"FARO Focus 3D X120",[4116],[],[4168],[2795,41,29,4169,4170],"TLS reference; scan registration checked with target check points (mean residual 12 mm courtyard, 18 mm village)","Sec. Courtyard (C), Fortified village (E); Tables 11, 15",{"c":2807,"m":4172,"d":20,"f":4173,"v":4174,"n":23,"y":1819,"u":4175},"FARO Focus 3D X330 HDR",[4116],[],[4176],[2091,41,2092,4177,917],"range 330 m; 360 deg horizontal, 300 deg vertical (60 deg blind cone downward); ranging noise 0.1 mm at 10.2 m and 0.3 mm at 22.7 m at calibration; up to 976,000 points\u002Fs; full-resolution omnidirectional scan 2 h with 0.3 mm lateral spacing at 2 m; usually run at half or quarter resolution; scan positions: 4 for small statues, 8 to 10 for mid-sized structures such as the train, 14 for Palace and 17 for Temple; counts for the indoor scenes are not given",{"c":2807,"m":4179,"d":20,"f":4180,"v":4181,"n":23,"y":152,"u":4182},"Faro Focus M",[4116],[],[4183],[3036,41,29,4184,4185],"360 deg horizontal, 305 deg vertical, 488,000 points\u002Fs, 70 m max distance, ranging error +-3 mm at 25 m; 6 stations in about 2.5 h","Sec. Evaluation Metrics",{"c":2807,"m":4187,"d":20,"f":4188,"v":4189,"n":41,"y":132,"u":4190},"FARO Focus M70",[4116],[],[4191,4194],[1904,53,29,4192,4193],"1\u002F8 resolution, 12.3 mm point spacing at 10 m, 80 s per scan, colour not used; triggered wirelessly at planned scan positions","Sec. 3.1, Sec. 4.1, Table 2",[4195,23,4196,4197,4198],"rauch2025rohbau3d","Rohbau3D","up to 1 million points\u002Fs; colour from HDR images mapped in post-processing; manufacturer 3D accuracy 2 mm at 10 m, 3 mm at 25 m plus 0.1 mm\u002Fm beyond; 25 m radius filter applied","Methods, Data acquisition, Fig. 2",{"c":2807,"m":4200,"d":20,"f":4201,"v":4202,"n":23,"y":132,"u":4203},"FARO Focus Premium 70",[4116],[],[4204],[3843,952,29,4205,4206],"1553.5 nm, FoV 360 x 300 deg, 2 Mpts\u002Fs, 70 m, 1 mm, 19 arcsec; 27 stations (mean point error 3.3 mm)","Sec. 2.2; Sec. 3.2; Table 1",{"c":2807,"m":4208,"d":20,"f":4209,"v":4210,"n":41,"y":233,"u":4212},"FARO Focus S70",[4116],[4211],"Faro Focus S 70",[4213,4215],[3818,41,29,4214,4159],"range up to 70 m; up to 1 M pts\u002Fs; 1 mm range accuracy; 2 mm 3D point accuracy; 0.15 mm noise at 10 m; used at 1\u002F8 resolution and 3x quality, 10.9 M points per scan (~12 mm spacing), 1.5 min per scan; three stations about 15 m apart at Mine-A",[1296,41,29,4216,4217],"reference scan of the office; maximum registration point error 0.35 cm","Table 2; Sec. 4.5",{"c":944,"m":4219,"d":20,"f":4220,"v":4221,"n":23,"y":233,"u":4222},"FARO Focus Swift",[4116],[],[4223],[4224,952,29,4225,4226],"elhashash2022mobilemappingsystems","trolley, 2020, indoor; HDR camera; FARO Focus laser scanner with FARO ScanPlan 2D mapper; IMU yes, GPS no; 0.2 cm relative accuracy at 10 m range, 0.1 cm absolute accuracy (manufacturer)","Table 4; Sec. 4.3",{"c":2807,"m":4228,"d":20,"f":4229,"v":4230,"n":23,"y":37,"u":4231},"FARO LS880 HE",[4116],[],[4232],[40,53,29,4233,4234],"resolution set to 1\u002F4 of full; room point cloud over 20 million points; written 'LS880 HE80' in Sec. 6","Sec. 5.1, 6",{"c":944,"m":4236,"d":20,"f":4237,"v":4238,"n":23,"y":132,"u":4239},"FARO Orbis",[4116],[],[4240],[3843,952,29,4241,4242],"32 channels, 905 nm, 640,000 pts\u002Fs, 120 m, FoV 290 x 360 deg, 8 MP 360-degree camera; SD 5 mm in text (10 mm range accuracy in Table 1); 2.1 kg + 0.95 kg logger","Sec. 2.2; Table 1",{"c":108,"m":4244,"d":20,"f":4245,"v":4246,"n":23,"y":299,"u":4247},"FARO PHOTON 120",[4116],[],[4248],[1606,952,29,4249,4250],"phase difference; 122 to 976 kHz; max range 120 m (reflectivity 90%); range accuracy +\u002F-2 mm at 25 m; vertical FOV 320 deg; used as helical 2D profiler with horizontal rotation disabled; 14.5 kg","Sec. 3.1, Table 3",{"c":33,"m":4252,"d":20,"f":4253,"v":4254,"n":23,"y":71,"u":4255},"Faro planar targets (A4 and A5 size)",[4107],[],[4256],[915,53,29,4257,4258],"A4 targets on rigid backing on walls, floor and ceiling; A5 only in calibration 1; 62 to 181 targets per dataset","Sec. 3.2, Table 1",{"c":33,"m":4260,"d":20,"f":4261,"v":4262,"n":23,"y":152,"u":4263},"FARO Quantum FaroArm",[4116],[],[4264],[3036,53,29,4265,4266],"3D scan of the sensor mounts for initial extrinsic calibration","Sec. Sensor Calibration",{"c":2807,"m":4268,"d":46,"f":4269,"v":4270,"n":23,"y":152,"u":4271},"Faro time-of-flight laser scanner (model not reported)",[4107],[],[4272],[155,41,29,4273,4274],"scans used as the reference model for mean registration error","Sec. I; Sec. VIII-B",{"c":33,"m":4276,"d":46,"f":4277,"v":4278,"n":23,"y":233,"u":4279},"fiber optic gyro (model not stated)",[],[],[4280],[668,41,4281,4282,4283],"MulRan (DCC03)","part of the MulRan ground-truth solution","Sec. VI-A2",{"c":33,"m":4285,"d":46,"f":4286,"v":4287,"n":23,"y":49,"u":4288},"fiducial markers and objects with known ground-truth locations (provided by DARPA)",[],[],[4289],[4290,41,29,4291,4292],"ebadi2021dareslam","known locations enforced in each robot's pose graph to build proxy ground-truth trajectories; object positions also used to score object localization error","Sec. 4, Sec. 4.3",{"c":1689,"m":4294,"d":20,"f":4295,"v":4297,"n":23,"y":233,"u":4298},"FILR BFS-U3-31S4C",[4296],"FILR (as printed)",[],[4299],[1562,23,1563,4300,4301],"two global-shutter colour cameras; 1024 x 768 at 20 Hz; FOV 66.5 deg vertical, 82.9 deg horizontal; external trigger; fixed exposure; average timestamp difference below 1 ms","Sec. III-A2; Table II",{"c":522,"m":4303,"d":46,"f":4304,"v":4305,"n":23,"y":252,"u":4306},"FireWire cameras (eight, custom rig; model not reported)",[],[],[4307],[255,53,29,4308,4309],"eight cameras distributed equally along a circle and connected to an on-board laptop; rig calibrated in advance; 260 joint images up to 2 m apart","Sec. 8; Fig. 15",{"c":522,"m":4311,"d":46,"f":4312,"v":4313,"n":23,"y":122,"u":4314},"first-person-view (FPV) camera",[],[],[4315],[2169,23,4316,4317,4318],"Point-LIO own sequences (Livox Avia sensor suite)","aligned with LiDAR FoV; used only for visual illustration","Sec. 5.2; Fig. 3a; Sec. 7.1",{"c":522,"m":4320,"d":46,"f":4321,"v":4322,"n":23,"y":122,"u":4323},"five cameras (Hilti 2021 rig; model not stated in paper)",[],[],[4324],[1029,23,1030,4325,332],"five cameras; frontal camera or stereo pair used for ROVIO\u002FOKVIS odometry, all five for loop closure",{"c":1776,"m":4327,"d":20,"f":4328,"v":4329,"n":23,"y":100,"u":4330},"five short-range depth cameras (RA2)",[],[],[4331],[2281,53,29,4332,4333],"used for perception","VoR Sec. 4.2.2",{"c":522,"m":4335,"d":46,"f":4336,"v":4337,"n":23,"y":132,"u":4338},"five wide-angle cameras (front-facing used)",[],[],[4339],[216,23,2231,4340,157],"40 Hz downsampled to 10 Hz; 752x480 grayscale",{"c":522,"m":4342,"d":20,"f":4343,"v":4345,"n":23,"y":122,"u":4346},"FL3-U3-13E4M-C (text also names FL3-FW-14S3M-C)",[4344],"FLIR",[],[4347],[4348,53,4349,4350,4351],"sdvloam2023","SDV-LOAM own platform (qualitative)","grayscale images 1280 x 1040 at 60 Hz","Sec. VII; Fig. 6",{"c":33,"m":4353,"d":20,"f":4354,"v":4355,"n":23,"y":37,"u":4356},"flatness defect test-bed boards (3 boards)",[],[],[4357],[4358,41,29,4359,4360],"tang2011flatness","flat boards verified within 1 mm with a straightedge; grey clay defects 3-50 cm diameter and 1-7 mm thick plus a 7 mm, 50 cm square; one defect-free control board; checkerboard fiducials; ground truth labelled from physical measurements","Framework, Flatness Defect Detection Test Bed; Fig. 3",{"c":944,"m":4362,"d":46,"f":4363,"v":4364,"n":23,"y":233,"u":4365},"FlatPack (CSIRO hand-held perception pack)",[2644],[],[4366],[668,53,4367,4368,4369],"QCAT (FlatPack)","fixed VLP-16 with 30 deg vertical FoV","Sec. VI-A3; Fig. 6",{"c":1689,"m":4371,"d":20,"f":4372,"v":4374,"n":23,"y":132,"u":4375},"Flea3 stereo",[4373],"Pointgrey",[],[4376],[225,53,226,185,228],{"c":4378,"m":4379,"d":20,"f":4380,"v":4381,"n":23,"y":1008,"u":4382},"thermal","Flir ADK infrared camera (UGV)",[4344],[],[4383],[1011,53,29,569,147],{"c":522,"m":4385,"d":20,"f":4386,"v":4388,"n":23,"y":49,"u":4389},"FLIR BFS-U3-04S2M-CS",[4387],"FLIR (as named)",[],[4390],[2785,53,29,569,898],{"c":522,"m":4392,"d":20,"f":4393,"v":4394,"n":23,"y":122,"u":4395},"FLIR BFS-U3-16S2C-CS",[4344],[],[4396],[1462,53,29,4397,1104],"RGB mono fisheye, 30 Hz, 1440 px x 1080 px, diagonal FoV 150 deg (SUB)",{"c":1689,"m":4399,"d":20,"f":4400,"v":4401,"n":23,"y":132,"u":4402},"FLIR BFS-U3-31S4C (x2, printed 'FILR' in Table 2)",[4344],[],[4403],[678,23,679,4404,4405],"global-shutter colour, 1024 x 768, 20 Hz, 66.5 deg vertical x 82.9 deg horizontal FOV; fixed exposure and white balance; timestamps shifted by half exposure; baseline 83 cm on vehicle","Table 2; Sec. 3.1.3; Sec. 3.2.3",{"c":522,"m":4407,"d":20,"f":4408,"v":4409,"n":23,"y":318,"u":4410},"FLIR Blackfly (with shutter-synchronized LEDs)",[4344],[],[4411],[4412,53,29,4413,332],"compslam2020","images at 20 Hz",{"c":522,"m":4415,"d":20,"f":4416,"v":4417,"n":28,"y":49,"u":4418},"FLIR Blackfly BFS-u3-13y3c global shutter camera",[4387],[],[4419,4422,4425],[1747,53,29,4420,4421],"FoV 82.9 x 66.5 deg; 1280x1024 or 320x256 input in Table III configurations","VoR Sec. VI-A1; Table III",[599,53,1751,4423,4424],"FoV 82.9 x 66.5 deg; 15 Hz; offline photometric calibration (response function, vignetting)","VoR Sec. VI-B1; Fig. 9; Sec. VI-G",[3153,53,29,4426,4427],"FoV 82.9 x 66.5 deg (model named only in the version of record)","VoR Sec. VI-A",{"c":522,"m":4429,"d":20,"f":4430,"v":4431,"n":23,"y":132,"u":4432},"FLIR Blackfly S",[4344],[],[4433],[1588,23,1589,4434,4435],"not used by the method","Fig. 14",{"c":522,"m":4437,"d":20,"f":4438,"v":4440,"n":23,"y":1008,"u":4441},"Flir BlackflyS (inspection camera) with Flir Tamron 8 mm lens",[4439],"FLIR (camera); Tamron (lens, written 'Flir Tamron')",[],[4442],[1011,53,29,4443,4444],"camera 36 g, 12.3 MP RGB, 23 fps; lens 44 g, 8 mm, 1\u002F1.8 inch image circle, C mount, FOV 50.8 x 38.6 deg","Table 1; Sec. 4.1",{"c":522,"m":4446,"d":20,"f":4447,"v":4449,"n":23,"y":1008,"u":4450},"Flir BlackflyS (SLAM camera) with Fujinon 2.7 mm lens",[4448],"FLIR (camera); Fujinon (lens)",[],[4451],[1011,53,29,4452,4453],"camera 36 g, 3.2 MP RGB, 55 fps; lens 160 g, 2.7 mm, 2\u002F3 inch image circle, C mount, FOV 185 x 140 deg; externally triggered by PWM","Table 1; Sec. 4.3",{"c":4378,"m":4455,"d":20,"f":4456,"v":4457,"n":41,"y":318,"u":4458},"FLIR Boson",[4344],[],[4459,4461],[321,23,322,4460,324],"replaced Tau2 for Tunnel Circuit runs; direct USB interface board",[1371,23,1372,4462,3908],"60 Hz, 512 x 640 pixels; calibrated with a sun-heated 7 x 9 chessboard",{"c":4378,"m":4464,"d":20,"f":4465,"v":4466,"n":41,"y":318,"u":4467},"FLIR Tau2",[4344],[],[4468,4470],[4412,53,29,4469,469],"full radiometric thermal imagery for ROTIO",[321,23,322,4471,324],"thermal infrared camera used at STIX; frame grabber unreliable",{"c":4378,"m":4473,"d":20,"f":4474,"v":4476,"n":23,"y":152,"u":4477},"Flir Vue Pro",[4475],"Flir",[],[4478],[3036,53,29,4479,4480],"7.5 to 13.5 um, 640 x 512 px, up to 30 Hz; software-triggered via Arduino","Sec. Sensors",{"c":372,"m":4482,"d":46,"f":4483,"v":4484,"n":23,"y":233,"u":4485},"flying robot (model not reported)",[],[],[4486],[3947,23,4487,4488,469],"outdoor flying robot dataset of Surber et al. (paper ref. [27])","outdoor flights",{"c":1689,"m":4490,"d":46,"f":4491,"v":4492,"n":23,"y":1819,"u":4493},"forward-facing stereo camera synced to an IMU (model not named)",[],[],[4494],[1764,53,29,4495,4496],"stereo matching input to mapping and to the visual-inertial estimator","Sec. VII",{"c":522,"m":4498,"d":46,"f":4499,"v":4500,"n":23,"y":152,"u":4501},"four additional synchronized cameras on the rig",[],[],[4502],[1795,41,1796,4503,4504],"used only to localise training sequences whose ground truth comes from Structure-from-Motion","Sec. 5; Supp. Sec. 3.1",{"c":1689,"m":4506,"d":46,"f":4507,"v":4508,"n":23,"y":233,"u":4509},"four stereo camera systems (grayscale and color)",[],[],[4510],[4511,23,578,4512,639],"ndtloam2022","KITTI platform cameras; not used by the method",{"c":18,"m":4514,"d":20,"f":4515,"v":4516,"n":23,"y":270,"u":4517},"four-core Intel Core i7",[98],[],[4518],[1730,28,29,4519,4520],"single laptop, 4 GB RAM","Sec. V.D",{"c":372,"m":4522,"d":46,"f":4523,"v":4524,"n":23,"y":233,"u":4525},"four-legged robot (model not stated)",[],[],[4526],[2493,53,29,4527,4528],"dataset 2.5 min, 72 x 42 m, industrial area","VoR Sec. VII-A; Fig. 11a",{"c":372,"m":4530,"d":46,"f":4531,"v":4532,"n":23,"y":132,"u":4533},"Four-wheeled Ackermann UGV (logistics)",[],[],[4534],[678,23,679,4535,4536],"dual GNSS antennas at rear; about 5 m\u002Fs","Sec. 3.2.2; Sec. 5.1.3",{"c":33,"m":4538,"d":46,"f":4539,"v":4540,"n":23,"y":299,"u":4541},"FPGA (model not reported)",[],[],[4542],[1433,53,29,4543,1072],"all sensor streams routed through it; timestamps taken when image sensors are triggered and IMU data requests start",{"c":33,"m":4545,"d":46,"f":4546,"v":4547,"n":23,"y":289,"u":4548},"FPGA board of the custom visual-inertial sensor (Nikolic et al. 2014)",[],[],[4549],[1074,53,29,4550,1065],"hardware synchronisation of imagery and IMU including camera pre-triggering; optional keypoint detection; Gigabit Ethernet to the host",{"c":33,"m":4552,"d":46,"f":4553,"v":4554,"n":23,"y":233,"u":4555},"FPGA synchronization unit (model not reported)",[],[],[4556],[1562,23,1563,4557,4558],"generates 200, 20 and 10 Hz triggers for IMU, cameras and LiDAR from GPS PPS; internal clock in GPS-denied scenes","Sec. III-B1",{"c":33,"m":4560,"d":46,"f":4561,"v":4562,"n":23,"y":1421,"u":4563},"FPGA-based visual-inertial sensor (Nikolic et al.)",[],[],[4564],[1424,53,29,4565,332],"routes all sensor data through an FPGA so hardware timestamps are assigned concurrently, including the LRF trigger output",{"c":522,"m":4567,"d":46,"f":4568,"v":4569,"n":23,"y":152,"u":4570},"front-view camera (model not stated)",[],[],[4571],[4572,23,4573,4574,4575],"sumapp2019","KITTI raw","image shown only to illustrate the scene; not used by the method","Fig. 6(c)",{"c":372,"m":4577,"d":46,"f":4578,"v":4579,"n":23,"y":132,"u":4580},"Frontier handheld perception unit, mounted in a backpack",[],[],[4581],[2662,23,791,4582,4583],"host computer synchronised by PTP; walked through six historic sites","Sec. 3.1, Sec. 5.2, Fig. 3",{"c":33,"m":4585,"d":20,"f":4586,"v":4587,"n":23,"y":1008,"u":4588},"FT232 chip",[],[],[4589],[2018,53,2019,4590,2021],"converts LiDAR TTL signal to serial data",{"c":662,"m":4592,"d":20,"f":4593,"v":4595,"n":23,"y":122,"u":4596},"G345 (built into Bynav X1-5H)",[4594],"EPSON",[],[4597],[1720,53,1721,4598,332],"gyroscope bias stability 0.00075 deg\u002Fs; angular random walk 0.003 deg\u002Fsqrt(s); accelerometer bias stability 70 ug; velocity random walk 0.0005 m\u002Fsqrt(s^3)",{"c":33,"m":4600,"d":46,"f":4601,"v":4602,"n":23,"y":132,"u":4603},"Gazebo simulated robot with simulated 3D LiDAR, camera and IMU",[],[],[4604],[2447,23,4605,4606,4607],"Feng et al. simulated construction-site dataset (Gazebo)","sensor parameters configured to match the physical sensors; ground-truth pose from libgazebo_ros_paths_plugin at 100 Hz","Sec. 3.2; Sec. 3.2.1",{"c":33,"m":4609,"d":46,"f":4610,"v":4611,"n":23,"y":49,"u":4612},"Gazebo simulator ground-truth pose",[],[],[4613],[52,41,54,4614,332],"6-DoF ground-truth pose of the LiDAR in the global frame",{"c":18,"m":4616,"d":20,"f":4617,"v":4618,"n":23,"y":49,"u":4619},"GeForce MX250 laptop GPU",[702],[],[4620],[1536,28,29,4621,332],"about 13 ms per prediction",{"c":33,"m":4623,"d":20,"f":4624,"v":4626,"n":23,"y":4627,"u":4628},"General Electric Graphicon 700",[4625],"General Electric",[],1987,[4629],[4630,28,29,4631,3364],"lorensen1987marchingcubes","display system, 10,000 triangles per second",{"c":2807,"m":4633,"d":46,"f":4634,"v":4635,"n":23,"y":100,"u":4636},"geo-referenced terrestrial laser scanner (model not named)",[],[],[4637],[507,41,4638,4639,4640],"IPB-Car","global map used for scan-to-map constraints with the OS1-128 in the factor graph that generates reference poses (fused with GNSS-INS, LiDAR odometry and loop closures)","Sec. V-A1",{"c":2807,"m":4642,"d":46,"f":4643,"v":4644,"n":23,"y":132,"u":4645},"geo-referenced terrestrial laser scans (scanner model not named)",[],[],[4646],[790,41,2000,4647,793],"precise constraints in the offline reference-pose bundle adjustment",{"c":944,"m":4649,"d":46,"f":4650,"v":4652,"n":41,"y":122,"u":4654},"Geo-SLAM ZEB-Revo",[4651],"GeoSLAM",[4653],"GeoSLAM ZEB-REVO",[4655,4659],[4656,53,29,4657,4658],"ibrahimkhil2023masonryslam","maximum range 30 m, scan rate 43,200 points\u002Fs, relative accuracy +\u002F-1 to 3 cm (from manufacturer manual); six scans of under 10 min each","Sec. 4.1, Fig. 3a, Sec. 5",[4660,53,29,4661,4662],"keitaanniemi2023drift","range 30 m; rotation 0.5 Hz; scanner profile frequency 100 Hz; 43,200 points\u002Fs; FOV 360 deg vertical x 270 deg horizontal; one 10-min scan at slow walking speed with internal loops; processed in GeoSLAM Hub with default settings","Sec. 3.1.2",{"c":18,"m":4664,"d":20,"f":4665,"v":4666,"n":23,"y":1819,"u":4667},"GeoSLAM dedicated cloud processing servers",[4651],[],[4668],[4669,28,29,4670,4671],"makkonen2017zebshaft","post analysis of data performed on dedicated servers in the cloud; hardware not reported","Sec. 2, Sec. 4",{"c":944,"m":4673,"d":20,"f":4674,"v":4676,"n":23,"y":132,"u":4677},"GeoSLAM HORIZON RT",[4675],"GeoSLAM (footnote 12 links the product page on the FARO website)",[],[4678],[4679,952,29,4680,4681],"yu2025_3dgs_lidar_heritage","mobile scanner with integrated camera; SLAM-based 3D scanning and panoramic image capture; outputs dense point clouds in .las; footnote 12 URL points to the FARO product page 'GeoSLAM-ZEB-Horizon-RT'; about 13 million vertices in the VR test (Table 3)","Sec. 3.3.1, footnote 12, Fig. 6, Table 3",{"c":944,"m":4683,"d":20,"f":4684,"v":4685,"n":23,"y":233,"u":4686},"GeoSLAM Zeb Go",[4651],[],[4687],[4224,952,29,4688,4689],"handheld, 2020, indoor; camera as accessory; Hokuyo UTM-30LX laser scanner, 30 m; IMU and GPS columns marked no; 1-3 cm relative accuracy (manufacturer)","Table 4; Sec. 4.2",{"c":944,"m":4691,"d":20,"f":4692,"v":4693,"n":41,"y":233,"u":4695},"GeoSLAM Zeb Horizon",[4651],[4694],"GeoSLAM ZEB Horizon",[4696,4698],[4224,952,29,4697,4689],"handheld, 2018, indoor and outdoor; camera as accessory; Velodyne Puck VLP-16, 100 m; IMU and GPS columns marked no; 0.6 cm relative accuracy (manufacturer)",[3761,952,29,4699,4700],"300,000 pts\u002Fs, max range 100 m, ranging accuracy 30 mm at 100 m; processed with GeoSLAM proprietary tool","Sec. 2.3; Table 1; Sec. 3",{"c":944,"m":4702,"d":20,"f":4703,"v":4705,"n":23,"y":132,"u":4706},"GeoSLAM ZEB Horizon RT",[4704],"FARO (GeoSLAM)",[],[4707],[3843,952,29,4708,4242],"handheld; Velodyne VLP-16 (16 channels, distance SD 3 cm), 903 nm, FoV 360 x 270 deg, 0.3 Mpts\u002Fs, 100 m; declared relative accuracy 6 mm; processed in GeoSLAM Connect 2.3.0",{"c":944,"m":4710,"d":20,"f":4711,"v":4712,"n":23,"y":233,"u":4713},"GeoSLAM Zeb Revo RT",[4651],[],[4714],[4224,952,29,4715,4689],"handheld, 2015, indoor; camera as accessory; Hokuyo UTM-30LX laser scanner, 30 m; IMU and GPS columns marked no; 0.6 cm relative accuracy (manufacturer)",{"c":372,"m":4717,"d":46,"f":4718,"v":4719,"n":23,"y":233,"u":4720},"gimbal stabilizer",[],[],[4721],[1562,23,1563,4722,4723],"sensor rig mounted on a gimbal stabilizer for the handheld mode (Sec. I-B; Fig. 1b); the gimbal-mounted device performs 6-DoF but stable motion, while the purely handheld device performs arbitrary 6-DoF jerky motion (Sec. IV-A5); Table III lists the platform only as Handheld and does not state which sequences used the gimbal","Fig. 1b; Sec. I-B; Sec. IV-A5; Table III",{"c":522,"m":4725,"d":46,"f":4726,"v":4727,"n":23,"y":318,"u":4728},"global-shutter monocular camera (model not reported)",[],[],[4729],[4730,53,29,4731,4732],"licfusion2_2020","20 Hz, 1920x1200 (Table IV)","Sec. VI; Fig. 4; Table IV",{"c":651,"m":4734,"d":46,"f":4735,"v":4736,"n":41,"y":132,"u":4738},"GNSS (receiver model not reported)",[],[4737],"GNSS receiver (model not_reported)",[4739,4741],[1520,41,29,4740,1072],"position-only ground truth for the forest trajectory",[2537,23,2538,569,917],{"c":651,"m":4743,"d":46,"f":4744,"v":4745,"n":23,"y":100,"u":4746},"GNSS reference station",[],[],[4747],[4092,53,4093,4748,4749],"corrections used to remove systematic errors from pseudoranges","Abstract; Sec. III",{"c":651,"m":4751,"d":46,"f":4752,"v":4753,"n":23,"y":100,"u":4754},"GNSS-IMU system with real-time kinematic signals (model not stated)",[],[],[4755],[4756,41,4757,4758,332],"voxelmappp2024","M2DGR","ground truth",{"c":651,"m":4760,"d":46,"f":4761,"v":4762,"n":23,"y":100,"u":4763},"GNSS-INS (model not named)",[],[],[4764],[507,41,4765,4766,4640],"KITTI odometry; MulRAN","poses regarded as the evaluation reference",{"c":651,"m":4768,"d":46,"f":4769,"v":4770,"n":23,"y":122,"u":4771},"GNSS\u002FINS integrated navigation system with GNSS-RTK and a navigation-grade IMU (models not stated)",[],[],[4772],[1020,41,1021,4773,332],"post-processed ground truth, 0.02 m position and 0.01 deg attitude",{"c":372,"m":4775,"d":20,"f":4776,"v":4777,"n":23,"y":122,"u":4778},"Go1",[],[],[4779],[4780,53,29,4781,4782],"bimslam2023","legged robot (quadruped, Fig. 6 caption) carrying the mapping system during the real-world tests","Sec. 4.2, Fig. 6",{"c":372,"m":4784,"d":46,"f":4785,"v":4786,"n":23,"y":152,"u":4787},"golf cart",[],[],[4788],[4789,53,29,4790,4791],"liomapping2019","lidar at the front and IMU above the base link (sensor models not stated)","Sec. VI-A; Fig. 4b",{"c":33,"m":4793,"d":20,"f":4794,"v":4796,"n":23,"y":122,"u":4797},"GOM Atos Q34",[4795],"GOM",[],[4798],[1252,41,1253,4799,4749],"industrial 3D scanner used to verify placement of LiDAR, metal tip and cameras (micrometer-accurate per abstract); footnote 4 links the ATOS Q product page, so the exact variant is not resolvable",{"c":33,"m":4801,"d":46,"f":4802,"v":4803,"n":23,"y":455,"u":4804},"Google Earth satellite image of the Freiburg campus",[],[],[4805],[4806,41,4807,4808,4809],"kuemmerle2009measuring","Freiburg University Hospital","aerial image processed with Canny edge extraction as prior for global relations","Sec. 5; Fig. 5",{"c":944,"m":4811,"d":46,"f":4812,"v":4814,"n":23,"y":289,"u":4815},"Google Tango 'Peanut' mobile phone",[4813],"Google",[],[4816],[3245,53,29,4817,332],"2 GB RAM, quad-core CPU, six-axis gyroscope and accelerometer, 120 deg wide-angle tracking camera at 60 Hz, projective depth sensor at 6 Hz, 4 megapixel colour sensor at 30 Hz",{"c":944,"m":4819,"d":46,"f":4820,"v":4821,"n":23,"y":289,"u":4822},"Google Tango 'Yellowstone' tablet",[4813],[],[4823],[3245,53,29,4824,332],"4 GB RAM, quad-core CPU, Nvidia Tegra K1 graphics, 120 deg FOV tracking camera at 60 Hz, projective depth sensor at 3 Hz, 4 megapixel colour sensor at 30 Hz",{"c":33,"m":4826,"d":20,"f":4827,"v":4828,"n":23,"y":1819,"u":4829},"Google Tango Peanut sensor (mapper version 3.15)",[4813],[],[4830],[1067,952,29,4831,4832],"engineered VIO device; VI-Sensor rigidly attached to it for the outdoor runs","Sec. VIII-B3",{"c":522,"m":4834,"d":46,"f":4835,"v":4836,"n":23,"y":233,"u":4837},"Gopro",[],[],[4838],[2493,53,29,4839,2494],"independent camera without a common clock with the LiDAR",{"c":651,"m":4841,"d":46,"f":4842,"v":4843,"n":41,"y":455,"u":4844},"GPS",[],[],[4845,4848],[1957,53,29,4846,4847],"converted to UTM easting, northing and altitude; unary position edges for outdoor mapping","Sec. GPS constraint",[4849,41,4850,569,4851],"segal2009gicp","instrumented-car logs, suburban loop","Sec. IV (p. 4)",{"c":651,"m":4853,"d":46,"f":4854,"v":4855,"n":23,"y":289,"u":4856},"GPS (KITTI ground truth, model not stated)",[],[],[4857],[2595,41,578,4858,2597],"ground truth from GPS and a Velodyne laser scanner",{"c":651,"m":4860,"d":46,"f":4861,"v":4862,"n":23,"y":318,"u":4863},"GPS (KITTI; model not named)",[],[],[4864],[4865,41,578,4866,4867],"segmap2020","used to find ground-truth segment correspondences in revisited areas","Sec. 5.2.2",{"c":651,"m":4869,"d":46,"f":4870,"v":4871,"n":23,"y":318,"u":4872},"GPS (KITTI; model not reported)",[],[],[4873],[1183,41,842,4874,745],"used to count the total number of loop closures",{"c":651,"m":4876,"d":46,"f":4877,"v":4878,"n":41,"y":455,"u":4879},"GPS (model not reported)",[],[],[4880,4883],[4806,952,29,4881,4882],"GPS positions (blue) plotted against the trajectory estimated from satellite images on the ALU-FR campus image (Fig. 7; Sec. 5.2); the paper does not state which benchmark log this example comes from","Sec. 5.2; Fig. 7",[4101,41,4884,4885,469],"fast flight dataset (KumarRobotics msckf_vio wiki)","x-y position reference for fast-flight RMSE",{"c":651,"m":4887,"d":46,"f":4888,"v":4889,"n":23,"y":100,"u":4890},"GPS (model not stated in paper)",[],[],[4891],[646,23,647,4892,4893],"attached to the handheld system; ground-truth positions for all sequences obtained via FAST-LIO2 with GPS","Sec. II-A, Sec. III",{"c":651,"m":4895,"d":46,"f":4896,"v":4897,"n":41,"y":233,"u":4898},"GPS (model not stated)",[],[],[4899,4901],[668,41,4281,4900,4283],"combined with fiber optic gyro and SLAM to give 6-DoF ground truth at 100 Hz",[1191,41,1192,4902,4903],"unstable under high-rise buildings; approximate reference only","Sec. IV-B-4",{"c":651,"m":4905,"d":46,"f":4906,"v":4907,"n":23,"y":100,"u":4908},"GPS (not used as input)",[],[],[4909],[599,23,600,569,4427],{"c":651,"m":4911,"d":46,"f":4912,"v":4913,"n":23,"y":49,"u":4914},"GPS (receiver model not stated)",[],[],[4915],[623,41,4916,4917,332],"self-collected Urban, Campus, Suburban and UrbanLoco","used only as ground truth",{"c":651,"m":4919,"d":46,"f":4920,"v":4921,"n":23,"y":122,"u":4922},"GPS (YQ ground truth)",[],[],[4923],[164,41,4924,4925,167],"CLIC YQ dataset (authors)","GPS measurements provide ground truth for the outdoor YQ dataset",{"c":651,"m":4927,"d":20,"f":4928,"v":4930,"n":23,"y":132,"u":4931},"GPS 18x",[4929],"Garmin",[],[4932],[225,53,854,4933,228],"1 Hz",{"c":651,"m":4935,"d":46,"f":4936,"v":4937,"n":23,"y":233,"u":4938},"GPS antenna (model not reported; footnote links to fixposition.com)",[],[],[4939],[3947,23,4940,4941,4942],"outdoor ground robot dataset (Fixposition team)","5 Hz","Sec. IV-C, footnote 1",{"c":651,"m":4944,"d":46,"f":4945,"v":4946,"n":23,"y":71,"u":4947},"GPS base station (model not reported)",[],[],[4948],[1573,53,29,4949,4950],"less than 1 km from the test area; 2 cm GPS error added to model predictions","Comparison section",{"c":651,"m":4952,"d":46,"f":4953,"v":4954,"n":23,"y":318,"u":4955},"GPS measurement",[],[],[4956],[2501,41,29,4957,4958],"start and end coordinates printed in Fig. 9; Sec. V-B compares odometry distance with the GPS measurement and the Fig. 9 caption says results were compared with Google maps to compute traveled distance; receiver not stated","Sec. V-B, Fig. 9",{"c":651,"m":4960,"d":46,"f":4961,"v":4962,"n":23,"y":318,"u":4963},"GPS receiver",[],[],[4964],[2391,41,29,4965,469],"provides ground-truth positions in outdoor tests",{"c":651,"m":4967,"d":46,"f":4968,"v":4969,"n":23,"y":233,"u":4970},"GPS receiver (labelled in Fig. 4)",[],[],[4971],[3255,23,3509,4972,4973],"not_reported (no GNSS use described)","Fig. 4",{"c":651,"m":4975,"d":46,"f":4976,"v":4977,"n":23,"y":233,"u":4978},"GPS receiver (model not reported)",[],[],[4979],[3947,23,4487,4941,469],{"c":651,"m":4981,"d":46,"f":4982,"v":4983,"n":23,"y":299,"u":4984},"GPS-INS (model not reported)",[],[],[4985],[2604,41,29,4986,1788],"position ground truth",{"c":651,"m":4988,"d":46,"f":4989,"v":4990,"n":23,"y":270,"u":4991},"GPS-INS system (model not stated)",[],[],[4992],[339,41,29,4993,4994],"ground truth for the outdoor driving experiment","Sec. 7.1.3",{"c":651,"m":4996,"d":46,"f":4997,"v":4998,"n":23,"y":233,"u":4999},"GPS\u002FIMU (model not reported)",[],[],[5000],[2979,41,842,5001,2300],"ground-truth poses of KITTI",{"c":651,"m":5003,"d":46,"f":5004,"v":5005,"n":23,"y":233,"u":5006},"GPS\u002FIMU (post-processed; model not reported)",[],[],[5007],[2979,41,5008,5009,2300],"ParisLuco","ground truth translations only",{"c":651,"m":5011,"d":46,"f":5012,"v":5013,"n":23,"y":346,"u":5014},"GPS\u002FINS (model not stated)",[],[],[5015],[2573,41,5016,5017,398],"Oxford RobotCar; in-house U.S., R.A., B.D.","reference maps built in UTM coordinates",{"c":651,"m":5019,"d":46,"f":5020,"v":5021,"n":23,"y":346,"u":5022},"GPS+IMU navigation system (model not reported)",[],[],[5023],[2612,41,578,5024,1204],"ground truth for 11 training sequences; author notes errors above 5 m at the start of sequence 8",{"c":522,"m":5026,"d":20,"f":5027,"v":5028,"n":23,"y":346,"u":5029},"Grasshopper3 2.8 MP color camera",[],[],[5030],[3868,53,29,5031,5032],"2.8 MP; fisheye lens (Sec. V-A); used only for colourising the dense surfel map","Sec. V-A; Sec. VII",{"c":372,"m":5034,"d":46,"f":5035,"v":5036,"n":23,"y":152,"u":5037},"GRoMI (ground robot for mapping infrastructure)",[],[],[5038],[431,53,29,5039,5040],"robotic hybrid LiDAR system on a mobile platform; accessible slope threshold 20 deg; robot height 1.8 m used for navigation map","Sec. 4.2; Sec. 5; Fig. 8",{"c":372,"m":5042,"d":46,"f":5043,"v":5044,"n":23,"y":346,"u":5045},"GRoMI (Ground Robot Mapping Infrastructure)",[],[],[5046],[2316,53,29,5047,5048],"four-wheeled autonomous mobile robot with an upper hybrid laser scanning system; remote or autonomous navigation","Sec. 4.1; Fig. 1",{"c":372,"m":5050,"d":46,"f":5051,"v":5052,"n":23,"y":122,"u":5053},"ground robot (drive type not stated)",[],[],[5054],[1720,53,1721,5055,5056],"carries radar, GNSS\u002FINS, LiDAR and cameras","Fig. 3",{"c":372,"m":5058,"d":46,"f":5059,"v":5060,"n":23,"y":233,"u":5061},"ground robot (model and locomotion not reported)",[],[],[5062],[3947,23,4940,5063,745],"outdoor trajectory",{"c":372,"m":5065,"d":46,"f":5066,"v":5067,"n":23,"y":100,"u":5068},"ground robot platform (not further specified)",[],[],[5069],[5070,23,4757,5071,1204],"loglio2024","indoor and outdoor campus scenes, night street sequences",{"c":33,"m":5073,"d":20,"f":5074,"v":5075,"n":23,"y":71,"u":5076},"ground targets (8 reflective horizontal and 8 vertical-only)",[],[],[5077],[1573,41,29,5078,5079],"established at an airport calibration site; horizontal positions digitized from 1 m intensity rasters","Comparison section: fixed wing system",{"c":372,"m":5081,"d":46,"f":5082,"v":5083,"n":23,"y":270,"u":5084},"ground vehicle",[],[],[5085],[2630,53,29,5086,2153],"lidar mounted at the front; outdoor tests at 0.5 m\u002Fs",{"c":372,"m":5088,"d":46,"f":5089,"v":5090,"n":23,"y":1819,"u":5091},"Ground vehicle (outdoor)",[],[],[5092],[5093,53,29,5094,5095],"loam2017_auro","lidar mounted at the front; 0.5 m\u002Fs","Sec. 7.1, Fig. 10",{"c":33,"m":5097,"d":46,"f":5098,"v":5099,"n":23,"y":100,"u":5100},"ground-truth structure point clouds",[],[],[5101],[2054,41,5102,5103,5104],"FusionPortable; self-collected","provided for FusionPortable and both self-collected devices (acquisition instrument not stated)","Table I; Sec. IV",{"c":33,"m":5106,"d":46,"f":5107,"v":5108,"n":23,"y":318,"u":5109},"ground-truth target maps built with the laser SLAM tool released with SegMap",[],[],[5110],[636,41,637,5111,478],"used as target maps and trajectory reference",{"c":18,"m":5113,"d":20,"f":5114,"v":5115,"n":23,"y":233,"u":5116},"GTX 1080 Ti",[],[],[5117],[5118,28,29,5119,5120],"scancontextpp2022","GPU used for PointNetVLAD timing","Sec. VII-G; Table V",{"c":18,"m":5122,"d":20,"f":5123,"v":5124,"n":23,"y":100,"u":5126},"GTX RTX3090",[],[5125],"GTX RTX3090 (as written)",[5127],[5128,28,29,5129,2162],"huang2024_2dgs","single GPU for all experiments",{"c":18,"m":5131,"d":46,"f":5132,"v":5133,"n":23,"y":1819,"u":5134},"GTX Titan Black",[702],[],[5135],[1822,28,29,5136,5137],"correspondence search and global pose optimisation","Sec. 6; Fig. 4",{"c":662,"m":5139,"d":46,"f":5140,"v":5141,"n":23,"y":132,"u":5142},"GVINS-Dataset IMU (model not reported in this paper)",[],[],[5143],[3944,23,1059,5144,3280],"IMU recorded with the stereo camera and the ZED-F9P GNSS sensor",{"c":1689,"m":5146,"d":46,"f":5147,"v":5148,"n":23,"y":132,"u":5149},"GVINS-Dataset stereo camera (model not reported in this paper)",[],[],[5150],[3944,23,1059,5151,3280],"stereo camera recorded together with an IMU and a ZED-F9P GNSS sensor",{"c":18,"m":5153,"d":20,"f":5154,"v":5155,"n":23,"y":100,"u":5156},"H100 GPU",[],[],[5157],[5158,28,29,5159,5160],"dust3r2024","pairwise inference about 40 ms","Sec. 3.4",{"c":372,"m":5162,"d":46,"f":5163,"v":5164,"n":23,"y":122,"u":5165},"hand-carried HILTI-Oxford sensor rig (not described in the paper)",[],[],[5166],[5167,23,5168,5169,5170],"adalio2023","HILTI-Oxford dataset","a surveyor descends stairs while carrying the device","Sec. IV-B; Sec. IV-C",{"c":522,"m":5172,"d":46,"f":5173,"v":5174,"n":23,"y":100,"u":5175},"hand-held cameras (models not named)",[],[],[5176],[5177,23,5178,5179,3230],"mast3r2024","Aachen Day-Night","4,328 Aachen reference images",{"c":372,"m":5181,"d":46,"f":5182,"v":5183,"n":23,"y":318,"u":5184},"Hand-held device carrying LiDAR, camera and laptop",[],[],[5185],[2501,53,29,5186,5187],"used for data collection; motion described as jerky","Fig. 8d; Sec. V-A",{"c":522,"m":5189,"d":46,"f":5190,"v":5191,"n":23,"y":270,"u":5192},"hand-held monocular camera (model not stated)",[],[],[5193],[5194,53,29,5195,5196],"lsdslam2014","pipeline figure assumes 640x480 at 30 Hz; used for the qualitative outdoor trajectories of about 500 m","Sec. 4, 4.1; Fig. 3",{"c":944,"m":5198,"d":46,"f":5199,"v":5200,"n":23,"y":233,"u":5201},"hand-held multi-beam LiDAR device",[],[],[5202],[2493,53,29,5203,5204],"Velodyne VLP-16, Microstrain 3DM-GX3, GoPro","VoR Fig. 9b; Sec. VII-A",{"c":372,"m":5206,"d":46,"f":5207,"v":5208,"n":23,"y":289,"u":5209},"hand-held RGB-D camera",[],[],[5210],[2830,53,2831,5211,5212],"seven datasets from 30 to 318 m (coffee room, corridor, garden, outdoors, two floors, indoor and outdoor, apartment)","Sec. 5.2; Table 5",{"c":1776,"m":5214,"d":46,"f":5215,"v":5216,"n":23,"y":346,"u":5217},"hand-held RGB-D camera (model not reported)",[],[],[5218],[5219,53,29,5220,5221],"staticfusion2018","two recorded sequences: person interacting with objects, and a selfie sequence with the camera pointing at its carrier","Sec. VII-B",{"c":372,"m":5223,"d":46,"f":5224,"v":5225,"n":23,"y":100,"u":5226},"hand-held rig (not further described)",[],[],[5227],[5228,53,5229,5230,469],"coinlio2024","ENWIDE","walking (smooth) and running (dynamic) sequences",{"c":944,"m":5232,"d":46,"f":5233,"v":5234,"n":23,"y":49,"u":5235},"hand-held scanner (perception module, rotation platform, computing module)",[],[],[5236],[5237,53,29,5238,5239],"sslslam2021","less than 500 g; walked at normal speed indoors","Sec. IV-D; Fig. 5",{"c":372,"m":5241,"d":46,"f":5242,"v":5243,"n":23,"y":289,"u":5244},"hand-held sensor",[],[],[5245],[1074,53,29,5246,5247],"Vicon Loops 1200 m; ETH Main Building outdoor loop 620 m; qualitative 470 m indoor walk over three floors","Sec. VII-B1, VII-B3; Fig. 1",{"c":372,"m":5249,"d":46,"f":5250,"v":5251,"n":23,"y":318,"u":5252},"hand-held sensor suite",[],[],[5253],[5254,53,29,5255,5256],"legentil2020gpm","moved up and down while walking in the UTS lab; 6.2 m trajectory, maximum estimated velocity 1.7 m\u002Fs","Sec. V-B; Fig. 4",{"c":372,"m":5258,"d":46,"f":5259,"v":5260,"n":23,"y":142,"u":5261},"hand-held sensor suite (stereo camera on top, IMU below)",[],[],[5262],[5263,53,29,5264,5265],"lupton2012preint","hand held and carried around the buildings","Sec. VIII-A; Fig. 5",{"c":944,"m":5267,"d":20,"f":5268,"v":5269,"n":23,"y":233,"u":5270},"hand-held single-beam 3D spinning LiDAR device",[],[],[5271],[2493,53,29,5272,5273],"Hokuyo UTM-30LX, encoder, Microstrain 3DM-GX3, RGB camera","VoR Fig. 9a; Sec. VII-A",{"c":372,"m":5275,"d":20,"f":5276,"v":5277,"n":23,"y":122,"u":5278},"hand-held system (Hilti SLAM Dataset 2021)",[],[],[5279],[5280,23,5281,5282,5283],"malio2023","Hilti SLAM Dataset 2021","small-scale indoor and outdoor environments","Sec. III-A-1",{"c":33,"m":5285,"d":46,"f":5286,"v":5287,"n":23,"y":100,"u":5288},"hand-held thermometer",[],[],[5289],[2101,41,29,5290,5291],"scanner temperature measured before and after each stationary collection","Sec. 3.2; Sec. 4.1",{"c":372,"m":2223,"d":46,"f":5293,"v":5294,"n":41,"y":270,"u":5295},[],[],[5296,5299],[2135,23,2136,5297,5298],"flat ground, stairs, some metro-tunnel sequences","Table 3, Fig. 2b",[2630,53,29,5300,5221],"person walks at 0.5 m\u002Fs moving the lidar up and down about 0.5 m; staircase test",{"c":372,"m":5302,"d":46,"f":5303,"v":5304,"n":23,"y":1819,"u":5305},"Handheld (person holding the lidar)",[],[],[5306],[5093,53,29,5307,5308],"walking at 0.5 m\u002Fs while moving the lidar up and down about 0.5 m; staircase test","Sec. 7.2, Fig. 13",{"c":522,"m":5310,"d":46,"f":5311,"v":5312,"n":23,"y":49,"u":5313},"handheld camera",[],[],[5314],[92,23,991,5315,5316],"TUM-RGBD indoor scenes with rolling shutter artifacts, motion blur and heavy rotation","Sec. 4 (TUM-RGBD)",{"c":522,"m":5318,"d":46,"f":5319,"v":5320,"n":23,"y":318,"u":5321},"handheld cellphone (model not named)",[],[],[5322],[706,23,5323,5324,5325],"Real Forward-Facing (5 LLFF scenes + 3 new)","forward-facing captures, 20 to 62 images per scene, 1008 x 756 pixels","Sec. 6.1; Table 1 caption",{"c":372,"m":5327,"d":46,"f":5328,"v":5329,"n":23,"y":289,"u":5330},"handheld custom camera-lidar sensor",[2901],[],[5331],[5332,53,29,5333,2153],"vloam2015","carried by a person walking at about 0.7 m\u002Fs in the accuracy tests",{"c":372,"m":5335,"d":46,"f":5336,"v":5337,"n":23,"y":1421,"u":5338},"handheld custom-built camera and lidar sensor pack",[],[],[5339],[3860,53,29,5340,5341],"carried by a person walking at 0.5 m\u002Fs","Sec. VI; Fig. 5",{"c":372,"m":5343,"d":46,"f":5344,"v":5345,"n":23,"y":132,"u":5346},"handheld data collection device",[],[],[5347],[5348,23,5349,5350,5351],"ghadimzadeh2025slamnde","authors' Drexel University basement handheld dataset","carries the depth camera, LiDAR and IMU of Table 4; about 50 m loop in a poorly lit, textureless basement","Sec. 7.2.1; Fig. 24",{"c":372,"m":5353,"d":20,"f":5354,"v":5355,"n":23,"y":122,"u":5356},"handheld data-collection device (custom rig, Fig. 6a)",[],[],[5357],[3444,53,29,5358,5359],"mini-computer, Livox Avia LiDAR and a preview RGB camera","Sec. VIII-A1, Fig. 6",{"c":372,"m":5361,"d":46,"f":5362,"v":5363,"n":23,"y":233,"u":5364},"handheld data-collection device with FDM-printable mechanical parts",[],[],[5365],[1747,53,29,5366,5367],"total weight 2.09 kg (arXiv v1 Fig. 6 caption)","VoR Fig. 5; arXiv v1 Fig. 6",{"c":372,"m":5369,"d":46,"f":5370,"v":5371,"n":28,"y":100,"u":5372},"handheld device",[],[],[5373,5375,5378],[567,23,824,5374,1538],"Oxford university campus",[2423,23,5376,5377,917],"R3LIVE dataset; FAST-LIVO dataset","Livox Avia, built-in IMU and RGB camera",[211,23,2415,5379,214],"handheld device-collected dataset; no position ground truth",{"c":372,"m":5381,"d":20,"f":5382,"v":5383,"n":23,"y":233,"u":5384},"handheld device (L515)",[],[],[5385],[5386,53,29,5387,469],"yuan2022voxelmap","three sequences in laboratory and warehouse; hand-carried with much faster motion (especially rotation) than the slow UGV data of SSL_SLAM; routes start and end at the same place",{"c":372,"m":5389,"d":20,"f":5390,"v":5391,"n":23,"y":233,"u":5392},"handheld device (Livox Avia)",[],[],[5393],[5386,53,29,5394,5395],"park with trees; two 485 m loops and one 815 m loop starting and ending at the same place","Sec. IV-C1",{"c":372,"m":5397,"d":46,"f":5398,"v":5399,"n":23,"y":122,"u":5400},"handheld device integrated with multi-sensor fusion SLAM",[],[],[5401],[2160,53,29,5402,5403],"random trajectory keeping working distance below 4.11 m","Sec. 6, Fig. 12",{"c":372,"m":5405,"d":20,"f":5406,"v":5407,"n":23,"y":100,"u":5408},"handheld device with FDM 3D-printed mechanical components",[],[],[5409],[599,53,1751,5410,1753],"schematics open-sourced",{"c":944,"m":5412,"d":20,"f":5413,"v":5414,"n":23,"y":122,"u":5415},"handheld device with five cameras and an Ouster OS0-128",[],[],[5416],[1029,53,29,5417,469],"23 runs, more than two hours, about 10 km, indoor-outdoor transitions; OKVIS odometry",{"c":372,"m":5419,"d":46,"f":5420,"v":5421,"n":23,"y":49,"u":5422},"handheld device with power supply",[],[],[5423],[3153,53,29,5424,5425],"total weight 2.09 kg (minimum system)","Fig. 6 caption",{"c":372,"m":5427,"d":20,"f":5428,"v":5429,"n":23,"y":142,"u":5430},"handheld Kinect with reflective markers",[],[],[5431],[990,23,991,5432,5433],"11 handheld SLAM sequences with 6-DOF motion","Sec. III, Fig. 1c",{"c":108,"m":5435,"d":46,"f":5436,"v":5437,"n":23,"y":122,"u":5438},"handheld LiDAR (model not stated in the paper)",[],[],[5439],[807,23,815,5440,332],"cm-level measurement noise and substantial motion distortion",{"c":372,"m":5442,"d":46,"f":5443,"v":5444,"n":23,"y":1008,"u":5445},"handheld LIV frame (optimized from the design of Wang et al. 2025)",[],[],[5446],[2018,53,2019,5447,5448],"LiDAR at frame center, camera on top, handle below","Sec. 2.1.1; Fig. 2",{"c":522,"m":5450,"d":46,"f":5451,"v":5452,"n":23,"y":152,"u":5453},"handheld monocular camera (model not reported)",[],[],[5454],[5455,53,29,5456,5457],"densesurfelmapping2019","depth from MVDepthNet, poses from VINS-Mono; map used for quadrotor flights","Sec. VI-D",{"c":372,"m":5459,"d":46,"f":5460,"v":5462,"n":23,"y":132,"u":5463},"Handheld multi-sensor suite (aluminium alloy frame)",[5461],"custom (authors)",[],[5464],[678,23,679,5465,5466],"plug-and-play suite mounted on all platforms; two PCs synchronized by NTP","Sec. 3; Fig. 1",{"c":372,"m":5468,"d":46,"f":5469,"v":5470,"n":41,"y":233,"u":5471},"handheld platform",[],[],[5472,5475],[378,53,29,5473,5474],"Livox Avia with DJI Manifold 2-C","Fig. 6(b); Sec. VII-A",[216,53,3513,5476,5477],"handheld data-collection device (Fig. 9a)","Fig. 9",{"c":372,"m":5479,"d":46,"f":5480,"v":5481,"n":23,"y":122,"u":5482},"handheld platform (not named)",[],[],[5483],[1349,23,5484,5485,745],"Fusion Portable","Canteen Day and Garden Day, medium-scale semi-outdoor courtyards",{"c":372,"m":5487,"d":46,"f":5488,"v":5489,"n":23,"y":1008,"u":5490},"handheld rig with backpack",[],[],[5491],[1011,53,29,5492,147],"base sensors about 958 g plus a rear-facing camera; computer and battery in a backpack",{"c":372,"m":5494,"d":46,"f":5495,"v":5496,"n":23,"y":132,"u":5497},"handheld sensor mast (Newer College Dataset)",[],[],[5498],[1588,23,824,5499,5500],"aggressive high-frequency motions, dynamic swinging of the mast","Sec. V; Sec. V-B",{"c":372,"m":5502,"d":46,"f":5503,"v":5504,"n":23,"y":100,"u":5505},"Handheld sensor pack",[],[],[5506],[1371,23,1372,5507,5508],"max 2 m\u002Fs (Laurel Cavern, Low Light 1 and 2)","Tables 7-8",{"c":372,"m":5510,"d":46,"f":5511,"v":5512,"n":23,"y":152,"u":5513},"handheld sensor pair",[],[],[5514],[4789,53,29,5515,5516],"lidar and IMU close together; attached camera only records the scene","Sec. VI-A; Fig. 4a",{"c":372,"m":5518,"d":20,"f":5519,"v":5520,"n":23,"y":132,"u":5521},"handheld sensor platform (Hilti 2021)",[],[],[5522],[5523,23,5524,5525,469],"zhu2025meshloam","Hilti SLAM Challenge 2021","records the data and provides millimetre-accurate ground truth; most sequences have 3-DoF ground truth",{"c":372,"m":5527,"d":46,"f":5528,"v":5529,"n":23,"y":152,"u":5530},"handheld sensor rig (held at chest height)",[],[],[5531],[2080,53,5532,5533,2788],"self-collected indoor sequences","indoor carrying mode",{"c":372,"m":5535,"d":46,"f":5536,"v":5537,"n":23,"y":49,"u":5538},"handheld sensor suite (same LiDAR platform)",[],[],[5539],[3133,53,29,569,5540],"Sec. IV-C; Sec. IV-D; Fig. 6",{"c":372,"m":5542,"d":46,"f":5543,"v":5544,"n":23,"y":49,"u":5545},"handheld sensor suite carried by an operator",[],[],[5546],[2785,53,5547,569,5548],"Urban; Handheld","Sec. III-A; Sec. III-C",{"c":372,"m":5550,"d":46,"f":5551,"v":5552,"n":23,"y":100,"u":5553},"handheld system",[],[],[5554],[646,23,647,5555,5556],"handheld rig carrying monocular camera, solid-state LiDAR, IMU and GPS; 12 sequences on different dates along nearly identical paths","Sec. II-A, Sec. III, Fig. 2",{"c":372,"m":5558,"d":46,"f":5559,"v":5560,"n":23,"y":100,"u":5561},"handheld system (model not reported)",[],[],[5562],[5563,23,1280,5564,5565],"vegatorres2024slam2ref","four construction-site sequences S2-S5 of 225-340 m","Sec. 5.1, Table 1",{"c":372,"m":5567,"d":46,"f":5568,"v":5569,"n":23,"y":1008,"u":5570},"HandNav handheld device",[],[],[5571],[1101,53,2142,5572,1843],"3 sequences, 1.5 km (Table I)",{"c":522,"m":5574,"d":46,"f":5575,"v":5576,"n":23,"y":346,"u":5577},"HD color camera (model not_reported)",[],[],[5578],[2905,53,29,5579,2907],"1600 x 1200 px, used for point cloud colorization (Contour)",{"c":372,"m":5581,"d":46,"f":5582,"v":5583,"n":23,"y":132,"u":5584},"HEAP autonomous walking excavator",[],[],[5585],[1520,53,29,569,5586],"Sec. V-D; Fig. 5-E",{"c":372,"m":5588,"d":46,"f":5589,"v":5590,"n":23,"y":1008,"u":5591},"HEAP hydraulic walking excavator",[],[],[5592],[1476,53,5593,5594,3280],"HEAP construction missions (authors)","IMU, two GNSS antennas, cabin-to-chassis rotary encoder",{"c":372,"m":5596,"d":46,"f":5597,"v":5598,"n":23,"y":37,"u":5599},"Hector UGV",[],[],[5600],[5601,53,29,5602,5603],"hector2011","LIDAR stabilized about roll and pitch to stay aligned with the ground plane","Sec. VI-A, Fig. 4a",{"c":33,"m":5605,"d":46,"f":5606,"v":5607,"n":23,"y":1819,"u":5608},"HeliOS simulated mobile laser scanner",[],[],[5609],[5610,53,29,5611,5612],"rebolj2017pcqualityscanvsbim","virtual scanner with 360-degree vertical view moved along a modelled person trajectory through all floors; depth accuracy 5, 20, 50, 100, 150, 200 mm; beam divergence 1, 5, 10 deg; frequency 200, 100, 60, 30, 10, 1 kHz (108 combinations)","Sec. 2.3, Sec. 3",{"c":372,"m":5614,"d":46,"f":5615,"v":5616,"n":23,"y":152,"u":5617},"helmet (head-mounted sensor)",[],[],[5618],[2567,53,2568,5619,5620],"person walking indoors and outdoors (campus run)","Sec. 5; Fig. 1",{"c":372,"m":5622,"d":46,"f":5623,"v":5624,"n":23,"y":1008,"u":5625},"helmet (per the dataset name and the title of ref. [21])",[],[],[5626],[2537,23,2538,5627,5628],"Wuhan University helmet-based multisensor dataset","Sec. 4.1, ref. [21]",{"c":372,"m":5630,"d":20,"f":5631,"v":5632,"n":23,"y":233,"u":5633},"helmet carrying the VI-Sensor and ZED-F9P",[],[],[5634],[1058,53,1059,5635,5636],"used in the real-world experiments","Fig. 10",{"c":372,"m":5638,"d":46,"f":5639,"v":5640,"n":23,"y":289,"u":5641},"helmet-mounted sensor and GNSS recorder (bicycle ride)",[],[],[5642],[1074,53,29,5643,5644],"7940 m in 23 min, up to 13.1 m\u002Fs","Sec. VII-B2; Table II; Fig. 14",{"c":372,"m":5646,"d":46,"f":5647,"v":5648,"n":23,"y":346,"u":5649},"helmet-mounted sensor suite with processing computer in a backpack",[],[],[5650],[2905,53,29,5651,4103],"operator running and jumping over a vehicle",{"c":372,"m":5653,"d":46,"f":5654,"v":5655,"n":23,"y":61,"u":5656},"Herbert",[],[],[5657],[5658,53,29,5659,5660],"hahnel2003_compact3d","outdoor robot with one laser on a pan\u002Ftilt unit, angular resolution 0.25 deg","Sec. 2.3; Sec. 4; Fig. 4",{"c":944,"m":5662,"d":46,"f":5663,"v":5665,"n":23,"y":233,"u":5666},"HERON LITE Color",[5664],"Gexcel",[],[5667],[4224,952,29,5668,5669],"handheld, 2018, indoor and outdoor; 360° x 360° FoV camera; 1 Velodyne Puck, 100 m; IMU yes, GPS no; 3 cm relative accuracy (manufacturer)","Table 4; Sec. 4.2; Fig. 3a",{"c":944,"m":5671,"d":46,"f":5672,"v":5673,"n":23,"y":233,"u":5674},"HERON MS Twin",[5664],[],[5675],[4224,952,29,5676,5677],"wearable, 2020, indoor and outdoor; 360° x 360° FoV camera; dual Velodyne Puck, 100 m; IMU yes, GPS no; 3 cm relative accuracy (manufacturer)","Table 4; Sec. 4.2; Fig. 3b",{"c":108,"m":5679,"d":20,"f":5680,"v":5682,"n":23,"y":132,"u":5683},"Hesai 128-line LiDAR",[5681],"Hesai",[],[5684],[1966,53,1967,5685,5686],"128 lines","Sec. IV-C2; Table I",{"c":108,"m":5688,"d":20,"f":5689,"v":5691,"n":23,"y":1008,"u":5692},"Hesai AT128",[5690],"Hesai (as named)",[],[5693],[1101,23,1102,5694,1104],"10 Hz (Table I)",{"c":108,"m":5696,"d":20,"f":5697,"v":5698,"n":23,"y":132,"u":5699},"Hesai LiDAR XT32",[5681],[],[5700],[2298,53,29,185,2300],{"c":108,"m":5702,"d":20,"f":5703,"v":5704,"n":23,"y":49,"u":5705},"Hesai Pandar128",[5681],[],[5706],[1976,23,5707,5708,5709],"HESAI","mechanical LiDAR","Sec. IV-B2, Table I",{"c":108,"m":5711,"d":20,"f":5712,"v":5713,"n":23,"y":49,"u":5714},"Hesai Pandar64",[5681],[],[5715],[1976,23,5707,5708,5709],{"c":108,"m":5717,"d":20,"f":5718,"v":5719,"n":23,"y":49,"u":5720},"Hesai PandarQT Lite",[5681],[],[5721],[1976,23,5707,5708,5709],{"c":108,"m":5723,"d":20,"f":5724,"v":5725,"n":41,"y":49,"u":5726},"Hesai PandarXT",[5681],[],[5727,5728],[1976,23,5707,5708,5709],[2239,53,29,5729,5730],"mechanical spinning; output set to 10 Hz; not synchronized with the Pixhawk IMU","Fig. 1, Sec. IV, Sec. IV-A to IV-C, Table III to IV, Fig. 9",{"c":108,"m":5732,"d":20,"f":5733,"v":5734,"n":23,"y":132,"u":5735},"Hesai PandarXT (32 beams)",[5681],[],[5736],[2221,23,2222,5737,1104],"handheld, labelled indoor campus",{"c":108,"m":5739,"d":20,"f":5740,"v":5741,"n":952,"y":122,"u":5743},"Hesai PandarXT-32",[5707,5681],[5742],"HESAI Pandar XT-32",[5744,5747,5751,5754],[2423,53,29,5745,5746],"self-collected sequence 'private-pandar', images 640 x 512","Supp. 7.2",[5748,23,5749,5750,441],"yarovoi2024review","Hilti SLAM Challenge Dataset 2022","not_reported (points converted to the Velodyne point format, preserving all information)",[1252,23,1253,5752,5753],"10 Hz, 32 channels, range 5 cm to 120 m, 31 deg vertical FoV, 1024 horizontal resolution, range accuracy +\u002F-1 cm, precision 0.5 cm (1 sigma); mounted below the cameras","Table I; Sec. III",[216,23,2231,5755,157],"10 Hz (handheld sequences)",{"c":108,"m":5757,"d":20,"f":5758,"v":5759,"n":41,"y":132,"u":5760},"Hesai QT64",[5681],[],[5761,5763],[1155,23,791,5762,1538],"backpack-mounted",[2662,23,791,5764,5765],"64 channels, 10 Hz, 104 deg FoV, maximum range 60 m, accuracy +\u002F-3 cm typical; rolling-shutter style continuous scanning, motion-undistorted with VILENS IMU preintegration","Sec. 3.1, Sec. 5.1.2",{"c":108,"m":5767,"d":20,"f":5768,"v":5769,"n":23,"y":132,"u":5770},"Hesai QT64 (64 beams)",[5681],[],[5771],[2221,23,791,5772,1104],"backpack, campus",{"c":108,"m":5774,"d":20,"f":5775,"v":5776,"n":952,"y":100,"u":5779},"Hesai XT-32",[5707,5681],[5777,5778],"HESAI XT32","Hesai XT32",[5780,5782,5787,5789],[1145,53,29,5781,2717],"3D LiDAR used for mapping; max range processed reduced to 50 m indoors",[5783,23,5784,5785,5786],"lim2024quatropp","Hilti-Oxford (HiltiOxford)","hand-held sensor configuration","Sec. 6.1; Sec. 7.5",[2173,23,2218,5788,2174],"handheld sequences; LiDAR at 10 Hz",[1155,53,29,5790,4103],"on a tree-harvesting machine (qualitative example only)",{"c":108,"m":5792,"d":20,"f":5793,"v":5794,"n":23,"y":132,"u":5795},"Hesai XT32 (L1)",[5681],[],[5796],[1510,23,1511,5797,5798],"on the Boxi multi-sensor rig","Table I; Table III",{"c":108,"m":5800,"d":20,"f":5801,"v":5802,"n":23,"y":1008,"u":5803},"Hesai XT32, QT32 and QT64 (different sessions)",[5681],[],[5804],[1155,23,5805,5806,1157],"DigiForests","backpack sensor rig; LiDAR inclined 45 deg in the first season",{"c":372,"m":5808,"d":46,"f":5809,"v":5810,"n":23,"y":233,"u":5811},"hex-rotor flying robot (EuRoC MAV)",[],[],[5812],[3947,23,1054,5813,332],"average velocities up to 0.9 m\u002Fs and 0.75 rad\u002Fs in difficult sequences",{"c":372,"m":5815,"d":20,"f":5816,"v":5818,"n":23,"y":346,"u":5819},"hexacopter based on a DJI F550 frame",[5817],"DJI (frame)",[],[5820],[3264,53,29,5821,294],"px4 autopilot for low-level attitude stabilization; all high-level computation on board",{"c":372,"m":5823,"d":46,"f":5824,"v":5825,"n":23,"y":318,"u":5826},"hexacopter MAV",[],[],[5827],[3237,53,29,5828,5829],"carries 64-beam LiDAR, monocular camera, synchronized IMU and RTK-GNSS","Sec. VIII-B; Fig. 5",{"c":944,"m":5831,"d":46,"f":5832,"v":5834,"n":23,"y":233,"u":5835},"Hi-Target HiScan-C",[5833],"Hi-Target",[],[5836],[4224,952,29,5837,5838],"vehicle-mounted, 2017, outdoor; 360° FoV camera; LiDAR range 650 m; IMU and GPS; 5 cm at 40 m range (manufacturer)","Table 4; Sec. 4.1",{"c":651,"m":5840,"d":46,"f":5841,"v":5842,"n":23,"y":270,"u":5843},"high accuracy GPS\u002FINS",[],[],[5844,5846],[2630,41,29,5845,2153],"on the ground vehicle in the orchard drift test",[2630,41,5847,5848,2777],"KITTI odometry benchmark","ground truth of the KITTI benchmark",{"c":651,"m":5850,"d":46,"f":5851,"v":5852,"n":23,"y":1819,"u":5853},"High accuracy GPS\u002FINS on the ground vehicle (model not stated)",[],[],[5854],[5093,41,29,5855,5856],"ground truth for orchard drift tests","Sec. 7.1, Table 2",{"c":33,"m":5858,"d":46,"f":5859,"v":5860,"n":23,"y":346,"u":5861},"high frequency motion capture system (model not reported)",[],[],[5862],[5863,41,991,5864,1084],"supereight2018","trajectory ground truth of the TUM RGB-D sequences",{"c":662,"m":5866,"d":46,"f":5867,"v":5868,"n":23,"y":24,"u":5869},"high precision Inertial Navigation System (model not reported)",[],[],[5870],[27,41,29,5871,5872],"orientation always from the inertial sensors","Secs. 5, 5.1, 5.3",{"c":33,"m":5874,"d":46,"f":5875,"v":5876,"n":23,"y":100,"u":5877},"high-accuracy laser tracking (instrument not named)",[],[],[5878],[211,41,212,5879,214],"provides position ground truth",{"c":18,"m":5881,"d":20,"f":5882,"v":5883,"n":23,"y":100,"u":5884},"high-performance Computing Unit, x64 CPU with CUDA-capable GPU (RA2)",[],[],[5885],[2281,28,29,569,4333],{"c":33,"m":5887,"d":46,"f":5888,"v":5889,"n":23,"y":270,"u":5890},"high-precision motion capturing system (model not reported)",[],[],[5891],[1807,41,1808,5892,5893],"synchronised ground-truth sensor trajectory","Secs. II, IV-A",{"c":5895,"m":5896,"d":46,"f":5897,"v":5898,"n":41,"y":132,"u":5899},"total_station","high-precision total station control network (model not reported)",[],[],[5900,5902],[3836,41,29,5901,530],"12 black and white targets for TLS and MLS alignment",[3840,41,29,5903,3841],"12 black and white targets for aligning MLS and TLS data",{"c":5895,"m":5905,"d":46,"f":5906,"v":5907,"n":23,"y":132,"u":5908},"high-precision total stations (model not reported) with 360° prism",[],[],[5909],[2071,41,29,5910,5911],"3 total stations tracking a 360° prism on the platform","Sec. 3.1.1, 4.1; Fig. 7",{"c":522,"m":5913,"d":46,"f":5914,"v":5915,"n":23,"y":132,"u":5916},"high-resolution global-shutter camera",[],[],[5917],[216,23,1137,5918,157],"2448x2048 RGB, triggered at 10 Hz",{"c":33,"m":5920,"d":46,"f":5921,"v":5922,"n":23,"y":233,"u":5923},"high-resolution scanner (type and model not named)",[],[],[5924],[517,41,5925,5926,3807],"Cow and Lady","reference point cloud supplied with the Cow and Lady dataset, used for the accuracy evaluation",{"c":33,"m":5928,"d":46,"f":5929,"v":5930,"n":23,"y":289,"u":5931},"highly precise motion capture system",[],[],[5932],[5933,41,991,5934,2153],"elasticfusion2015","synchronised ground-truth poses of the TUM RGB-D benchmark",{"c":33,"m":5936,"d":20,"f":5937,"v":5938,"n":23,"y":132,"u":5939},"highly reflective targets, 200 mm diameter, on wooden desks",[],[],[5940],[1990,53,29,5941,5942],"8 GCPs for georeferencing and route correction of GCP runs; coordinates measured with Leica GMP111 mini prism","The used devices and software; Measurement and processing of the reference dataset; Fig. 4",{"c":1689,"m":5944,"d":20,"f":5945,"v":5947,"n":23,"y":132,"u":5948},"HikRobot MV-CS050-10GC",[5946],"HikRobot",[],[5949],[2135,23,2136,5950,5951],"GigE cameras, 1224 x 1024 pixels, 10 Hz, global shutter, fixed exposure","Table 2, Sec. 6.1",{"c":522,"m":5953,"d":20,"f":5954,"v":5956,"n":23,"y":122,"u":5957},"Hikvision CA-050-11UC",[5955],"Hikvision",[],[5958],[3444,53,29,5959,2597],"global shutter RGB camera; images used for mesh texturing with R3LIVE++ poses",{"c":522,"m":5961,"d":46,"f":5962,"v":5963,"n":23,"y":132,"u":5964},"Hikvision cameras (four)",[5955],[],[5965],[1966,53,1967,5966,1969],"four cameras; colored point cloud via R3LIVE",{"c":522,"m":5968,"d":20,"f":5969,"v":5970,"n":23,"y":122,"u":5971},"Hikvision MV-CE060-10UC",[5955],[],[5972],[2160,53,29,5973,5974],"CMOS industrial camera, 3072 x 2048, 1\u002F1.8 in sensor (CMOS length 7.18 mm), USB 3.0","Sec. 6.1; Tables 1-2",{"c":33,"m":5976,"d":20,"f":5977,"v":5978,"n":23,"y":122,"u":5979},"Hikvision MVL-HF0628M-6MP lens",[5955],[],[5980],[2160,53,29,5981,691],"focal length 6 mm",{"c":33,"m":5983,"d":46,"f":5984,"v":5985,"n":23,"y":132,"u":5986},"Hilti handheld and robot-mounted rigs (LiDAR, cameras, IMUs; models not stated in this paper)",[],[],[5987],[135,23,5988,5989,4640],"Hilti'22 and Hilti'23","sensors at different frequencies",{"c":5895,"m":5991,"d":20,"f":5992,"v":5994,"n":23,"y":233,"u":5995},"Hilti PLT 300",[5993],"Hilti",[],[5996],[1038,41,1039,5997,5998],"automated total station tracking a survey-grade prism on the stick in stop-and-go mode, stick gravity-aligned mechanically before each measurement; static prism range accuracy 3 mm","Sec. II, Sec. III-D; Fig. 1",{"c":5895,"m":6000,"d":20,"f":6001,"v":6002,"n":23,"y":100,"u":6003},"Hilti PLT 300 automated total station",[5993],[],[6004],[905,41,6005,6006,332],"Hilti 2021","millimeter-accurate ground truth (or MoCap)",{"c":108,"m":6008,"d":46,"f":6009,"v":6010,"n":23,"y":132,"u":6011},"Hilti-Oxford 32-channel LiDAR (model not reported)",[],[],[6012],[3944,23,6013,6014,3256],"Hilti-Oxford","32-channel point clouds on the handheld device; far plane 30 m in mapping",{"c":33,"m":6016,"d":46,"f":6017,"v":6018,"n":23,"y":132,"u":6019},"Hilti-Oxford ground truth (sparse control positions and mm-accurate dense point clouds)",[],[],[6020],[3944,41,6013,6021,3256],"sparse ground-truth positions for scoring; dense point clouds for exp04 to exp06",{"c":662,"m":6023,"d":46,"f":6024,"v":6025,"n":23,"y":132,"u":6026},"Hilti-Oxford handheld device IMU (model not reported in this paper)",[],[],[6027],[3944,23,6013,6028,6029],"IMU measurements; known camera-IMU time offset accounted for","Secs. VI, VI-C",{"c":522,"m":6031,"d":46,"f":6032,"v":6033,"n":23,"y":132,"u":6034},"Hilti-Oxford handheld rig cameras (5 cameras; model not reported)",[],[],[6035],[3944,23,6013,6036,3256],"all 5 used by the estimator, 2 front cameras for the stereo network, front-left for MVS",{"c":108,"m":6038,"d":20,"f":6039,"v":6041,"n":23,"y":1819,"u":6042},"Hokuyo laser scanner",[6040],"Hokuyo",[],[6043],[5093,53,29,6044,6045],"scanner model not stated; continuously spinning 2-axis lidar of the same design as Fig. 2; sweep is a semi-spherical rotation on the slow axis lasting 1 s","Sec. 7.3, Fig. 14",{"c":108,"m":6047,"d":46,"f":6048,"v":6049,"n":23,"y":299,"u":6050},"Hokuyo line scanner (model not_reported)",[6040],[],[6051],[4141,952,29,6052,6053],"270 deg scan swath; in the i-MMS two heads (one up, one down) give the point cloud and one upright head feeds SLAM; the same scanner is used in the ZEB1","Sec. 2.1; Sec. 2.2",{"c":108,"m":6055,"d":20,"f":6056,"v":6057,"n":23,"y":318,"u":6058},"Hokuyo sensor (model number not given in the paper)",[6040],[],[6059],[6060,23,6061,6062,469],"brossard2020icpcov","Challenging data sets for point cloud registration algorithms (Pomerleau et al., IJRR 2012)","white noise and bias standard deviation taken as 5 cm, the mean value reported for this sensor by Pomerleau et al. (CARPI 2012)",{"c":108,"m":6064,"d":46,"f":6065,"v":6066,"n":23,"y":49,"u":6067},"Hokuyo single-layer LiDARs (three units; model not reported)",[6040],[],[6068],[6069,41,6070,6071,157],"zhou2021planeadjust","own indoor datasets A-D (NavVis M6)","part of the NavVis M6; used by NavVis for its reference trajectory",{"c":108,"m":6073,"d":20,"f":6074,"v":6075,"n":23,"y":318,"u":6076},"HOKUYO URG 04LX-UG01",[6040],[],[6077],[2248,53,29,6078,6079],"2D, indoor use only; scan limited to 180 deg of 240 deg; maximum radius 4000 mm; beam diameter under 20 mm at 2000 mm","Sec. 3.1, 4",{"c":108,"m":6081,"d":20,"f":6082,"v":6083,"n":28,"y":357,"u":6085},"Hokuyo URG-04LX",[6040],[6084],"HOKUYO URG04-LX",[6086,6090,6093],[6087,53,29,6088,6089],"hong2010vicp","2D laser scanner; 785 nm; accuracy plus or minus 10 mm; resolution 1 mm; scan angle 240 deg; range 4000 mm; angular resolution 0.36 deg; 100 ms per scan; 50 x 50 x 70 mm; the paper spells the model URG04-LX (the vendor spelling is URG-04LX)","Table I, Sec. V, VI",[861,53,29,6091,6092],"2D scanner assembled into 3D scans of 340,000 points with a 50 s refresh time","Sec. 3.2; Table 3.7",[360,53,29,6094,6095],"10 Hz horizontal scan over about 240 deg; maximum range limited to 4 m (another sentence states 5 m); USB","Sec. III; Fig. 1 caption ('HOKUYO URG-04')",{"c":108,"m":6097,"d":46,"f":6098,"v":6099,"n":23,"y":122,"u":6100},"Hokuyo UST-10LX (simulated)",[6040],[],[6101],[6102,53,6103,6104,6105],"vegatorres2023ogm2pgbm","Gazebo simulation of the three BIM-based evaluation scenarios (building not named in the paper)","2D LiDAR simulated in Gazebo; glass removed from collision models","Sec. 5.2-5.3",{"c":108,"m":6107,"d":20,"f":6108,"v":6109,"n":6116,"y":37,"u":6117},"Hokuyo UTM-30LX",[6040],[6110,6111,6112,6113,6114,6115],"Hokuyo UTM-30LX (custom rotating 3D lidar)","Hokuyo UTM-30LX-EW","Hokuyo UTM-30LX-EW (rotating)","Hokuyo UTM30-LX","UTM-30LX (motor-rotated)","UTM-30LX-EW (spinning, in Multisense SL)",18,[6118,6120,6123,6127,6129,6132,6135,6138,6142,6145,6148,6151,6153,6156,6159,6161,6163,6166],[467,53,29,6119,2682],"2D time-of-flight, 270 deg FoV, 30 m maximum range, 40 Hz scan rate, 60 x 60 x 85 mm, 210 g; manufacturer range accuracy 3 to 5 cm as cited by the authors",[688,53,6121,6122,691],"Atlas, Valkyrie, HyQ","40 Hz; full-rotation FoV 220 x 180 deg (Atlas entry); produces sparse clouds that AICP accumulates",[771,23,6124,6125,6126],"ETH ASL Datasets Repository (reviewed)","custom-made rotating scanner with a theodolite; lower accuracy and density than TLS","Table 1; Sec. 3.2",[3106,53,29,6128,127],"2D scanner mounted upside down close to the floor; 180 deg field of view with 720 scan points",[5601,53,29,6130,6131],"returns no valid distance when beams hit water","Sec. VI-B, VI-C",[3868,53,29,6133,6134],"2D laser spun on a hand-held device to give 3D scans","Sec. VII; Fig. 2a",[2493,53,29,6136,6137],"spinning single-beam laser, rotor 1 rotation\u002Fs; datasets moved at 0.9 m\u002Fs and 0.7 rad\u002Fs","VoR Sec. VII-A; Fig. 9a",[302,23,6139,6140,6141],"Challenging Laser Registration data sets (Pomerleau et al. 2012)","2D laser range sensor mounted on a tilting platform; 102k to 365k points per scan depending on data set","Sec. 5.2.1; Table 3",[3751,53,3752,6143,6144],"2D LRF, 270 deg apex angle, 0.25 deg resolution, 1080 points per scan line, 62x62x87.5 mm, 210 g; rotated at 1 rotation\u002Fs giving 40 scan lines and 43,200 points per rotation, 3D scans of up to 21,600 points at 2 Hz, ~9 deg angular resolution between lines","Sec. I, Sec. III, Fig. 2",[1424,53,29,6146,6147],"2D laser range finder, full 270 deg scans at 40 Hz, device timestamps quantized to 1 ms, range noise SD set to 7.5 mm, ranges subsampled to about 15%","Sec. IV-A, IV-B, IV-G",[2795,53,29,6149,6150],"2D lightweight pulsed ranging sensor, near infrared 905 nm, inside the ZEB head","Sec. ZEB system operational behaviour",[2630,53,29,6152,2368],"180 deg FoV, 0.25 deg resolution, 40 lines\u002Fs; motor rotates at 180 deg\u002Fs between -90 and 90 deg (1 s sweep); encoder resolution 0.25 deg",[5332,53,29,6154,6155],"2D laser scanner, 180 deg FoV, 0.25 deg resolution, 40 lines\u002Fs; motor-actuated to form a 3D lidar","Sec. VII, Fig. 8",[5093,53,29,6157,6158],"2D scanner turned into a 2-axis back-and-forth spinning lidar by a motor and an encoder (Fig. 2); 180 deg FoV, 0.25 deg resolution, 40 lines\u002Fs; motor rotates at 180 deg\u002Fs between -90 and 90 deg, one sweep lasts 1 s; onboard encoder with 0.25 deg resolution","Sec. 4.1, Fig. 2",[2905,53,29,6160,2907],"43.2 thousand points per second; on a motor-encoder shaft spinning at 1 Hz to act as a 3D scanner (Contour)",[3063,53,3064,6162,3066],"180 deg FoV, 0.25 deg resolution, 40 lines\u002Fs; rotated by a motor for 3D scanning",[3860,53,29,6164,6165],"180 deg field of view, 0.25 deg resolution, 40 lines\u002Fs; rotated by a motor to form a custom 3D lidar","Sec. V-A; Fig. 5",[3116,53,29,6167,332],"40 Hz, 30 m range; mounted on a motor rotating continuously at 180 deg\u002Fs; scans projected with encoder angles",{"c":662,"m":6169,"d":20,"f":6170,"v":6173,"n":41,"y":71,"u":6175},"Honeywell HG1700",[6171,6172],"HONEYWELL","Honeywell",[6174],"HONEYWELL HG1700",[6176,6179],[1573,53,29,6177,6178],"used in the helicopter validation system and the ground-based system; as Novatel SPAN (HG1700 AG58): 0.015 deg roll and pitch, 0.05 deg heading (Table 1)","Table 1; Comparison section",[1606,952,29,6180,1667],"tactical-grade IMU based on a ring laser gyro, 100 Hz",{"c":662,"m":6182,"d":20,"f":6183,"v":6184,"n":23,"y":152,"u":6185},"Honeywell HG1700 (in the AHRS)",[6172],[],[6186],[2111,53,29,6187,332],"AHRS gives drift-free pitch and roll",{"c":662,"m":6189,"d":20,"f":6190,"v":6191,"n":23,"y":142,"u":6192},"Honeywell HG1900",[6172],[],[6193],[5263,53,29,6194,6195],"600 Hz sampling; measured accelerometer noise 0.0775 m\u002Fs2 (1 sigma), gyroscope noise 0.001 rad\u002Fs (1 sigma), accelerometer bias stability +\u002F-0.003 m\u002Fs2, gyroscope bias stability +\u002F-6.0e-5 rad\u002Fs","Sec. VIII-A; Table II",{"c":18,"m":6197,"d":46,"f":6198,"v":6199,"n":23,"y":289,"u":6200},"host computer (model not stated)",[],[],[6201],[1074,28,29,6202,1065],"receives sensor data via Gigabit Ethernet; no specification or timing reported",{"c":944,"m":6204,"d":46,"f":6205,"v":6206,"n":23,"y":233,"u":6207},"Hovermap",[],[],[6208],[1405,53,6209,6210,6211],"CoSTAR multi-robot datasets","provides odometry and point clouds as an alternative front-end","Sec. II-B, III-A",{"c":944,"m":6213,"d":46,"f":6214,"v":6215,"n":23,"y":100,"u":6216},"Hovermap ST",[3811],[],[6217],[2281,53,29,6218,6219],"LiDAR-based dynamic 3D scanner, range up to 100 m; registration relies on LiDAR-based odometry; delivers a registered point cloud at the end of each session","VoR Sec. 4.2.1, Fig. 8c",{"c":944,"m":6221,"d":20,"f":6222,"v":6223,"n":23,"y":1008,"u":6224},"Hovermap ST (Table 1: 'Hovermap ST AUTONOMY')",[],[],[6225],[6226,53,29,6227,6228],"han2026nifcyl","SLAM drift +\u002F-0.03%; FoV 360 x 290 deg; range 0.40 to 100 m; LiDAR accuracy +\u002F-30 mm; mapping accuracy +\u002F-15 mm in typical underground and indoor environments; intensity, range, time, return number and ring attributes; up to 300,000 pts\u002Fs single return, 600,000 pts\u002Fs dual return; handheld, 60 m closed loop in about 5 min","Sec. 2.2, Table 1",{"c":662,"m":6230,"d":46,"f":6231,"v":6232,"n":23,"y":71,"u":6233},"HRP-2 3-axis chest gyro (model not stated)",[],[],[6234],[74,53,29,6235,1416],"reports angular velocity at 200 Hz, sampled at 30 Hz, std 0.01 rad\u002Fs per axis",{"c":522,"m":6237,"d":46,"f":6238,"v":6239,"n":23,"y":71,"u":6240},"HRP-2 additional wide-angle camera (model not stated)",[],[],[6241],[74,53,29,6242,398],"field of view around 90 degrees; one-parameter radial distortion model",{"c":372,"m":6244,"d":20,"f":6245,"v":6246,"n":23,"y":71,"u":6247},"HRP-2 humanoid robot",[],[],[6248],[74,53,29,6249,6250],"walked a 0.75 m radius circle in about 30 s, SLAM on board with a wireless Ethernet link","Sec. 5, 5.3; Fig. 9",{"c":372,"m":6252,"d":46,"f":6253,"v":6254,"n":23,"y":132,"u":6255},"human-driven vehicle platform",[],[],[6256],[2483,23,3771,6257,469],"used for the high-speed loop2 sequence",{"c":372,"m":6259,"d":46,"f":6260,"v":6261,"n":23,"y":122,"u":6262},"human-driving vehicle",[],[],[6263],[2452,23,2577,6264,6265],"urban data collection vehicle","Sec. IV-B2",{"c":6267,"m":6268,"d":20,"f":6269,"v":6271,"n":23,"y":100,"u":6272},"uwb","Humatic P440",[6270],"Humatic",[],[6273],[4121,23,184,569,5457],{"c":6267,"m":6275,"d":20,"f":6276,"v":6278,"n":23,"y":233,"u":6279},"Humatics P440 (2 radios on UAV, 4 antennas; 3 anchors)",[6277],"Humatics",[],[6280],[3460,23,184,6281,6282],"12 UAV-to-anchor pairs, UAV-to-anchor ranges 68.571 Hz, anchor-to-anchor 5.714 Hz, anchors on tripods at nominal 1.5 m height","Sec. 2.4, Table 2, Fig. 3",{"c":372,"m":6284,"d":46,"f":6285,"v":6286,"n":23,"y":1008,"u":6287},"Huron (USV, as written)",[2721],[],[6288],[1011,53,29,6289,147],"same sensors as the UGV minus the infrared camera",{"c":372,"m":6291,"d":46,"f":6292,"v":6293,"n":28,"y":233,"u":6294},"Husky",[2712],[],[6295,6297,6300],[1405,53,6209,6296,717],"wheeled platform equipped with three Velodyne lidars and a Hovermap",[225,53,854,6298,6299],"UGV for the UD Husky dataset","Sec. 7; Figure 15",[2510,53,2511,6301,1867],"skid-steer wheeled robot on rough terrain",{"c":372,"m":6303,"d":46,"f":6304,"v":6305,"n":23,"y":1008,"u":6306},"Husky (UGV)",[2721],[],[6307],[1011,53,29,6308,6309],"base module plus upward camera, upward VLP-16 and Flir ADK; used for both evaluated datasets","Sec. 4.2, 5.1",{"c":372,"m":6311,"d":20,"f":6312,"v":6313,"n":23,"y":152,"u":6314},"Husky A200",[],[],[6315],[6316,53,29,6317,6318],"babin2019robust","indoor-outdoor route on the Universite Laval campus, start and end at the same place","Fig. 4 caption",{"c":372,"m":6320,"d":20,"f":6321,"v":6322,"n":23,"y":49,"u":6323},"Husky A200 series",[],[],[6324],[4290,53,29,6325,6326],"wheeled ground robot; up to two robots in multi-robot trials","Sec. 4, Fig. 16",{"c":372,"m":6328,"d":20,"f":6329,"v":6330,"n":23,"y":152,"u":6331},"Husky A200 UGV",[2721],[],[6332],[3036,53,29,6333,6334],"skid-steered, 4 pneumatic tyres, up to 1 m\u002Fs, 50 kg self weight, 75 kg payload; onboard computer (model not reported)","Sec. Platform Development",{"c":353,"m":6336,"d":46,"f":6337,"v":6338,"n":28,"y":346,"u":6339},"Husky wheel encoders",[],[],[6340,6343,6346],[2739,53,29,6341,6342],"read through a Raspberry Pi and used to scale ORB-SLAM odometry","Sec. 3, Sec. 4.1",[3036,53,29,6344,6345],"linear velocities in x and y used in the EKF","Sec. Mapping",[225,53,854,185,228],{"c":33,"m":6348,"d":46,"f":6349,"v":6351,"n":23,"y":6352,"u":6353},"Hyscan laser triangulation sensor",[6350],"Hymarc, Ltd.",[],1992,[6354],[6355,23,6356,6357,6358],"besl1992icp","NRCC (National Research Council of Canada) African mask range data","commercially available laser triangulation sensor; a low-resolution 64 x 68 gridded image computed from the original data","Sec. VI-C2",{"c":18,"m":6360,"d":20,"f":6361,"v":6362,"n":23,"y":37,"u":6363},"i7 CPU",[98],[],[6364],[6365,28,29,6366,6367],"stereoscan2011","single core at 3.0 GHz used for ELAS dense stereo; the pipeline assumes two CPU cores","Secs. III, III-C",{"c":18,"m":6369,"d":20,"f":6370,"v":6371,"n":23,"y":37,"u":6372},"i7 quad-core CPU",[],[],[6373],[6374,28,29,6375,1924],"dtam2011","host CPU of the GPU system",{"c":18,"m":6377,"d":20,"f":6378,"v":6379,"n":23,"y":49,"u":6380},"i7-10750H",[],[],[6381],[6382,28,29,6383,214],"balm2021","laptop computer with CPU i7-10750H and 16 GiB memory; all experiments",{"c":18,"m":6385,"d":20,"f":6386,"v":6387,"n":23,"y":132,"u":6388},"i7-13700",[98],[],[6389],[3944,28,29,6390,6391],"desktop CPU; all timings in Sec. VI-H","Sec. VI-H",{"c":18,"m":6393,"d":20,"f":6394,"v":6395,"n":23,"y":233,"u":6396},"i7-6700K CPU, 24 GB RAM, GTX970 GPU",[],[],[6397],[2493,28,29,6398,6399],"2.1 s per 1 s of VLP-16, IMU and camera data","VoR Sec. VIII-B",{"c":18,"m":6401,"d":20,"f":6402,"v":6403,"n":23,"y":49,"u":6404},"i7-8565U low-power laptop CPU",[98],[],[6405],[1536,28,29,6406,332],"about 48 ms per prediction",{"c":18,"m":6408,"d":20,"f":6409,"v":6410,"n":23,"y":49,"u":6411},"i7-8850H CPU, 32GB RAM (laptop)",[],[],[6412],[6413,28,29,6414,6415],"yang2021teaser","all tests except 3DMatch","Sec. XI (implementation details)",{"c":18,"m":6417,"d":20,"f":6418,"v":6419,"n":23,"y":100,"u":6420},"i9-13900kf",[],[],[6421],[6422,28,29,6423,478],"madicp2024","CPU with 16 physical cores, written only as 'CPU i9-13900kf'",{"c":18,"m":6425,"d":20,"f":6426,"v":6428,"n":23,"y":4627,"u":6429},"IBM 3081",[6427],"IBM",[],[6430],[4630,28,29,6431,3364],"under IX\u002F370; about 12 times faster than the VAX 11\u002F780",{"c":662,"m":6433,"d":20,"f":6434,"v":6435,"n":41,"y":318,"u":6436},"ICM-20948 (LiDAR IMU)",[],[],[6437,6439],[896,23,815,6438,2207],"3-axis gyroscope and accelerometer at 100 Hz inside the Ouster",[1247,23,1248,6440,1104],"100 Hz, 3-axis gyroscope and accelerometer",{"c":662,"m":6442,"d":20,"f":6443,"v":6444,"n":41,"y":233,"u":6445},"ICM20948",[],[],[6446,6447],[2054,23,1563,569,1104],[1562,23,1563,6448,6449],"internal to the OS1-128; 100 Hz, 9-axis MEMS, intrinsic calibrated","Table II; Sec. III-B3",{"c":662,"m":6451,"d":20,"f":6452,"v":6453,"n":23,"y":132,"u":6454},"ICM20948 (inside Ouster LiDAR)",[],[],[6455],[678,23,679,6456,1374],"9-axis MEMS, 100 Hz",{"c":1689,"m":6458,"d":20,"f":6459,"v":6461,"n":23,"y":233,"u":6462},"IDS uEye 1221 LE (x2, monochrome)",[6460],"IDS",[],[6463],[3460,23,184,6464,6465],"global shutter, forward facing, externally triggered, timestamp difference typically below 3 ms, 10 Hz, 752x480, 120 deg FOV lens; ~20 ms camera-IMU offset found by Kalibr (reported, not corrected)","Sec. 2.3, Table 1, Table 2, Sec. 5.1-5.2",{"c":662,"m":6467,"d":46,"f":6468,"v":6470,"n":23,"y":299,"u":6471},"IGI IMU-IIe",[6469],"IGI mbH",[],[6472],[1606,952,29,6473,5160],"FOG-based IMU, 256 Hz raw data rate, in TERRAcontrol with a 12-channel L1\u002FL2 GNSS receiver",{"c":2807,"m":6475,"d":20,"f":6476,"v":6478,"n":23,"y":132,"u":6479},"Imager 5016",[6477],"Z+F",[],[6480],[2071,53,29,6481,398],"high-precision TLS scanner used by the Z+F FlexScan 22 for data acquisition (Sec. 5.1)",{"c":662,"m":6483,"d":46,"f":6484,"v":6486,"n":23,"y":142,"u":6487},"IMAR AirSurv-RQH",[6485],"IMAR",[],[6488],[6489,53,29,6490,6491],"glennie2012hdl64","navigation grade; expected attitude noise about 0.005 deg roll and pitch, 0.01 deg yaw","Experimental Description; Accuracy of Parameter Estimates",{"c":944,"m":6493,"d":20,"f":6494,"v":6495,"n":23,"y":318,"u":6496},"iMS3D",[2178],[],[6497],[951,952,29,6498,6499],"trolley with several 2D LiDARs, SLAM and an IMU to correct slopes and ground irregularities; 12 kg; 86,400 pts\u002Fs; relative accuracy 3 cm; operating time N\u002FA; Ladybug camera","Sec. 2.3.1; Tables 1-4, 6; Fig. 6",{"c":662,"m":6501,"d":46,"f":6502,"v":6503,"n":23,"y":455,"u":6504},"IMU",[],[],[6505],[4849,41,4850,569,4851],{"c":662,"m":6507,"d":46,"f":6508,"v":6509,"n":23,"y":233,"u":6510},"IMU (model not named; listed together with the RealSense D435i)",[],[],[6511],[2775,53,2776,569,2777],{"c":662,"m":6513,"d":46,"f":6514,"v":6515,"n":28,"y":152,"u":6516},"IMU (model not named)",[],[],[6517,6522,6527],[6518,23,6519,6520,6521],"gaussianlic2025","FAST-LIVO, R3LIVE and MCD datasets","inertial factors in Coco-LIC","Sec. III-B; Sec. IV-A2",[6523,41,6524,6525,6526],"lonet2019","Ford Campus Vision and Lidar","Ford ground-truth trajectories generated from IMU readings","Supplementary Fig. 18 caption; Sec. 4.1",[3805,23,6528,6529,441],"MulRan; NCLT","more than 100 Hz",{"c":662,"m":6531,"d":46,"f":6532,"v":6533,"n":6535,"y":346,"u":6536},"IMU (model not reported)",[],[6534],"IMU (model not_reported)",11,[6537,6539,6542,6544,6545,6547,6550,6552,6553,6556,6557],[610,53,611,569,6538],"Sec. 3, Sec. 3.2",[2316,53,29,6540,6541],"listed on the lower mobile platform; Sec. 4.2 says an attitude source 'like an IMU can be used' to add roll and pitch for the base_laser_link frame","Sec. 4.1; Sec. 4.2",[3153,53,29,6543,5056],"200 Hz in the input-sequence illustration (Fig. 3)",[2173,23,846,2377,2174],[615,53,29,6546,478],"used in Cartographer",[2391,53,29,6548,6549],"placed inside the bus (Fig. 4 caption: an IMU is stuck to the bus); used with the roof-mounted RS-LiDAR-16 in the indoor parking lot and urban tests","Sec. IV-A; Sec. IV-B2; Fig. 4",[2510,53,2511,6551,570],"recorded at 50 Hz; used for motion distortion correction",[155,53,29,2219,157],[3133,53,29,6554,6555],"rigidly attached to the LiDAR with known extrinsic","Sec. III-B-2",[2537,23,2538,569,917],[2429,23,2430,6558,6559],"synchronized IMU measurements included in each CityU sequence; the IMU device (built-in or external) is not named","Sec. 4 (inputs include IMU measurements), Sec. 4.2",{"c":662,"m":6561,"d":46,"f":6562,"v":6563,"n":23,"y":152,"u":6564},"IMU (model not stated in paper or supplement)",[],[],[6565],[1795,23,1796,6566,6567],"benchmark stated to provide full IMU data","Sec. 2, Table 1",{"c":662,"m":6569,"d":46,"f":6570,"v":6571,"n":23,"y":100,"u":6572},"IMU (model not stated in paper)",[],[],[6573],[646,23,647,6574,6575],"attached to the handheld system to enable visual-inertial or LiDAR-inertial odometry use; extrinsics to LiDAR and camera calibrated","Sec. II-A, Sec. II-B",{"c":662,"m":6577,"d":46,"f":6578,"v":6579,"n":28,"y":49,"u":6580},"IMU (model not stated)",[],[],[6581,6582,6585],[1136,53,29,569,3853],[623,53,29,6583,6584],"IMU data at 400 Hz (Fig. 2)","Fig. 2; Sec. III-B; Sec. IV-A",[627,53,29,6586,629],"IMU and LION output provided at up to 200 Hz",{"c":662,"m":6588,"d":46,"f":6589,"v":6590,"n":23,"y":233,"u":6591},"IMU (optional, for initial odometry; model not named)",[],[],[6592],[6593,53,29,569,898],"ltmapper2022",{"c":662,"m":6595,"d":46,"f":6596,"v":6597,"n":23,"y":122,"u":6598},"IMU at 100 Hz (model not stated)",[],[],[6599],[5280,53,6600,6601,2229],"MA-LIO city dataset (City01-03)","100 Hz; sensors time-referenced with PTP but not fired simultaneously",{"c":662,"m":6603,"d":46,"f":6604,"v":6605,"n":23,"y":122,"u":6606},"IMU at 200 Hz (model not stated)",[],[],[6607],[5280,23,5281,1272,1104],{"c":662,"m":6609,"d":46,"f":6610,"v":6611,"n":23,"y":122,"u":6612},"IMU at 400 Hz (model not stated)",[],[],[6613],[5280,23,2577,2219,1104],{"c":662,"m":6615,"d":46,"f":6616,"v":6617,"n":23,"y":100,"u":6618},"IMU embedded in the OS0-64",[],[],[6619],[905,23,6005,6620,6621],"used only by LIO baselines","Table III footnote",{"c":662,"m":6623,"d":46,"f":6624,"v":6625,"n":23,"y":122,"u":6626},"IMU integrated in ZEB-REVO (model not reported)",[],[],[6627],[4660,53,29,6628,6629],"accelerations and rotational velocities used in trajectory update; biases estimated","Sec. 3.1.3",{"c":662,"m":6631,"d":46,"f":6632,"v":6633,"n":23,"y":346,"u":6634},"IMU measuring attitude (model not reported)",[],[],[6635],[1948,53,3553,569,1084],{"c":662,"m":6637,"d":46,"f":6638,"v":6639,"n":23,"y":289,"u":6640},"IMU of an Apple iPad Air 2",[1644],[],[6641],[1648,53,29,6642,6643],"orientation information for the couch sequence; rotation replaces visual rotation estimation","Sec. 5; Sec. 7.1",{"c":662,"m":6645,"d":46,"f":6646,"v":6647,"n":23,"y":233,"u":6648},"IMU of the Ouster OS0-128 (model not reported)",[180],[],[6649],[2265,53,29,6650,530],"IMU data from the LiDAR sensor fed to SLAM tracking",{"c":662,"m":6652,"d":46,"f":6653,"v":6654,"n":23,"y":233,"u":6655},"IMU of the robot payload (model not stated)",[],[],[6656],[2493,53,29,569,2494],{"c":662,"m":6658,"d":46,"f":6659,"v":6660,"n":23,"y":233,"u":6661},"IMU of the time-synchronized VI sensor (model not reported)",[],[],[6662,6664],[3947,23,4487,6663,469],"part of the VI sensor (stereo camera and IMU); time-synchronized with the camera",[3947,23,4940,6665,745],"part of the VI sensor (monocular camera and IMU); time-synchronized with the camera",{"c":662,"m":6667,"d":46,"f":6668,"v":6669,"n":23,"y":1421,"u":6670},"IMU on the Cartographer backpack (model not reported)",[],[],[6671],[2638,53,29,6672,478],"used to estimate the orientation of gravity for projecting scans from the horizontally mounted LIDAR; model and rate not reported",{"c":662,"m":6674,"d":46,"f":6675,"v":6676,"n":23,"y":152,"u":6677},"IMU on the rig (model not reported)",[],[],[6678],[3089,23,3054,6679,898],"time-aligned raw IMU data used by the in-house SLAM",{"c":662,"m":6681,"d":46,"f":6682,"v":6683,"n":23,"y":1819,"u":6684},"IMU synced to the stereo camera (model not named)",[],[],[6685],[1764,53,29,6686,4496],"input to the visual-inertial state estimator",{"c":662,"m":6688,"d":46,"f":6689,"v":6690,"n":23,"y":132,"u":6691},"IMU used for Lidarslam_ros2 preintegration (source not specified; Sec. 5 mentions the robot's IMU)",[],[],[6692],[405,53,29,569,6693],"Sec. 3, 3.3, 5",{"c":651,"m":6695,"d":46,"f":6696,"v":6697,"n":23,"y":152,"u":6698},"IMU\u002FGPS (models not named)",[],[],[6699],[6523,41,578,6700,917],"ground-truth poses for KITTI sequences 00-10",{"c":662,"m":6702,"d":20,"f":6703,"v":6704,"n":23,"y":233,"u":6705},"IMU380ZA-200",[],[],[6706],[236,53,29,6707,332],"acceleration and angular velocity, sampling rate set to 200 Hz; bias ignored in experiments",{"c":372,"m":6709,"d":46,"f":6710,"v":6711,"n":23,"y":49,"u":6712},"industrial AGV with a robot arm for gripping and transporting materials",[],[],[6713],[5237,53,29,6714,6715],"solid-state LiDAR mounted at the front; maximum speed 0.8 m\u002Fs","Sec. IV-C; Fig. 4",{"c":662,"m":6717,"d":46,"f":6718,"v":6719,"n":23,"y":100,"u":6720},"inertial measurement unit (IMU)",[],[],[6721],[1553,53,29,569,2153],{"c":651,"m":6723,"d":46,"f":6724,"v":6725,"n":23,"y":122,"u":6726},"Inertial Navigation System (model not stated)",[],[],[6727],[5280,41,6600,6728,717],"ground truth using only positions with status INS SOLUTION FREE",{"c":651,"m":6730,"d":46,"f":6731,"v":6732,"n":23,"y":152,"u":6733},"inertial navigation system with GPS referenced to a base station (model not stated)",[],[],[6734],[4572,41,842,6735,478],"source of KITTI ground-truth poses; described as accurate but often only locally consistent",{"c":662,"m":6737,"d":20,"f":6738,"v":6739,"n":23,"y":132,"u":6740},"inertial navigation unit of OmniSLAM R6",[],[],[6741],[528,53,29,6742,6743],"fixed LiDAR and IMU configuration across runs","Sec. 3.1-3.2",{"c":662,"m":6745,"d":46,"f":6746,"v":6748,"n":23,"y":71,"u":6749},"Inertial Science ISIS IMU",[6747],"Inertial Science",[],[6750],[2564,53,29,2377,478],{"c":662,"m":6752,"d":46,"f":6753,"v":6755,"n":23,"y":49,"u":6756},"Inertial Sense uINS",[6754],"Inertial Sense",[],[6757],[1164,53,6758,6759,717],"SUSTech dataset (CamVox)","200 Hz, synchronized with the trigger; used for LiDAR motion correction",{"c":651,"m":6761,"d":46,"f":6762,"v":6763,"n":23,"y":49,"u":6764},"Inertial Sense uINS (GPS-RTK)",[6754],[],[6765],[1164,41,6758,6766,717],"GPS-RTK recorded for ground truth",{"c":33,"m":6768,"d":46,"f":6769,"v":6770,"n":23,"y":71,"u":6771},"initialisation target: black rectangle with four known corner features",[],[],[6772],[74,53,29,6773,6774],"defines world frame and metric scale at start-up","Sec. 3.3; Fig. 2a",{"c":662,"m":6776,"d":46,"f":6777,"v":6778,"n":23,"y":100,"u":6779},"INS (model not stated)",[],[],[6780],[5783,53,6781,6782,6783],"KITTI Seq. 06 (Table 5); HiltiOxford (Fig. 18)","raw roll and pitch used to compensate the source cloud before quasi-SO(3) estimation","Sec. 5.5; Sec. 7.5; Table 5",{"c":651,"m":6785,"d":46,"f":6786,"v":6787,"n":23,"y":233,"u":6788},"INS and GPS (model not stated)",[],[],[6789],[5118,23,6790,6791,6792],"Oxford Radar RobotCar","sequences chosen where INS and GPS were available over the whole trajectory","Sec. VI-A3",{"c":372,"m":6794,"d":46,"f":6795,"v":6797,"n":23,"y":152,"u":6798},"Inspector Bots Super Mega Bot mobile base with Kinova Jaco arm (robot 'Waco')",[6796],"Inspector Bots; Kinova",[],[6799],[6800,53,29,6801,2113],"gawel2019fabricatorloc","skid-steered four-wheel base, 6-DoF arm, custom 3D-printed end-effector with spring-loaded marker",{"c":372,"m":6803,"d":46,"f":6804,"v":6805,"n":23,"y":455,"u":6806},"instrumented car",[],[],[6807],[4849,53,4850,569,4851],{"c":651,"m":6809,"d":46,"f":6810,"v":6811,"n":23,"y":37,"u":6812},"integrated navigation system (INS) fusing GPS, wheel speed sensors and inertial measurements (model not reported)",[],[],[6813],[4039,952,4040,6814,6815],"local errors higher than those of the proposed method; not usable as ground truth","Sec. III; Table I",{"c":33,"m":6817,"d":46,"f":6818,"v":6819,"n":23,"y":233,"u":6820},"integrated navigation system (model not named)",[],[],[6821],[517,41,6822,6823,6824],"Apollo-SouthBay","source of the ground-truth poses used for the Apollo-SouthBay example","Sec. 5.6.4",{"c":18,"m":6826,"d":20,"f":6827,"v":6828,"n":23,"y":299,"u":6829},"Intel Atom Z530",[98],[],[6830],[302,28,29,6831,6832],"about 10 Hz tracking","Sec. 5.1; Fig. 3",{"c":18,"m":6834,"d":20,"f":6835,"v":6836,"n":23,"y":37,"u":6837},"Intel Atom Z530 based CPU board",[98],[],[6838],[5601,28,29,6839,3256],"embedded board of the handheld mapping system",{"c":18,"m":6841,"d":20,"f":6842,"v":6843,"n":23,"y":142,"u":6844},"Intel Core 2 Duo",[98],[],[6845],[6846,28,29,569,5425],"sunderhauf2012switchable",{"c":18,"m":6848,"d":20,"f":6849,"v":6850,"n":23,"y":71,"u":6851},"Intel Core 2 Duo 2.66 GHz desktop PC",[98],[],[6852],[6853,28,29,6854,6855],"ptam2007","dual-core processor, Linux, C++ with libCVD and TooN","Sec. 6.5",{"c":18,"m":6857,"d":20,"f":6858,"v":6859,"n":23,"y":132,"u":6860},"Intel Core i3-3120M CPU",[98],[],[6861],[5348,28,29,6862,6863],"with 16 GB DDR3 RAM and 500 GB SSD; mean CPU and RAM of all SLAM threads recorded over 181 s","Sec. 7.1.2",{"c":18,"m":6865,"d":20,"f":6866,"v":6868,"n":23,"y":233,"u":6869},"Intel Core i5-3210M laptop",[6867],"Intel (CPU)",[],[6870],[236,28,29,6871,1351],"6 GB RAM, Ubuntu 14.04; used for CPU load and memory usage evaluation via top",{"c":18,"m":6873,"d":20,"f":6874,"v":6875,"n":23,"y":49,"u":6876},"Intel Core i5-8250U CPU @ 1.60GHz x 8, 19.5 GB RAM",[98],[],[6877],[6878,28,29,6879,478],"manhattanslam2021","no GPU used; about 15 Hz",{"c":18,"m":6881,"d":20,"f":6882,"v":6883,"n":41,"y":299,"u":6884},"Intel Core i7",[98],[],[6885,6888],[1807,28,29,6886,6887],"3.40 GHz; used for all experiments","Secs. IV, IV-B",[6889,28,29,6890,76],"kazhdan2013screened","quad-core; laptop with 8 GB RAM",{"c":18,"m":6892,"d":20,"f":6893,"v":6894,"n":23,"y":299,"u":6895},"Intel Core i7 2.66 GHz",[98],[],[6896],[2604,28,29,6897,6898],"single-threaded C++ implementation","Secs. 3.4, 9",{"c":18,"m":6900,"d":20,"f":6901,"v":6902,"n":23,"y":233,"u":6903},"Intel Core i7 3.2 GHz workstation (Ubuntu 18.04)",[98],[],[6904],[3947,28,29,6905,478],"8 cores used for the optimization",{"c":18,"m":6907,"d":20,"f":6908,"v":6909,"n":23,"y":152,"u":6910},"Intel Core i7 3.4GHz 4-core CPU",[98],[],[6911],[6523,28,29,6912,6913],"data preparation 8.5 ms and mapping 61.4 ms per scan","Sec. 4.5; Table 4",{"c":18,"m":6915,"d":20,"f":6916,"v":6917,"n":23,"y":299,"u":6918},"Intel Core i7 3.4GHz CPU",[98],[],[6919],[1786,28,29,6920,6921],"16 GB RAM","Sec. 9.1",{"c":18,"m":6923,"d":20,"f":6924,"v":6925,"n":23,"y":1819,"u":6926},"Intel Core i7 3.4GHz CPU (32GB RAM)",[98],[],[6927],[1822,28,29,6928,6929],"desktop host","Sec. 6 Performance and Convergence",{"c":18,"m":6931,"d":20,"f":6932,"v":6933,"n":23,"y":152,"u":6934},"Intel Core i7 6700K",[98],[],[6935],[1795,28,29,569,6936],"Sec. 6 Test environment",{"c":18,"m":6938,"d":20,"f":6939,"v":6940,"n":23,"y":346,"u":6941},"Intel Core i7 7700 @3.6GHz CPU",[98],[],[6942],[1825,28,29,6943,127],"CPU used for all dataset experiments",{"c":18,"m":6945,"d":20,"f":6946,"v":6947,"n":23,"y":100,"u":6948},"Intel Core i7 8700K",[98],[],[6949],[4121,28,29,6950,6951],"CPU used for the processing-time measurement","Sec. VI-C; Table VII",{"c":18,"m":6953,"d":20,"f":6954,"v":6955,"n":23,"y":318,"u":6956},"Intel Core i7 NUC",[98],[],[6957],[896,23,815,6958,898],"onboard recording computer, Ubuntu 18.04, kernel 4.15.0-74-generic",{"c":18,"m":6960,"d":20,"f":6961,"v":6962,"n":23,"y":299,"u":6963},"Intel Core i7 Q820",[98],[],[6964],[302,28,29,6965,6832],"Kinect tracker, real time with 4,000 points on a single laptop core",{"c":18,"m":6967,"d":20,"f":6968,"v":6969,"n":23,"y":100,"u":6970},"Intel Core i7-11700",[98],[],[6971],[211,28,29,6972,214],"consumer-level computer, 32 GB RAM",{"c":18,"m":6974,"d":20,"f":6975,"v":6976,"n":23,"y":233,"u":6977},"Intel Core i7-11800H",[98],[],[6978],[585,28,29,6979,898],"2021 XMG 64-bit laptop, 2.30 GHz x 8 cores, 24576 KB cache",{"c":18,"m":6981,"d":20,"f":6982,"v":6983,"n":23,"y":122,"u":6984},"Intel Core i7-12700K",[98],[],[6985],[1890,28,29,6986,6987],"3.60 GHz desktop CPU","Sec. 4.1; Sec. 4.3",{"c":18,"m":6989,"d":20,"f":6990,"v":6991,"n":23,"y":1008,"u":6992},"Intel Core i7-14700KF CPU",[98],[],[6993],[2366,28,29,569,332],{"c":18,"m":6995,"d":20,"f":6996,"v":6997,"n":41,"y":299,"u":6998},"Intel Core i7-2600",[98],[],[6999,7003],[7000,28,29,7001,7002],"hornung2013octomap","3.4 GHz; single core used","Sec. 5.5",[4061,28,29,7004,127],"3.40GHz, 16GB RAM; visual odometry and SLAM run in separate threads",{"c":18,"m":7006,"d":20,"f":7007,"v":7008,"n":23,"y":1819,"u":7009},"Intel Core i7-2760QM laptop",[98],[],[7010],[3618,28,29,7011,7012],"2.80 GHz","Sec. XI-B-1, Table II",{"c":18,"m":7014,"d":20,"f":7015,"v":7016,"n":23,"y":152,"u":7017},"Intel Core i7-3770",[98],[],[7018],[2555,28,29,7019,313],"four cores, 6 GB RAM, 512 GB SSD, Ubuntu 16.04; single core for offline datasets",{"c":18,"m":7021,"d":20,"f":7022,"v":7023,"n":23,"y":289,"u":7024},"Intel Core i7-3960X",[98],[],[7025],[2830,28,29,7026,3677],"3.30 GHz, 16 GB RAM, Ubuntu 12.04 desktop",{"c":18,"m":7028,"d":20,"f":7029,"v":7030,"n":23,"y":289,"u":7031},"Intel Core i7-4700MQ",[98],[],[7032],[2595,28,29,7033,7034],"4 cores at 2.40 GHz, 8 GB RAM; no GPU","Sec. VIII",{"c":18,"m":7036,"d":20,"f":7037,"v":7038,"n":23,"y":1819,"u":7039},"Intel Core i7-4790",[98],[],[7040],[1783,28,29,7041,478],"desktop computer, 16 GB RAM",{"c":18,"m":7043,"d":20,"f":7044,"v":7045,"n":23,"y":49,"u":7046},"Intel Core i7-4790K",[98],[],[7047],[3692,28,29,7048,7049],"8-core CPU in a desktop PC used for all runtime benchmarks","Sec. V-C, Table III",{"c":18,"m":7051,"d":20,"f":7052,"v":7053,"n":23,"y":289,"u":7054},"Intel Core i7-4930K",[98],[],[7055],[5933,28,29,7056,2777],"3.4GHz, 32GB of RAM",{"c":18,"m":7058,"d":20,"f":7059,"v":7060,"n":23,"y":1421,"u":7061},"Intel Core i7-5960X",[98],[],[7062],[7063,28,29,7064,398],"zhou2016fgr","3.00 GHz; all execution times measured with a single thread",{"c":18,"m":7066,"d":20,"f":7067,"v":7068,"n":23,"y":289,"u":7069},"Intel Core i7-5960X (CPU-only implementation, partly OpenMP)",[98],[],[7070],[1648,28,29,7071,7072],"workstation CPU runs","Sec. 7.1; Table 1",{"c":18,"m":7074,"d":20,"f":7075,"v":7076,"n":23,"y":346,"u":7077},"Intel Core i7-6600U CPU at 2.60 GHz",[98],[],[7078],[5863,28,29,7079,294],"used for the path-planning timings (GCC 5.4.0)",{"c":18,"m":7081,"d":20,"f":7082,"v":7083,"n":23,"y":152,"u":7084},"Intel Core i7-6700 CPU @ 3.4 GHz, 16 GB RAM",[98],[],[7085],[6800,28,29,7086,332],"on-board computer; separate off-board computer over WLAN for the building task interface",{"c":18,"m":7088,"d":20,"f":7089,"v":7090,"n":23,"y":346,"u":7091},"Intel Core i7-6700HQ",[98],[],[7092],[1948,28,29,7093,127],"quad-core at 2.6 GHz, 32 GB RAM",{"c":18,"m":7095,"d":20,"f":7096,"v":7097,"n":23,"y":49,"u":7098},"Intel Core i7-7700",[98],[],[7099],[3658,28,29,7100,4496],"3.6 GHz, 32 GB memory, CPU only",{"c":18,"m":7102,"d":20,"f":7103,"v":7104,"n":23,"y":49,"u":7105},"Intel Core i7-7700HQ",[98],[],[7106],[1976,28,29,7107,478],"2.80 GHz; all experiments",{"c":18,"m":7109,"d":20,"f":7110,"v":7111,"n":23,"y":100,"u":7112},"Intel Core i7-7700K",[98],[],[7113],[5783,28,29,7114,7115],"preprocessing and correspondence timing","Fig. 16",{"c":18,"m":7117,"d":20,"f":7118,"v":7119,"n":23,"y":233,"u":7120},"Intel Core i7-7700K at 4.2GHz desktop",[98],[],[7121],[742,28,29,7122,478],"used only to run ORB-SLAM3 (not supported on macOS)",{"c":18,"m":7124,"d":20,"f":7125,"v":7126,"n":23,"y":132,"u":7127},"Intel Core i7-8700",[98],[],[7128],[6518,28,29,7129,793],"3.2 GHz CPU, 32 GB RAM",{"c":18,"m":7131,"d":20,"f":7132,"v":7133,"n":23,"y":357,"u":7134},"Intel Core i7-920",[98],[],[7135],[7136,28,29,7137,214],"karto_spa2010","2.67 GHz",{"c":18,"m":7139,"d":20,"f":7140,"v":7141,"n":23,"y":37,"u":7142},"Intel Core i7-930",[98],[],[7143],[7144,28,29,7145,127],"kummerle2011g2o","one core at 2.8 GHz",{"c":18,"m":7147,"d":20,"f":7148,"v":7149,"n":23,"y":233,"u":7150},"Intel Core i7-9700 + NVIDIA RTX 2080 Ti",[3317],[],[7151],[781,28,29,7152,917],"Ubuntu 16.04, Python, TensorFlow; RandLA-Net training and inference",{"c":18,"m":7154,"d":20,"f":7155,"v":7156,"n":23,"y":49,"u":7157},"Intel Core i7-9750H",[98],[],[7158],[7159,28,29,7160,332],"saloam2021","3.00 GHz, 16 GB RAM",{"c":18,"m":7162,"d":20,"f":7163,"v":7164,"n":23,"y":132,"u":7165},"Intel Core i7-9800X",[98],[],[7166],[5523,28,29,7167,332],"3.80 GHz; the method runs mainly on the GPU except for data transmission",{"c":18,"m":7169,"d":20,"f":7170,"v":7171,"n":41,"y":100,"u":7173},"Intel Core i9 12900K",[98],[7172],"Intel Core i9-12900K",[7174,7176],[2054,28,29,7175,478],"3.50 GHz",[7177,28,29,7178,332],"hislam2_2025","CPU for all evaluations",{"c":18,"m":7180,"d":20,"f":7181,"v":7182,"n":41,"y":100,"u":7183},"Intel Core i9 12900K 3.50GHz",[98],[],[7184,7187],[7185,28,29,7186,917],"monogs2024","desktop CPU",[3211,28,29,7186,313],{"c":18,"m":7189,"d":20,"f":7190,"v":7191,"n":23,"y":1008,"u":7192},"Intel Core i9 14900K",[98],[],[7193],[6226,28,29,7194,917],"desktop CPU, Linux",{"c":18,"m":7196,"d":20,"f":7197,"v":7198,"n":23,"y":122,"u":7199},"Intel Core i9-10920X",[98],[],[7200],[7201,28,29,7202,917],"goslam2023","3.5 GHz CPU",{"c":18,"m":7204,"d":20,"f":7205,"v":7206,"n":23,"y":100,"u":7207},"Intel Core i9-12900HX",[98],[],[7208],[7209,28,29,7210,917],"photoslam2024","laptop CPU, 32 GB RAM",{"c":18,"m":7212,"d":20,"f":7213,"v":7214,"n":23,"y":100,"u":7215},"Intel Core i9-12900KF",[98],[],[7216],[114,28,29,7217,3518],"desktop; main experiments",{"c":18,"m":7219,"d":20,"f":7220,"v":7221,"n":41,"y":100,"u":7222},"Intel Core i9-13900",[98],[],[7223,7224],[5783,28,29,7114,7115],[841,28,29,7225,7226],"runtime and parameter studies","Table I caption; Sec. III-C; Fig. 7 caption",{"c":18,"m":7228,"d":20,"f":7229,"v":7230,"n":23,"y":132,"u":7231},"Intel Core i9-13900HX",[98],[],[7232],[2423,28,29,7233,7234],"5.50 GHz CPU, 64 GB RAM desktop PC","Sec. 4.1 Implementation Details",{"c":18,"m":7236,"d":20,"f":7237,"v":7238,"n":28,"y":100,"u":7239},"Intel Core i9-13900K",[98],[],[7240,7242,7244],[7241,28,29,7186,917],"deng2026_mcgs_slam",[7209,28,29,7243,917],"desktop CPU, 64 GB RAM",[7245,28,29,7246,917],"gsslam2024","5.50 GHz CPU",{"c":18,"m":7248,"d":20,"f":7249,"v":7250,"n":41,"y":49,"u":7251},"Intel Core i9-9900K",[98],[],[7252,7255],[7253,28,29,7254,332],"koide2021vgicp","CPU used for all methods",[7256,28,29,7257,332],"litamin2_2021","desktop PC with 32 GB RAM",{"c":18,"m":7259,"d":20,"f":7260,"v":7261,"n":23,"y":100,"u":7262},"Intel Core i9-9900KF",[98],[],[7263],[5783,28,29,7264,7265],"optimization timing (Quatro 5.0 ms KITTI, 6.4 ms NAVER LABS)","Fig. 17",{"c":18,"m":7267,"d":20,"f":7268,"v":7269,"n":23,"y":100,"u":7271},"Intel Core Xeon(R) Gold 6248R",[98],[7270],"Intel Core Xeon(R) Gold 6248R (as written)",[7272],[5070,28,29,7273,1204],"3.00 GHz, 32 GB RAM, Ubuntu 18.04",{"c":18,"m":7275,"d":20,"f":7276,"v":7277,"n":23,"y":455,"u":7278},"Intel Core2 Duo 2.80 GHz, 2 GiB RAM (one core used)",[98],[],[7279],[1000,28,29,7280,7281],"NDT and ICP pairwise experiments","Sec. 6.4.2",{"c":1689,"m":7283,"d":20,"f":7284,"v":7285,"n":23,"y":152,"u":7286},"Intel D435",[98],[],[7287],[1795,952,29,7288,7289],"infrared emitter considered but not used because its projected pattern has lower resolution than the Xtion pattern","Supp. Sec. 3.1, Fig. 3",{"c":18,"m":7291,"d":20,"f":7292,"v":7293,"n":23,"y":122,"u":7294},"Intel E-2186M",[98],[],[7295],[1462,28,29,7296,7297],"processor in a laptop; 6 cores\u002F12 threads, 2.9 GHz base frequency; 16 GB RAM","Sec. VII-D, Table IV",{"c":18,"m":7299,"d":20,"f":7300,"v":7301,"n":23,"y":318,"u":7302},"Intel E5-1620",[98],[],[7303],[3953,28,29,7304,7305],"4 cores, 8 virtual cores; implementation uses all available CPU resources","Sec. VI-d",{"c":18,"m":7307,"d":20,"f":7308,"v":7309,"n":41,"y":49,"u":7310},"Intel Hades Canyon NUC8i7HVKVA",[98],[],[7311,7313],[4290,28,29,7312,313],"4 x 1.9 GHz, 32 GB RAM; base station merging local pose graphs",[1359,28,29,7314,7315],"4 x 1.9 GHz, 32 GB RAM, Ubuntu 18.04 LTS; used for the efficiency comparison","Sec. III-B3",{"c":18,"m":7317,"d":20,"f":7318,"v":7319,"n":23,"y":100,"u":7320},"Intel i5-12500",[98],[],[7321],[1371,28,1372,7322,1374],"Kim et al. (FAST-LIO2, Point-LIO, Quatro) runtime platform",{"c":18,"m":7324,"d":20,"f":7325,"v":7326,"n":23,"y":318,"u":7327},"Intel i5-6300U",[98],[],[7328],[5254,28,29,7329,137],"laptop; 2.40 GHz; 24 GiB RAM; single-thread Matlab code, not optimised for high performance",{"c":18,"m":7331,"d":20,"f":7332,"v":7333,"n":23,"y":100,"u":7334},"Intel i5-9400",[98],[],[7335],[1371,28,1372,7336,1374],"Jiang et al. (LET-NET, VINS-Mono) runtime platform",{"c":18,"m":7338,"d":20,"f":7339,"v":7340,"n":23,"y":233,"u":7341},"Intel i7 10700F",[98],[],[7342],[1549,28,29,7343,1072],"CPU-only inference 28 ms",{"c":18,"m":7345,"d":20,"f":7346,"v":7348,"n":23,"y":100,"u":7349},"intel i7 10750-H with nvidia 2070",[7347],"Intel and NVIDIA",[],[7350],[7351,28,7352,7353,7354],"rtgslam2024","Azure dataset (self-scanned)","laptop for data acquisition and viewing; frames sent to the desktop over wireless network","Supp. C",{"c":18,"m":7356,"d":20,"f":7357,"v":7358,"n":23,"y":1819,"u":7359},"Intel i7 2.1 GHz (on-board)",[98],[],[7360],[1764,28,29,7361,4496],"on-board MAV computer",{"c":18,"m":7363,"d":20,"f":7364,"v":7365,"n":23,"y":49,"u":7366},"Intel i7 3.2GHz processor based computer",[98],[],[7367],[1850,28,832,7368,469],"used to time all compared methods on KITTI; ROS Melodic, Ubuntu 18.04",{"c":18,"m":7370,"d":20,"f":7371,"v":7372,"n":23,"y":1421,"u":7373},"Intel i7 3.4GHz CPU (standard PC)",[98],[],[7374],[7375,28,29,7376,3364],"yang2016goicp","C++ implementation",{"c":18,"m":7378,"d":20,"f":7379,"v":7381,"n":23,"y":152,"u":7382},"Intel i7 8-core 3.2 GHz CPU with Nvidia Titan-X Pascal (used by baselines [7], [6], [36])",[7380],"Intel; Nvidia",[],[7383],[7384,28,29,7385,7386],"choy2019fcgf","8-core 3.2 GHz","Sec. 6.7",{"c":18,"m":7388,"d":20,"f":7389,"v":7390,"n":23,"y":299,"u":7391},"Intel i7 8-core CPU",[98],[],[7392],[7393,28,29,7394,7395],"keller2013pointfusion","8-core","Table 1 caption",{"c":18,"m":7397,"d":46,"f":7398,"v":7399,"n":23,"y":122,"u":7400},"Intel i7 CPU @ 2.50 GHz (model not stated)",[98],[],[7401],[5280,28,29,7402,3922],"48 GB RAM",{"c":18,"m":7404,"d":20,"f":7405,"v":7407,"n":23,"y":100,"u":7408},"Intel i7 CPU + NVIDIA RTX 3000 Mobile (Laptop)",[7406],"Intel, NVIDIA",[],[7409],[7410,28,29,7411,127],"millane2024nvblox","not_reported beyond model",{"c":18,"m":7413,"d":46,"f":7414,"v":7415,"n":23,"y":49,"u":7416},"Intel i7 CPU at 3.1 GHz (model not reported)",[98],[],[7417],[6069,28,29,7418,4070],"16 GB memory",{"c":18,"m":7420,"d":20,"f":7421,"v":7422,"n":2256,"y":49,"u":7423},"Intel i7-10700",[98],[],[7424,7426,7428,7431,7433],[3483,28,29,7425,1084],"PC, 2.90 GHz, only 2 threads running",[567,28,29,7427,3518],"2.90 GHz, 32 GB RAM, desktop",[5386,28,29,7429,7430],"desktop computer, 2.9 GHz, 16 GB RAM; used for all experiments","Sec. IV (intro); Sec. IV-C",[1371,28,1372,7432,1374],"Yibin et al. (LIO-EKF) and Li et al. (ORB-SLAM3) runtime platform",[2239,28,29,7434,2241],"desktop computer, 2.90 GHz, 32 GB RAM; all experiments",{"c":18,"m":7436,"d":20,"f":7437,"v":7438,"n":23,"y":100,"u":7439},"Intel i7-10700 CPU",[98],[],[7440],[3805,28,29,7441,441],"2.90 GHz, 16 cores, 15.5 GB RAM",{"c":18,"m":7443,"d":20,"f":7444,"v":7445,"n":23,"y":49,"u":7447},"Intel i7-10700k",[98],[7446],"Intel i7-10700k (as written)",[7448],[623,28,29,7449,7450],"ROS on Ubuntu Linux","Sec. IV-A; Table VI",{"c":18,"m":7452,"d":20,"f":7453,"v":7454,"n":23,"y":233,"u":7455},"Intel i7-10700K CPU (3.80 GHz)",[98],[],[7456],[7457,28,29,7458,917],"niceslam2022","desktop PC used for all NICE-SLAM runs",{"c":18,"m":7460,"d":20,"f":7461,"v":7462,"n":41,"y":233,"u":7463},"Intel i7-10750H",[98],[],[7464,7466],[1340,28,29,7465,127],"laptop CPU, 6 cores, boost 5 GHz; used for accuracy experiments",[201,28,29,7467,478],"desktop; 16 GB RAM (written '16Gb RAM'); Ubuntu 20.04",{"c":18,"m":7469,"d":20,"f":7470,"v":7471,"n":23,"y":100,"u":7472},"Intel i7-10875H",[98],[],[7473],[7474,28,29,7475,898],"iglio2024","2.30 GHz x 16 cores, 32 GB RAM, ROS on Ubuntu 18.04",{"c":18,"m":7477,"d":20,"f":7478,"v":7479,"n":23,"y":122,"u":7480},"Intel i7-11700k",[98],[],[7481],[7482,28,29,7483,478],"std2023","@ 3.6 GHz with 16 GB memory; same system for all experiments",{"c":18,"m":7485,"d":20,"f":7486,"v":7487,"n":23,"y":122,"u":7488},"Intel i7-11700KF",[98],[],[7489],[7490,28,29,7491,332],"ruan2023slamesh","3.6 GHz, 8 cores; 8 threads allocated",{"c":18,"m":7493,"d":20,"f":7494,"v":7495,"n":41,"y":122,"u":7496},"Intel i7-11800H",[98],[],[7497,7499],[2995,28,29,7498,478],"16-core CPU",[5228,28,29,7500,332],"mobile CPU",{"c":18,"m":7502,"d":20,"f":7503,"v":7504,"n":23,"y":132,"u":7505},"Intel i7-12700k CPU, 96 GB RAM (desktop)",[98],[],[7506],[657,28,29,7507,7508],"desktop used for all MapEval experiments; Open3D and PCL implementation, single-threaded for the proposed metrics, multi-threaded MME","Sec. IV-A3; Sec. IV-D; Table VII",{"c":18,"m":7510,"d":20,"f":7511,"v":7512,"n":23,"y":289,"u":7513},"Intel i7-2760QM",[98],[],[7514],[1050,28,29,7515,7516],"single core","Sec. IV-B; Table I",{"c":18,"m":7518,"d":20,"f":7519,"v":7520,"n":23,"y":152,"u":7522},"Intel i7-3700K",[98],[7521],"Intel i7-3700K (as written)",[7523],[2567,28,29,569,7524],"Sec. 5 Runtime",{"c":18,"m":7526,"d":20,"f":7527,"v":7528,"n":23,"y":346,"u":7529},"Intel i7-4770HQ laptop, 16GB RAM",[98],[],[7530],[3979,28,29,7531,3518],"Ubuntu 18.04",{"c":18,"m":7533,"d":20,"f":7534,"v":7535,"n":23,"y":346,"u":7536},"Intel i7-4790",[98],[],[7537],[1078,28,29,7538,7539],"3.60 GHz; campus dataset timing (Table II)","Sec. IX-B-2",{"c":18,"m":7541,"d":20,"f":7542,"v":7543,"n":23,"y":152,"u":7544},"Intel i7-4790 desktop CPU",[98],[],[7545],[155,28,1054,7546,7547],"3.60 GHz; EuRoC VIO results obtained in real time","Sec. VIII-C",{"c":18,"m":7549,"d":20,"f":7550,"v":7551,"n":23,"y":346,"u":7552},"Intel i7-4790k",[98],[],[7553],[7554,28,29,7555,7556],"yun2018reflection","4.38 GHz as written","Sec. 5; Table 1",{"c":18,"m":7558,"d":20,"f":7559,"v":7560,"n":23,"y":346,"u":7561},"Intel i7-4910MQ CPU",[98],[],[7562],[349,28,29,7563,917],"hard-enforced real-time evaluation",{"c":18,"m":7565,"d":20,"f":7566,"v":7567,"n":23,"y":346,"u":7568},"Intel i7-5500U",[98],[],[7569],[1078,28,29,7570,7571],"3.00 GHz onboard the aerial robot","Sec. IX-C-1",{"c":18,"m":7573,"d":20,"f":7574,"v":7575,"n":23,"y":346,"u":7576},"Intel i7-6700",[98],[],[7577],[7578,28,29,7579,478],"suma2018","3.4 GHz, 16 GB RAM",{"c":18,"m":7581,"d":20,"f":7582,"v":7583,"n":23,"y":346,"u":7584},"Intel i7-6700 CPU",[98],[],[7585],[7586,28,29,7587,478],"scancontext2018","3.40 GHz, 16 GB memory; Matlab",{"c":18,"m":7589,"d":20,"f":7590,"v":7591,"n":41,"y":318,"u":7592},"Intel i7-6700K",[98],[],[7593,7595],[4865,28,29,7594,398],"processor used for all experiments",[636,28,29,3272,478],{"c":18,"m":7597,"d":20,"f":7598,"v":7599,"n":23,"y":152,"u":7600},"Intel i7-6950 (10-core, 3.0 GHz)",[98],[],[7601],[7384,28,29,7602,7386],"10-core 3.0 GHz",{"c":18,"m":7604,"d":20,"f":7605,"v":7606,"n":23,"y":142,"u":7607},"Intel i7-720",[98],[],[7608],[7609,28,29,7610,7611],"kaess2012isam2","1.6 GHz; laptop; all timing results; iSAM2 research C++ implementation running single-threaded","Comparison to other methods",{"c":18,"m":7613,"d":20,"f":7614,"v":7615,"n":23,"y":233,"u":7616},"Intel i7-7700HQ",[98],[],[7617],[4511,28,29,7618,478],"laptop CPU at 2.8 GHz, 8 GB RAM, ROS on Ubuntu 16.04",{"c":18,"m":7620,"d":20,"f":7621,"v":7622,"n":23,"y":152,"u":7623},"Intel i7-7700K",[98],[],[7624],[4789,28,29,7625,2777],"4.20 GHz, 16 GB RAM",{"c":18,"m":7627,"d":20,"f":7628,"v":7629,"n":23,"y":318,"u":7630},"Intel i7-8086k CPU@4.0GHz",[98],[],[7631],[4730,28,29,7632,1204],"desktop computer (Sec. VI-B)",{"c":18,"m":7634,"d":20,"f":7635,"v":7636,"n":23,"y":122,"u":7637},"Intel i7-8550U onboard computer",[98],[],[7638],[7639,28,29,7640,7641],"he2023ikfom","1.8 GHz quad-core CPU, 8 GB RAM","Sec. IV; Sec. IV-C3",{"c":18,"m":7643,"d":20,"f":7644,"v":7645,"n":28,"y":318,"u":7646},"Intel i7-8700",[98],[],[7647,7649,7650],[924,28,29,7648,2486],"3.2 GHz",[1029,28,29,569,332],[5237,28,29,7651,332],"6-core desktop CPU (VICON experiment)",{"c":18,"m":7653,"d":20,"f":7654,"v":7655,"n":23,"y":132,"u":7656},"Intel i7-8700 at 3.20 GHz",[98],[],[7657],[282,28,29,7658,917],"desktop CPU used for MulRan per-scan timings",{"c":18,"m":7660,"d":20,"f":7661,"v":7662,"n":23,"y":233,"u":7663},"Intel i7-8700K at 3.7 GHz, 32 GB",[98],[],[7664],[1058,28,29,7665,7034],"desktop PC used for all experiments",{"c":18,"m":7667,"d":20,"f":7668,"v":7669,"n":41,"y":122,"u":7670},"Intel i7-9700K",[98],[],[7671,7674],[3444,28,29,7672,7673],"desktop CPU with 64 GB RAM","Sec. VIII-C3",[1371,28,1372,7675,1374],"Liu et al. (FAST-LIO2, HBA) runtime platform",{"c":18,"m":7677,"d":20,"f":7678,"v":7679,"n":23,"y":100,"u":7680},"Intel i7-9750H",[98],[],[7681],[1553,28,29,7682,7683],"laptop CPU equivalent to the one on the robot; single-threaded timing","Sec. VII-A, Table V",{"c":18,"m":7685,"d":20,"f":7686,"v":7687,"n":23,"y":100,"u":7688},"intel i9 13900KF",[98],[],[7689],[7351,28,29,7690,917],"desktop CPU running SLAM",{"c":18,"m":7692,"d":20,"f":7693,"v":7694,"n":23,"y":100,"u":7695},"Intel i9 CPU + NVIDIA RTX 3090 Ti (Desktop)",[7406],[],[7696],[7410,28,29,7411,127],{"c":18,"m":7698,"d":20,"f":7699,"v":7700,"n":23,"y":122,"u":7701},"Intel i9-10900",[98],[],[7702],[3444,28,29,7703,7704],"mini-computer CPU with 64 GB RAM","Sec. VIII-A1",{"c":18,"m":7706,"d":20,"f":7707,"v":7708,"n":23,"y":100,"u":7709},"Intel i9-12900",[98],[],[7710],[1371,28,1372,7711,1374],"Peng et al. (DVI-SLAM) and Thien et al. (VR-SLAM) runtime platform",{"c":18,"m":7713,"d":20,"f":7714,"v":7715,"n":23,"y":1008,"u":7716},"Intel i9-12900H CPU with NVIDIA 3070 Ti",[3317],[],[7717],[2537,28,29,7718,917],"32 GB RAM, Ubuntu 20.04, CUDA 11.3, cuDNN 8.9.6, ONNX 1.16.3",{"c":18,"m":7720,"d":20,"f":7721,"v":7722,"n":28,"y":100,"u":7724},"Intel i9-13900K",[98],[7723],"Intel i9 13900K",[7725,7728,7730],[1553,28,29,7726,7727],"desktop-class CPU; single-threaded timing","Sec. VII-G3, Table V",[1520,28,29,7729,745],"desktop computer; all operations single-threaded",[1966,28,29,7731,478],"3.0 GHz; 128 GB memory; desktop computer",{"c":18,"m":7733,"d":20,"f":7734,"v":7735,"n":23,"y":233,"u":7736},"Intel i9-9900 CPU",[98],[],[7737],[5118,28,29,7738,7739],"3.10 GHz, 64 GB RAM","Sec. VII-G",{"c":18,"m":7741,"d":20,"f":7742,"v":7743,"n":952,"y":49,"u":7744},"Intel NUC",[98],[],[7745,7747,7749,7752],[1396,28,29,7746,3816],"onboard computer of the Hovermap running Wildcat",[1145,28,29,7748,3518],"Intel i7 processor, 32 GB RAM; KISS-SLAM ran faster than the sensor frame rate on board",[1562,23,1563,7750,7751],"i7 processor, 1 TB SSD, 64 GB DDR4; runs sensor drivers, timestamps messages and records ROS bags (data-recording PC)","Sec. III-A; Fig. 1",[3692,28,29,7753,1084],"onboard computer of the DS drone sensor suite; no runtime numbers reported on it",{"c":18,"m":7755,"d":20,"f":7756,"v":7757,"n":23,"y":233,"u":7758},"Intel NUC (onboard)",[98],[],[7759],[3742,23,29,7760,332],"saves LiDAR data on an SSD for offline evaluation",{"c":18,"m":7762,"d":20,"f":7763,"v":7764,"n":23,"y":233,"u":7765},"Intel NUC (payload computer on Spot)",[98],[],[7766],[2265,28,29,7767,863],"Intel Core i7 CPU (4 cores), 16 GB RAM, Windows 10; runs the real-time data and tracking component",{"c":18,"m":7769,"d":20,"f":7770,"v":7771,"n":23,"y":49,"u":7772},"Intel NUC (recording computer)",[98],[],[7773],[1247,23,1248,7774,1084],"modified to support dual Ethernet ports with hardware timestamping; Ouster LiDAR and Alphasense synchronized with the NUC by PTP (sub-microsecond accuracy)",{"c":18,"m":7776,"d":20,"f":7777,"v":7778,"n":23,"y":132,"u":7779},"Intel NUC 11 (mini-PC)",[98],[],[7780],[1904,28,29,7781,530],"onboard; coordinates sensors, processing and robot motion",{"c":18,"m":7783,"d":20,"f":7784,"v":7785,"n":23,"y":49,"u":7786},"Intel NUC 7i7DNBE",[98],[],[7787],[4290,28,29,7788,313],"4 x 1.9 GHz, 32 GB RAM; onboard SLAM on each robot",{"c":18,"m":7790,"d":20,"f":7791,"v":7792,"n":23,"y":233,"u":7793},"Intel NUC Board NUC7i7DNBE",[98],[],[7794],[715,28,29,7795,7796],"1.9 GHz CPU","Sec. III-C",{"c":18,"m":7798,"d":20,"f":7799,"v":7800,"n":23,"y":346,"u":7801},"Intel NUC Core i7-7567U",[98],[],[7802],[3264,28,29,7803,294],"on board; runs the proposed approach entirely in real time",{"c":18,"m":7805,"d":20,"f":7806,"v":7807,"n":23,"y":318,"u":7808},"Intel NUC Core i7-8650U",[98],[],[7809],[3237,28,29,7810,7811],"carried by the MAV; all calculations on board; 800% maximum load (8 hyper-threads)","Abstract; Sec. VIII-B",{"c":18,"m":7813,"d":20,"f":7814,"v":7815,"n":23,"y":49,"u":7816},"Intel NUC mini computer",[98],[],[7817],[1850,28,29,7818,1969],"onboard the AGV",{"c":18,"m":7820,"d":46,"f":7821,"v":7822,"n":23,"y":318,"u":7823},"Intel NUC mini computer (model not reported)",[98],[],[7824],[1183,28,29,7825,332],"C++ implementation integrated in ROS",{"c":18,"m":7827,"d":46,"f":7828,"v":7829,"n":23,"y":49,"u":7830},"Intel NUC with i7 processor (model not stated)",[98],[],[7831],[627,28,29,7832,530],"LION back-end used about 30% of one CPU core",{"c":18,"m":7834,"d":20,"f":7835,"v":7836,"n":23,"y":100,"u":7837},"Intel NUC with Intel Core i7-8559U",[98],[],[7838],[114,28,29,7839,7840],"robot computer, 4-core CPU","Sec. IV-D, Sec. V-A2",{"c":18,"m":7842,"d":20,"f":7843,"v":7844,"n":23,"y":49,"u":7845},"Intel NUC with Intel i5-10210U",[98],[],[7846],[5237,28,29,7847,332],"mini computer on the AGV and hand-held device",{"c":18,"m":7849,"d":20,"f":7850,"v":7851,"n":23,"y":318,"u":7852},"Intel NUC-i7 (NUC7i7BNH)",[98],[],[7853],[4412,28,29,7854,332],"onboard computer running pose estimation in real time",{"c":18,"m":7856,"d":20,"f":7857,"v":7858,"n":23,"y":132,"u":7859},"Intel NUC11TNKV7",[98],[],[7860],[1296,28,29,7861,7862],"Intel Core i7-1185G7, 32 GB RAM; used for both validation tests","Table 2; Table 4",{"c":18,"m":7864,"d":20,"f":7865,"v":7866,"n":23,"y":346,"u":7867},"Intel NUC5i7RYH",[98],[],[7868],[4101,28,29,7869,4103],"onboard computer of the FALCON robot",{"c":18,"m":7871,"d":20,"f":7872,"v":7873,"n":23,"y":49,"u":7874},"Intel NUC7i7DN",[98],[],[7875],[1359,28,29,7876,1361],"4 cores, 1.9 GHz, onboard Spot",{"c":1776,"m":7878,"d":20,"f":7879,"v":7880,"n":28,"y":346,"u":7882},"Intel RealSense D415",[98],[7881],"Intel RealSense D415 Depth Camera",[7883,7885,7889],[3264,53,29,7884,294],"coloured pointclouds integrated at 10 Hz; extrinsics to the tracked camera calibrated offline with Kalibr",[3237,53,7886,7887,7888],"indoor MAV industrial dataset of C-blox [26]","produces RGB-D data; indoor dataset processed with 5 cm voxels","Sec. VIII-B, VIII-B2",[2272,53,29,7890,7891],"RGB-D camera for inspection images (640 x 480 aspect ratio used)","Sec. 3.3.1; Sec. 3.6.2; Fig. 15",{"c":1776,"m":7893,"d":20,"f":7894,"v":7895,"n":952,"y":318,"u":7898},"Intel RealSense D435",[98],[7896,7897],"Intel Realsense D435","RealSense D435",[7899,7902,7904,7906],[688,53,1454,7900,7901],"30 Hz, 848 x 480 for VO; a second downward-facing D435 also mounted","Table 1; Sec. 7.5",[4290,53,29,7903,313],"RGB-D camera used for YOLO-based object detection and localization, not for SLAM",[1323,53,29,7905,398],"RGB-D images at 30 frames per second, 1280 x 720 px; 1652 frames (1225 for the baseline model, 427 for updating)",[1164,952,29,7907,7908],"mounted for comparison; no points beyond 10 m and sunlight noise outdoors","Sec. III-A; Sec. III-B",{"c":522,"m":7893,"d":20,"f":7910,"v":7911,"n":23,"y":49,"u":7912},[98],[7896],[7913],[2559,952,7914,7915,7916],"IN2LAAMA UTS datasets (lab, staircase, calibration)","RGB camera used only for the chained IMU-camera-lidar calibration baseline","Sec. VII-F",{"c":1776,"m":7918,"d":20,"f":7919,"v":7921,"n":28,"y":233,"u":7925},"Intel RealSense D435I",[7920],"Intel (Santa Clara, CA, USA)",[7922,7923,7924],"Intel RealSense D435i","RealSense D435i","RealSense D435i RGBD Camera",[7926,7929,7931],[5348,23,5349,7927,7928],"RGB 1920 x 1080, IR 1280 x 720; up to 90 fps; depth FoV 85.2° x 58°; depth range 0.105 m to 10 m+; accuracy ±2% at 2 m","Sec. 7.2.1; Table 4",[2775,53,2776,7930,2777],"forward-facing",[1890,53,29,7932,7933],"depth quality described as slightly worse than Azure Kinect; two self-captured indoor room sequences, qualitative only","Supp. 2.2; Supp. Figs. 12 and 13",{"c":1689,"m":7918,"d":20,"f":7935,"v":7936,"n":41,"y":318,"u":7937},[98],[7922,7923],[7938,7941],[896,23,815,7939,7940],"stereo infrared global shutter 848x480 at 30 Hz; software time synchronization; firmware 0.5.10.13.00, librealsense 2.32.1.0","Sec. III, Table II, Sec. VI-D",[1462,53,29,7942,7943],"gray stereo, 30 Hz, 848 px x 480 px, diagonal FoV 100.6 deg; software-synchronized (SMR, FSC)","Table I, Sec. V-F",{"c":522,"m":7918,"d":20,"f":7945,"v":7946,"n":41,"y":49,"u":7947},[98],[7922],[7948,7951],[1296,53,29,7949,7950],"RGB up to 1920 x 1080, FoV 69 x 42 deg; recorded at 15 Hz, 640 x 480; used to detect AprilTags","Table 4; Sec. 4.5",[7952,53,29,7953,7954],"yuan2021lidarcameracalib","written 'Intel Realsense-D435i'; intrinsics and distortion calibrated beforehand","Fig. 10; Sec. IV",{"c":1776,"m":7956,"d":20,"f":7957,"v":7958,"n":952,"y":100,"u":7962},"Intel RealSense D455",[98],[7959,7960,7961],"Intel RealSense D455 (written 'D45' in Sec. 4.1 text)","Intel Realsense d455","RealSense D455",[7963,7966,7970,7972],[2352,53,29,7964,7965],"RGB 848 x 480 and depth 848 x 480 at 15 FPS; RGB and IMU feed VINS, RGB-D feeds surfel mapping","Sec. 4.1, Table 3, Fig. 11, ref. [84]",[5348,23,7967,7968,7969],"Luleå SubT tunnel dataset (Koval et al. 2022)","RGB-D camera; part of sensor setup 3, used for the vision-based methods (text names the maker only for the T265 and, in Table 4, for the D435i)","Sec. 7.1.1",[7185,53,29,7971,917],"self-captured real-world sequences for qualitative monocular results",[2537,23,2767,7973,917],"RGB-D camera on each robot",{"c":1689,"m":7956,"d":20,"f":7975,"v":7976,"n":23,"y":100,"u":7978},[98],[7977],"Intel Realsense D455",[7979],[4121,53,29,7980,7981],"active stereo camera","Sec. VI-B; Table III",{"c":1776,"m":7983,"d":20,"f":7984,"v":7985,"n":23,"y":100,"u":7986},"Intel RealSense Depth Camera D455",[98],[],[7987],[7410,53,29,7988,4103],"handheld; real-time reconstruction on an embedded GPU",{"c":108,"m":7990,"d":20,"f":7991,"v":7992,"n":952,"y":49,"u":7996},"Intel RealSense L515",[98],[7993,7994,7995],"Intel Realsense L515","RealSense L515","Realsense L515",[7997,8000,8002,8005],[2054,53,29,7998,7999],"solid-state; 23,000,000 points\u002Fs and 9 m to 25 m as written; FoV 70 x 55 deg; indoor only","Table I (Our Device I); Sec. IV-B",[4121,53,29,8001,7981],"described as solid-state LiDAR",[5237,53,29,8003,8004],"solid-state, 30 Hz, 70 x 55 deg FoV, 0.07 deg horizontal and vertical resolution, 0.25-9 m range, 1.4 cm accuracy, 61 x 26 mm, 95 g","Table I; Sec. IV-A",[5386,53,29,8006,8007],"solid-state, 30 Hz, repetitive scan, FoV 70°×55° (Table I); written as 'Intel L515' in Sec. IV-B; point colors in the supplementary warehouse map are provided by the L515 (Supp. Fig. 1 caption)","Table I; Sec. IV-B; Supp. Fig. 1",{"c":1776,"m":7990,"d":20,"f":8009,"v":8010,"n":41,"y":122,"u":8011},[98],[],[8012,8016],[8013,53,29,8014,8015],"hu2023robotassisted","solid-state LiDAR capturing RGB-D images; HFOV 70 deg, VFOV 50 deg, angular resolution 0.07 deg; mounted on the robot's back","Sec. 3.1.1, 4.1",[2325,23,2326,8017,8018],"solid-state RGB-D, built-in IMU, FoV 70 x 55 deg, depth up to 9 m, 30 Hz; depth factor 0.001; RGB and depth aligned; mounted on a tripod","Sec. 2.3, Table 2, Sec. 2.4 Eq. 1, Fig. 4a",{"c":522,"m":7990,"d":20,"f":8020,"v":8021,"n":23,"y":100,"u":8023},[],[8022],"RealSense L515 (RGB camera)",[8024],[2054,53,29,8025,2128],"rolling shutter, 1920 x 1080, FoV 70 x 43 deg",{"c":1776,"m":8027,"d":20,"f":8028,"v":8029,"n":41,"y":346,"u":8031},"Intel RealSense R200",[98],[8030],"Intel Realsense r200",[8032,8035],[3418,53,29,8033,8034],"full HD colour sensor, IR laser projector and two IR sensors; mounted facing down; 640x480 at 15 Hz; best sensing range 0.5 to 5 m; outdoor exposure, gain and emitter settings in Table 1","System architecture; Experimental setup; Table 1",[1183,952,29,8036,1157],"on the same AGV; the DBoW2 baseline used a front-mounted camera (that this is the R200 is an inference)",{"c":522,"m":8038,"d":20,"f":8039,"v":8040,"n":23,"y":132,"u":8041},"Intel RealSense T265",[98],[],[8042],[5348,23,7967,8043,7969],"described as an RGB camera",{"c":1689,"m":8038,"d":20,"f":8045,"v":8046,"n":23,"y":132,"u":8048},[98],[8047],"RealSense T265",[8049],[225,53,854,8050,228],"two 30 Hz cameras and a 200 Hz IMU",{"c":18,"m":8052,"d":20,"f":8053,"v":8054,"n":23,"y":71,"u":8055},"Intel T7200",[98],[],[8056],[2564,28,29,8057,478],"single core, 2 GHz; processing done off-line on recorded data",{"c":33,"m":8059,"d":20,"f":8060,"v":8062,"n":23,"y":1008,"u":8063},"Intel Wi-Fi 6E AX210 adapter, Ubiquiti UniFI U7 Pro access point, Mikrotik RouterBOARD hEX S router",[8061],"Intel; Ubiquiti; MikroTik",[],[8064],[2272,53,29,8065,8066],"Wi-Fi 6 link between onboard and off-board computers; bandwidth limited real-world logging","Sec. 3.3.1; Sec. 3.7.3",{"c":18,"m":8068,"d":20,"f":8069,"v":8070,"n":23,"y":122,"u":8071},"Intel Xeon CPU (2.1 GHz)",[98],[],[8072],[803,28,29,8073,7002],"used for the voxel-size, processing-time and memory study",{"c":18,"m":8075,"d":20,"f":8076,"v":8077,"n":23,"y":1819,"u":8078},"Intel Xeon CPU at 2.4GHz",[98],[],[8079],[8080,28,29,8081,313],"cnnslam2017","desktop PC with 16GB of RAM",{"c":18,"m":8083,"d":20,"f":8084,"v":8085,"n":23,"y":132,"u":8086},"Intel Xeon CPU E5-2698 v4",[98],[],[8087],[1588,28,29,8088,127],"16 threads",{"c":18,"m":8090,"d":20,"f":8091,"v":8092,"n":23,"y":318,"u":8093},"Intel Xeon E3-1505M v6 @ 3.00GHz",[98],[],[8094],[1071,28,29,8095,1072],"single-threaded execution",{"c":18,"m":8097,"d":20,"f":8098,"v":8099,"n":23,"y":1421,"u":8100},"Intel Xeon E5-1650",[98],[],[8101],[2638,28,29,8102,8103],"3.2 GHz, in a workstation used for all timing; up to 4 background threads for loop-closure scan matching on the Deutsches Museum data","Sec. VI.A, VI.C",{"c":18,"m":8105,"d":20,"f":8106,"v":8107,"n":23,"y":233,"u":8108},"Intel Xeon Gold 5128",[98],[],[8109],[1340,28,29,8110,1342],"16 cores, 2.3 GHz desktop ('iVox Intel'); Fig. 1 caption gives Xeon Gold 5218",{"c":18,"m":8112,"d":20,"f":8113,"v":8114,"n":23,"y":299,"u":8115},"Intel Xeon L5335",[98],[],[8116],[302,28,29,8117,6832],"real-time tracking with about 1,700 points",{"c":18,"m":8119,"d":20,"f":8120,"v":8121,"n":23,"y":233,"u":8122},"Intel Xeon W-10885M",[98],[],[8123],[668,28,29,8124,3280],"laptop CPU",{"c":18,"m":8126,"d":20,"f":8127,"v":8128,"n":41,"y":49,"u":8129},"Intel Xeon W-2145",[98],[],[8130,8133],[8131,28,29,8132,478],"vizzo2021puma","8 cores at 3.70 GHz, 32 GB RAM; algorithm runs entirely on CPU",[517,28,29,8134,441],"8 cores at 3.70 GHz, 32 GB RAM, GNU\u002FLinux 64-bit, GCC 9.3.0; every method run without multithreading",{"c":18,"m":8136,"d":20,"f":8137,"v":8138,"n":23,"y":100,"u":8139},"Intel Xeon(R) E3-1240v5",[98],[],[8140],[1371,28,1372,8141,1374],"Weitong et al. (FAST-LIO, pose graph) runtime platform",{"c":18,"m":8143,"d":20,"f":8144,"v":8145,"n":23,"y":152,"u":8146},"Intel Xeon(R) W-2123",[98],[],[8147],[4572,28,29,8148,478],"8 cores @3.60 GHz, 16 GB RAM",{"c":18,"m":8150,"d":20,"f":8151,"v":8152,"n":23,"y":100,"u":8153},"Intel(R) Core(TM) i9-12900H CPU",[98],[],[8154],[8155,28,29,8156,313],"zhang2024globalbimreg","all experiments",{"c":662,"m":8158,"d":20,"f":8159,"v":8160,"n":23,"y":132,"u":8161},"internal 6-axis IMU of the Ouster OS1-128",[180],[],[8162],[537,53,29,8163,8164],"6-axis, 100 Hz","Hardware Setup",{"c":662,"m":8166,"d":20,"f":8167,"v":8168,"n":23,"y":122,"u":8169},"internal IMU of the Livox Horizon",[3801],[],[8170],[3761,952,29,569,3763],{"c":662,"m":8172,"d":20,"f":8173,"v":8174,"n":23,"y":100,"u":8175},"internal IMU of the Livox LiDAR",[],[],[8176],[211,23,2415,569,214],{"c":662,"m":8178,"d":20,"f":8179,"v":8180,"n":23,"y":100,"u":8181},"internal IMU of the OS1 LiDAR",[],[],[8182],[211,23,212,8183,8184],"LiDAR-IMU extrinsics treated as exact","Sec. III-A, Sec. VI",{"c":662,"m":8186,"d":20,"f":8187,"v":8188,"n":41,"y":233,"u":8189},"InvenSense ICM-20948",[2372],[],[8190,8192],[2135,23,2136,8191,2138],"built-in IMU of the Ouster OS1-64, 100 Hz",[1038,23,1039,8193,1041],"IMU embedded in the Ouster LiDAR, 100 Hz, timestamped by the Ouster clock",{"c":662,"m":8195,"d":20,"f":8196,"v":8197,"n":23,"y":346,"u":8198},"InvenSense MP67B",[2372],[],[8199],[1078,53,29,8200,8201],"built-in six-axis gyroscope and accelerometer of the iPhone, 100 Hz","Sec. IX-C-2",{"c":662,"m":8203,"d":20,"f":8204,"v":8205,"n":23,"y":122,"u":8206},"InvenSense MPU-6050",[2372],[],[8207],[2995,53,2996,8208,8209],"6-axis, about 10 USD, mounted about 0.1 m below the LiDAR","Sec. IV-B-2",{"c":522,"m":8211,"d":46,"f":8212,"v":8214,"n":23,"y":122,"u":8215},"iOS handheld device camera and motion sensors via Apple ARKit (device model not reported)",[8213],"Apple (ARKit platform; device model not reported)",[],[8216],[3733,53,29,569,8217],"Sec. 2, 4, 8",{"c":372,"m":8219,"d":46,"f":8220,"v":8221,"n":23,"y":1819,"u":8222},"iPad Air",[],[],[8223],[1822,53,8224,8225,3364],"BundleFusion captured sequences","Structure Sensor mounted on it; receives live visual feedback; data sent over wireless network with zlib depth and jpeg colour compression",{"c":372,"m":8227,"d":20,"f":8228,"v":8229,"n":23,"y":1819,"u":8230},"iPad Air2 (128 GB)",[1644],[],[8231],[8232,23,8233,8234,8235],"dai2017scannet","ScanNet","handheld capture device running the custom iOS scanning app; several hours of RGB-D video per device, battery life was the bottleneck","Sec. 3.1, Fig. 14",{"c":662,"m":8237,"d":20,"f":8238,"v":8239,"n":23,"y":1819,"u":8240},"iPad Air2 built-in IMU (via Apple SDK)",[1644],[],[8241],[8232,23,8233,8242,8243],"acceleration and angular velocity with timestamps; used for gravity up-vector prior","Sec. 3.1 'Storage', Sec. 3.2 'Orientation'",{"c":522,"m":8245,"d":20,"f":8246,"v":8247,"n":23,"y":1819,"u":8248},"iPad Air2 RGB camera",[1644],[],[8249],[8232,23,8233,8250,8251],"colour 1296 x 968 at 30 Hz, auto white balance and dynamic exposure (average exposure about 30 ms), H.264 at 15 Mbps","Sec. 3.1, App. C.1",{"c":1776,"m":8253,"d":46,"f":8254,"v":8255,"n":23,"y":233,"u":8256},"iPad Pro (2020)",[1644],[],[8257],[8258,53,29,8259,8260],"voxfusion2022","iOS device with range sensor used for RGB-D capture","Sec. 5.1, Sec. 5.3",{"c":1776,"m":8262,"d":46,"f":8263,"v":8264,"n":23,"y":100,"u":8265},"iPhone (commodity camera and time-of-flight sensor)",[],[],[8266],[3701,53,29,8267,8268],"Qualitative online reconstructions shown on the project website only; no quantitative evaluation","Supplementary S1",{"c":944,"m":8270,"d":46,"f":8271,"v":8272,"n":23,"y":346,"u":8273},"iPhone (VINS-Mobile)",[],[],[8274],[1078,53,29,8275,8201],"30 Hz images at 640x480; arXiv v1 names an iPhone7 Plus",{"c":522,"m":8277,"d":20,"f":8278,"v":8279,"n":23,"y":132,"u":8280},"iPhone 12 Pro",[],[],[8281],[4679,952,29,8282,8283],"124 images at 3024 x 4032 px, overlap above 70%; the phone's built-in LiDAR is mentioned only as motivation, images alone are used","Sec. 3.2.1, Sec. 3.2.2(b)",{"c":1776,"m":8285,"d":20,"f":8286,"v":8287,"n":23,"y":233,"u":8288},"iPhone 13 Pro",[1644],[],[8289],[8258,53,29,8290,8291],"RGB images with depth from the onboard lidar sensor; very low depth resolution","Sec. 5.1, Sec. 5.3, Fig. 8",{"c":522,"m":8293,"d":20,"f":8294,"v":8295,"n":23,"y":132,"u":8296},"iPhone 15 Pro",[],[],[8297],[2706,53,29,8298,3571],"smartphone video 1080 x 1920 pixels; handheld at about 1.6 m in scene_1",{"c":944,"m":8300,"d":20,"f":8301,"v":8302,"n":23,"y":132,"u":8303},"iPhone 15 Pro flash LiDAR with Pix4D Capture app (analysed in Pix4D Catch)",[],[],[8304],[2706,952,29,8305,8306],"narrow vertical field of view; commercial SLAM visualization","Sec. 4.1; Sec. 4.2.2; Fig. 19",{"c":522,"m":8308,"d":20,"f":8309,"v":8310,"n":23,"y":100,"u":8311},"iPhone 7",[],[],[8312],[5177,23,8313,8314,3230],"InLoc","329 InLoc query images",{"c":1776,"m":8316,"d":46,"f":8317,"v":8318,"n":23,"y":152,"u":8319},"IR structured-light depth (IR projector plus IR camera on the rig; model not reported)",[],[],[8320],[3089,23,3054,8321,898],"raw depth computed from the IR video stream given the projected structured-light pattern",{"c":944,"m":8323,"d":20,"f":8324,"v":8326,"n":23,"y":318,"u":8327},"IR100",[8325],"Indoor Reality",[],[8328],[951,952,29,8329,8330],"subscription for maps from a mobile phone only (iPhone 7 and up or Android); no additional sensors; accuracy estimated at about one foot; weight and operating time N\u002FA","Sec. 2.1.3; Tables 1-2, 6",{"c":944,"m":8332,"d":20,"f":8333,"v":8334,"n":23,"y":318,"u":8335},"IR1000",[8325],[],[8336],[951,952,29,8337,8338],"backpack with several 2D LiDARs and an IMU; 18.5 kg; 200,000 pts\u002Fs; relative accuracy 3-5 cm; 3 h; 5 cameras, 360° x 360° FoV","Sec. 2.2.1; Tables 1-6; Fig. 4a",{"c":944,"m":8340,"d":20,"f":8341,"v":8342,"n":23,"y":318,"u":8343},"IR1000T",[8325],[],[8344],[951,952,29,8345,8346],"IR1000 plus a thermographic camera; 18.5 kg; 200,000 pts\u002Fs; relative accuracy 3-5 cm; 3 h","Sec. 2.2.1; Tables 1-6",{"c":944,"m":8348,"d":20,"f":8349,"v":8350,"n":23,"y":318,"u":8351},"IR200",[8325],[],[8352],[951,952,29,8353,8354],"mobile phone plus a 360° x 360° RGB camera and mount; accuracy about one foot; 2 h","Sec. 2.1.3; Tables 1-2, 6; Fig. 3",{"c":944,"m":8356,"d":20,"f":8357,"v":8358,"n":23,"y":318,"u":8359},"IR500",[8325],[],[8360],[951,952,29,8361,8362],"handheld tablet with external RGB-D sensor; 0.9 kg; 190,000 pts\u002Fs; accuracy \u003C1% of distance; 2 h","Sec. 2.1.2; Tables 1-3, 6; Fig. 2a",{"c":944,"m":8364,"d":20,"f":8365,"v":8366,"n":23,"y":318,"u":8367},"IR500T",[8325],[],[8368],[951,952,29,8369,8370],"IR500 plus a thermographic camera; 0.9 kg; 190,000 pts\u002Fs; accuracy \u003C1% of distance; 2 h","Sec. 2.1.2; Tables 1-3, 6",{"c":372,"m":8372,"d":20,"f":8373,"v":8374,"n":23,"y":270,"u":8375},"Irma3D (Volksbot RT-3 chassis)",[],[],[8376],[2407,53,29,8377,8378],"mobile robot carrying scanner and cameras","Sec. 3.1; Fig. 2",{"c":372,"m":8380,"d":46,"f":8381,"v":8383,"n":23,"y":61,"u":8384},"iRobot ATRV Jr.",[8382],"iRobot",[],[8385],[311,53,29,8386,917],"skid steering; shaft encoders unreliable when turning",{"c":372,"m":8388,"d":46,"f":8389,"v":8390,"n":23,"y":252,"u":8391},"iRobot ATRV-Mini",[8382],[],[8392],[255,53,29,8393,4309],"mobile robot carrying the camera rig",{"c":372,"m":8395,"d":20,"f":8396,"v":8397,"n":23,"y":71,"u":8398},"iRobot B21r",[8382],[],[8399],[981,53,29,982,983],{"c":372,"m":8401,"d":46,"f":8402,"v":8403,"n":23,"y":318,"u":8404},"iRobot PackBot",[8382],[],[8405],[321,23,322,8406,8407],"tracked robot chosen for railroad tracks at the Edgar mine; teleoperated with an Xbox 360 controller","Sec. II-A, Fig. 2",{"c":372,"m":8409,"d":46,"f":8410,"v":8411,"n":23,"y":49,"u":8412},"iRobot PackBot Explorer (tracked robot)",[8382],[],[8413],[1536,23,8414,8415,469],"DARPA SubT Challenge Urban Circuit","tracked robot capable of fast in-spot yaw rotations",{"c":662,"m":8417,"d":46,"f":8418,"v":8419,"n":23,"y":299,"u":8420},"ISIS IMU",[],[],[8421],[2604,23,8422,569,5160],"5.5 km urban vehicle dataset used to generate simulations",{"c":662,"m":8424,"d":46,"f":8425,"v":8427,"n":23,"y":299,"u":8428},"IXSEA LANDINS",[8426],"IXSEA",[],[8429],[1606,952,29,8430,530],"fiber-optic based inertial navigation system",{"c":662,"m":8432,"d":46,"f":8433,"v":8434,"n":23,"y":152,"u":8435},"Jackal on-board IMU (model not reported)",[],[],[8436],[414,53,29,569,416],{"c":353,"m":8438,"d":46,"f":8439,"v":8440,"n":23,"y":152,"u":8441},"Jackal wheel odometer",[2712],[],[8442],[414,53,29,569,416],{"c":18,"m":8444,"d":20,"f":8445,"v":8446,"n":23,"y":122,"u":8447},"Jetson NX",[],[],[8448],[8013,28,29,8449,8450],"embedded in the legged robot; LiDAR connected to it for scanning and mapping","Sec. 3.1.1, Fig. 5",{"c":651,"m":8452,"d":20,"f":8453,"v":8454,"n":23,"y":49,"u":8455},"JingLing-K50 RTK-GPS",[],[],[8456],[8457,41,8458,8459,8460],"clins2021","YQ (authors' campus sequences)","RTK-GPS; shown as the 'GPS' reference trajectory in Fig. 6","Sec. V-B; Fig. 6",{"c":372,"m":8462,"d":46,"f":8463,"v":8465,"n":23,"y":142,"u":8466},"John Deere Gator TE electric vehicle",[8464],"John Deere",[],[8467],[467,53,29,8468,8469],"automated electric vehicle carrying the dual-spring vehicle-mounted Zebedee; driven off-road to provide inputs for spring system identification; no vehicle SLAM results are reported","Sec. II-A; Fig. 1(b)",{"c":353,"m":8471,"d":46,"f":8472,"v":8473,"n":23,"y":100,"u":8474},"joint encoders",[],[],[8475],[1553,53,29,8476,2153],"used with the IMU by ANYmal's leg odometry module (ref. [69]) to provide the registration prior",{"c":944,"m":8478,"d":46,"f":8479,"v":8481,"n":23,"y":49,"u":8482},"Kaarta Stencil",[8480],"Kaarta",[],[8483],[8484,53,29,8485,85],"interactiveslam2021","commercial visual-LIDAR-IMU mapping system; its own trajectory is shown as a baseline (Fig. 7a)",{"c":944,"m":8487,"d":20,"f":8488,"v":8489,"n":23,"y":233,"u":8490},"Kaarta Stencil 2",[8480],[],[8491],[3818,53,29,8492,2530],"Velodyne VLP-16, MEMS IMU and a gray-scale camera (not activated); LOAM-based SLAM; nominal 300,000 pts\u002Fs single return; 360 deg horizontal FOV",{"c":18,"m":8494,"d":20,"f":8495,"v":8497,"n":23,"y":233,"u":8498},"Khadas VIM3",[8496],"Khadas",[],[8499],[378,28,29,8500,167],"2.2 GHz quad-core Cortex-A73 CPU, 4 GB RAM",{"c":18,"m":8502,"d":20,"f":8503,"v":8504,"n":23,"y":122,"u":8505},"Khadas VIM3 Pro",[8496],[],[8506],[2169,28,29,8507,275],"2.2 GHz quad-core Cortex-A73, 4 GB RAM; runs Point-LIO onboard in real time",{"c":18,"m":8509,"d":20,"f":8510,"v":8511,"n":23,"y":100,"u":8512},"Khadas VIM4",[8496],[],[8513],[1263,28,29,8514,8515],"2.2 GHz quad-core Cortex-A73 plus 2.0 GHz quad-core Cortex-A53; one A73 core used; median 54 ms","Sec. IV-F",{"c":1776,"m":8517,"d":20,"f":8518,"v":8520,"n":8521,"y":37,"u":8522},"Kinect",[1197,8519],"Microsoft (for NYU Depth v2); not named for TUM",[],8,[8523,8525,8528,8531,8532,8535,8539,8542,8545],[1822,23,991,8524,2400],"hand-held Kinect sequences",[1822,23,8526,8527,257],"NYU2","all 464 scenes reconstructed qualitatively",[3245,23,8529,8530,3518],"Freiburg (TUM) RGB-D benchmark","sensor loops around a central desk in a roughly 12 by 12 m room; the paper writes only 'Kinect', manufacturer inferred from the product name",[1783,53,29,1784,717],[2821,53,29,8533,8534],"structured-light depth sensor; on-board ASIC produces an 11-bit 640x480 depth map at 30 Hz; conservative range about 0.4 to 8 m used for raycasting; only depth used","Sec. 2.1; Sec. 3.4",[8536,23,8537,8538,1176],"rusinkiewicz2019symmetric","TUM RGB-D (freiburg1_xyz, timestamps 1305031104.030279 and 1305031108.503548)","not_reported (paper notes warp and scanning noise from the Kinect sensor)",[8080,23,8540,8541,313],"TUM RGB-D; NYU Depth v2","TUM RGB-D is 'acquired with a Kinect sensor'; NYU Depth v2 ground truth from a Microsoft Kinect camera",[1674,53,29,8543,8544],"RGB and depth 640 x 480; openni_launch driver with depth registration to RGB","Sec. 3, Sec. 4.1.1, Sec. 4.2",[7375,23,8546,569,8547],"bowl and loom point sets collected by the authors","Sec. 6.3",{"c":1776,"m":8549,"d":20,"f":8550,"v":8551,"n":23,"y":299,"u":8552},"Kinect for Windows",[1197],[],[8553],[1786,53,29,8554,8555],"RGB-D data at 30 Hz; depth range assumed up to 8 m for streaming","Sec. 8, Sec. 9",{"c":1776,"m":8557,"d":20,"f":8558,"v":8559,"n":23,"y":299,"u":8560},"Kinect RGB-D sensor",[],[],[8561],[302,53,29,8562,398],"160x120 depth image, points beyond 7 m removed, tracking at 30 Hz",{"c":1776,"m":8564,"d":20,"f":8565,"v":8566,"n":28,"y":152,"u":8567},"Kinect v1",[1197],[],[8568,8570,8573],[2555,23,991,8569,147],"hand-held; RGB and depth at 30 Hz, synchronized with the dataset tool",[1795,23,991,8571,8572],"sensor of the TUM RGB-D benchmark; rolling shutter and unsynchronised depth and colour; its shutter times (about 30.5 ms depth, 26.1 ms colour, from [52]) used to render synthetic rolling-shutter variants; visible depth distortion pattern in BAD SLAM self-calibration","Sec. 5 Impact of distortions; Supp. Fig. 9",[8574,23,991,8575,8576],"surfelmeshing2020","640x480 images; depth beyond 3 m dropped in preprocessing","Sec. 4; Sec. 5.3",{"c":108,"m":8578,"d":20,"f":8579,"v":8580,"n":23,"y":270,"u":8581},"KITTI 360 deg Velodyne laser scanner",[221],[],[8582],[3063,53,578,8583,4496],"10 Hz logging",{"c":522,"m":8585,"d":46,"f":8586,"v":8587,"n":23,"y":346,"u":8588},"KITTI camera (model not named in the paper)",[],[],[8589],[492,23,578,8590,214],"grayscale and color images provided by KITTI; grayscale used for tracking",{"c":522,"m":8592,"d":46,"f":8593,"v":8594,"n":23,"y":289,"u":8595},"KITTI camera (model not stated)",[],[],[8596],[2595,23,578,8597,8598],"10 fps, 1241x376","Sec. V-A, VIII-E",{"c":522,"m":8600,"d":46,"f":8601,"v":8602,"n":23,"y":49,"u":8603},"KITTI cameras (model not stated)",[],[],[8604],[1850,23,832,8605,469],"not used by F-LOAM",{"c":1689,"m":8607,"d":46,"f":8608,"v":8609,"n":23,"y":122,"u":8610},"KITTI color and monochrome stereo cameras (model not named; only the left image is used)",[],[],[8611],[4348,23,578,8612,2153],"10 Hz; SDV-LOAM takes the LiDAR point cloud and the left image of the stereo camera as input",{"c":1689,"m":8614,"d":46,"f":8615,"v":8616,"n":23,"y":1819,"u":8617},"KITTI color and monochrome stereo cameras (model not stated)",[],[],[8618],[5093,23,578,4434,8619],"Sec. 7.5, Fig. 20",{"c":651,"m":8621,"d":46,"f":8622,"v":8623,"n":23,"y":49,"u":8624},"KITTI GNSS-INS ground truth (model not stated)",[],[],[8625],[1976,41,578,8626,332],"ground truth poses for seq. 00-10",{"c":651,"m":8628,"d":46,"f":8629,"v":8630,"n":23,"y":233,"u":8631},"KITTI GPS (model not named in the paper)",[],[],[8632],[585,41,8633,8634,588],"KITTI raw city sequence 05","raw GPS used as ground truth for the short KITTI raw city sequence 05 and as low-weight constraints in the GPS variant",{"c":651,"m":8636,"d":46,"f":8637,"v":8638,"n":23,"y":49,"u":8639},"KITTI GPS (model not stated)",[],[],[8640],[1850,23,832,8605,469],{"c":651,"m":8642,"d":46,"f":8643,"v":8644,"n":23,"y":346,"u":8645},"KITTI GPS-based inertial navigation system ground truth (model not stated)",[],[],[8646],[7578,41,578,8647,8648],"authors observed height inconsistencies in the training ground truth","Sec. IV, Fig. 6",{"c":651,"m":8650,"d":46,"f":8651,"v":8652,"n":41,"y":270,"u":8654},"KITTI high accuracy GPS\u002FINS",[],[8653],"KITTI high-accuracy GPS\u002FINS",[8655,8657],[4348,41,578,8656,4496],"ground truth for KITTI",[3063,41,578,8656,4496],{"c":651,"m":8659,"d":46,"f":8660,"v":8661,"n":23,"y":1819,"u":8662},"KITTI high accuracy GPS\u002FINS (model not stated)",[],[],[8663],[5093,41,578,8664,8665],"ground truth for sequences 0-10","Sec. 7.5",{"c":662,"m":8667,"d":46,"f":8668,"v":8669,"n":23,"y":233,"u":8670},"KITTI IMU (model not named in the paper)",[],[],[8671],[585,23,586,8672,898],"about 10 Hz in the synchronized data, 100 Hz in the unsynchronized data used for LIO-SAM",{"c":522,"m":8674,"d":46,"f":8675,"v":8676,"n":23,"y":270,"u":8677},"KITTI left monochrome camera",[],[],[8678],[3063,53,578,8583,4496],{"c":108,"m":8680,"d":46,"f":8681,"v":8682,"n":41,"y":346,"u":8683},"KITTI LIDAR (model not named in the paper)",[],[],[8684,8686],[7384,23,578,569,8685],"Sec. 6.1, Fig. 6",[492,23,578,8687,214],"LIDAR point clouds with calibration provided by KITTI; used only for feature depth",{"c":1689,"m":8689,"d":46,"f":8690,"v":8691,"n":23,"y":152,"u":8692},"KITTI Odometry stereo camera (model not reported)",[],[],[8693],[2936,23,832,8694,147],"11 sequences with ground truth; larger images than EuRoC",{"c":1689,"m":8696,"d":46,"f":8697,"v":8698,"n":23,"y":1819,"u":8699},"KITTI stereo camera (model not stated)",[],[],[8700],[1783,23,578,8701,332],"baseline about 54 cm, 10 Hz, 1240x376 after rectification",{"c":1689,"m":8703,"d":46,"f":8704,"v":8705,"n":23,"y":152,"u":8706},"KITTI stereo cameras (model not reported in the paper)",[],[],[8707],[5455,23,578,8708,6131],"depth maps from PSMNet stereo matching; left images also used for monocular depth prediction",{"c":372,"m":8710,"d":20,"f":8711,"v":8712,"n":23,"y":455,"u":8713},"Kurt3D",[],[],[8714],[8715,53,8716,8717,8718],"magnusson2009icpndt","Kvarntorp data sets A and B","robot scanning underground in drive-scan-and-go fashion","Sec. IV-A; Fig. 3",{"c":372,"m":8720,"d":20,"f":8721,"v":8722,"n":23,"y":455,"u":8723},"Kurt3D (Osnabrück University)",[],[],[8724],[1000,53,8725,8726,8727],"Mission-4, Mission-4-1, collaborative ICP comparison scan pair","controlled speed up to 4 m\u002Fs; two digital colour cameras","Sec. 4.3, 6.4.2, 6.4.3, 8.2.1",{"c":372,"m":8729,"d":20,"f":8730,"v":8731,"n":23,"y":71,"u":8732},"Kurt3D (outdoor version)",[],[],[8733],[547,53,29,8734,549],"45 cm x 33 cm x 29 cm, 22.6 kg; two 90 W motors driving six skid-steered wheels; 16-bit CMOS microcontroller for motor control",{"c":353,"m":8736,"d":20,"f":8737,"v":8738,"n":23,"y":455,"u":8739},"Kurt3D odometry",[],[],[8740],[8715,53,8741,8742,8743],"Kvarntorp data set B","initial pose estimates for data set B; manually altered for scan 33 (ICP) and scan 23 (NDT)","Sec. III-B; Sec. IV-B; Sec. IV-D-2",{"c":353,"m":8745,"d":20,"f":8746,"v":8747,"n":23,"y":71,"u":8748},"Kurt3D wheel odometer",[],[],[8749],[547,53,29,8750,530],"planar odometry extrapolated to 6 DoF as initial guess only",{"c":662,"m":8752,"d":20,"f":8753,"v":8755,"n":23,"y":318,"u":8756},"KVH 1750",[8754],"KVH",[],[8757],[688,53,8758,8759,691],"Atlas (DRC Finals)","333 Hz; initial bias 0.5 deg\u002Fh and 0.5 mg; bias stability 0.05 deg\u002Fh and 0.05 mg",{"c":662,"m":8761,"d":20,"f":8762,"v":8763,"n":23,"y":318,"u":8764},"KVH 1775",[8754],[],[8765],[688,53,1111,8766,691],"1000 Hz; initial bias 0.5 deg\u002Fh and 0.5 mg; bias stability 0.05 deg\u002Fh and 0.05 mg",{"c":372,"m":8768,"d":46,"f":8769,"v":8770,"n":23,"y":233,"u":8771},"Kylin backpack",[],[],[8772],[4511,53,8773,8774,639],"Kylin backpack sequences K1 and K2","walked at about 1 m\u002Fs; no GPS device",{"c":1776,"m":8776,"d":20,"f":8777,"v":8778,"n":23,"y":100,"u":8779},"L515 (printed 'Inter L515')",[],[],[8780],[1371,23,1372,8781,8782],"optional depth camera, 30 Hz, 1024 x 760 pixels","Fig. 2",{"c":33,"m":8784,"d":20,"f":8785,"v":8786,"n":23,"y":233,"u":8787},"laboratory targets: 24 MDF colour boards (300 x 300 mm, smooth and 40-grit rough) and cast concrete targets (dolomite and granite mixes, three roughness levels)",[],[],[8788],[3821,41,29,8789,8790],"Spraymate spray paint, 12 colour and sheen variants; targets on a rotating bracket at 0, 15, 30, 45 deg","Sec. 3.1, 3.2, 4.2",{"c":108,"m":8792,"d":20,"f":8793,"v":8795,"n":23,"y":1411,"u":8796},"Ladar 2D IBEO Lasertechnik",[8794],"IBEO Lasertechnik",[],[8797],[1414,23,1415,8798,1416],"2D laser range sensor, maximum viewing angle 220 degrees",{"c":522,"m":8800,"d":46,"f":8801,"v":8803,"n":23,"y":299,"u":8804},"Ladybug spherical camera (model not_reported)",[8802],"Point Grey",[],[8805],[4141,952,29,8806,1712],"spherical camera on the i-MMS",{"c":522,"m":8808,"d":20,"f":8809,"v":8810,"n":23,"y":299,"u":8811},"LADYBUG3",[],[],[8812],[1606,952,29,8813,1667],"six SONY progressive scan colour CCDs (five in a ring, one on top), 2 Mpx each, 360 deg panoramic, up to 15 fps",{"c":18,"m":8815,"d":46,"f":8816,"v":8817,"n":23,"y":270,"u":8818},"laptop",[],[],[8819],[3063,28,29,8820,4496],"2.5 GHz cores, 6 GB memory, about three cores used",{"c":18,"m":8822,"d":46,"f":8823,"v":8824,"n":23,"y":132,"u":8825},"laptop (ROS, Ubuntu 20.04)",[],[],[8826],[2325,23,2326,8827,8828],"recording computer; data recorded as ROS bags with official drivers and default synchronization","Sec. 2.3, Sec. 2.4",{"c":18,"m":8830,"d":46,"f":8831,"v":8832,"n":23,"y":1819,"u":8833},"laptop computer (2.67 GHz per Sec. 4.1; 2.6 GHz per the timing paragraph; model not reported)",[],[],[8834],[8835,28,29,8836,917],"fovis2017","used for the timing results of Table 1",{"c":18,"m":8838,"d":46,"f":8839,"v":8840,"n":23,"y":1819,"u":8841},"Laptop computer (model not stated)",[],[],[8842],[5093,28,29,8843,228],"2.5 GHz quad cores, 6 GiB memory, ROS on Linux; odometry and mapping on two threads",{"c":18,"m":8845,"d":46,"f":8846,"v":8847,"n":23,"y":233,"u":8848},"laptop or mini PC (model not reported)",[],[],[8849],[752,28,29,8850,8851],"receives LiDAR packets and MCU-timestamped IMU data; ROS driver","Sec. II, Fig. 1",{"c":18,"m":8853,"d":46,"f":8854,"v":8855,"n":23,"y":455,"u":8856},"laptop with 1.6 GHz Intel Celeron, 2 GiB RAM",[98],[],[8857],[1000,28,29,8858,8859],"collaborative ICP comparison (Sec. 6.4.2) and loop-detection timings (Sec. 8.2.5, Table 8.3: '1.6 GHz CPU'); Sec. 9.3.2 reports boulder labelling only on 'a laptop computer with a 1600 MHz CPU and 2 GiB of RAM' without naming the CPU, so it is not stated that this is the same Celeron laptop","Sec. 6.4.2, 8.2.5, 9.3.2",{"c":18,"m":8861,"d":46,"f":8862,"v":8863,"n":23,"y":132,"u":8864},"laptop with 11th Gen Intel Core i7-11800H and NVIDIA GeForce RTX 3060",[3317],[],[8865],[2447,28,29,8866,530],"remote computer over Wi-Fi 6.0, Ubuntu 18.04 LTS, ROS Melodic",{"c":18,"m":8868,"d":46,"f":8869,"v":8870,"n":23,"y":289,"u":8871},"laptop with 2.5 GHz quad cores",[],[],[8872],[5332,28,29,8873,4496],"Linux; method uses about 2.5 cores",{"c":18,"m":8875,"d":46,"f":8876,"v":8877,"n":23,"y":346,"u":8878},"laptop with 2.6 GHz i7 quad-core processor",[],[],[8879],[2905,28,29,8880,8881],"8 threads, integrated GPU, Linux with ROS","Sec. 10.1, Table 2",{"c":18,"m":8883,"d":46,"f":8884,"v":8885,"n":23,"y":49,"u":8886},"laptop with Intel Core i5-7300HQ",[],[],[8887],[1938,28,29,8888,3807],"8 GB RAM, all four CPU cores used",{"c":18,"m":8890,"d":46,"f":8891,"v":8892,"n":23,"y":233,"u":8893},"Laptop with Intel Core i5-8250U",[],[],[8894],[781,28,29,8895,5291],"Ubuntu 16.04, ROS; runs LIO-SAM offline on transferred bag files",{"c":18,"m":8897,"d":46,"f":8898,"v":8899,"n":23,"y":1008,"u":8900},"laptop with Intel Core i7-11800H and NVIDIA GeForce RTX 3060",[],[],[8901],[2865,28,29,8902,8903],"remote-control station connected over local Wi-Fi (Sec. 3.2); Sec. 4.1 states the Gazebo simulation ran on a computer with the same CPU and GPU, without saying it is this laptop; runtime figures in Table 8 are from the Jetson","Sec. 3.2, Sec. 4.1",{"c":18,"m":8905,"d":46,"f":8906,"v":8907,"n":23,"y":318,"u":8908},"laptop with Intel Core i7-4710HQ, 16 GB DDR3 RAM, NVIDIA GeForce GTX 960M",[],[],[8909],[2248,28,29,8910,8911],"Ubuntu 16.04, ROS Kinetic; runs segmentation, SLAM for both vehicles and planning","Sec. 3, 5.1",{"c":18,"m":8913,"d":46,"f":8914,"v":8915,"n":23,"y":152,"u":8916},"laptop with Intel Core i7-4940MX CPU @ 3.1GHz",[98],[],[8917],[1674,28,29,8918,147],"localization speed evaluation",{"c":18,"m":8920,"d":46,"f":8921,"v":8922,"n":23,"y":357,"u":8923},"laptop with Intel dual core 2.0 GHz CPU",[98],[],[8924],[6087,28,29,8925,214],"C++ implementation; no timings reported",{"c":18,"m":8927,"d":46,"f":8928,"v":8929,"n":23,"y":122,"u":8930},"laptop with Intel I5-8265U, 16G RAM",[6867],[],[8931],[8932,28,29,8933,917],"yin2023semanticbimloc","low-power laptop; libpointmatcher on ROS",{"c":18,"m":8935,"d":46,"f":8936,"v":8937,"n":23,"y":318,"u":8938},"laptop with Intel i7-10710U",[],[],[8939],[2758,28,29,8940,478],"CPU only, no parallel computing; ROS in Ubuntu",{"c":18,"m":8942,"d":46,"f":8943,"v":8944,"n":23,"y":49,"u":8945},"laptop with Intel i7-10710U (Ubuntu Linux)",[98],[],[8946],[2785,28,29,8947,898],"all compared methods executed on it",{"c":18,"m":8949,"d":46,"f":8950,"v":8951,"n":23,"y":1008,"u":8952},"laptop with Intel i7-10750H (written i7-10 750 H) at 3.5 GHz, 32 GB memory",[98],[],[8953],[2173,28,29,8954,2174],"used for initialization, single-session and multisession experiments",{"c":18,"m":8956,"d":46,"f":8957,"v":8958,"n":23,"y":122,"u":8959},"laptop with Intel i7-11700",[],[],[8960],[4348,28,29,6920,4496],{"c":18,"m":8962,"d":46,"f":8963,"v":8964,"n":23,"y":132,"u":8965},"laptop with Intel i7-11800H CPU, 48 GB RAM",[98],[],[8966],[1510,28,29,8967,127],"Ubuntu 22.04; all evaluations",{"c":18,"m":8969,"d":46,"f":8970,"v":8971,"n":23,"y":100,"u":8972},"laptop with Intel i7-12800H CPU",[98],[],[8973],[1263,28,29,8974,8515],"median 17 ms per scan-matching step",{"c":18,"m":8976,"d":46,"f":8977,"v":8978,"n":23,"y":346,"u":8979},"laptop with Intel i7-4710MQ",[],[],[8980],[2680,28,29,8981,3853],"2.5 GHz i7-4710MQ CPU, chosen to match the LOAM papers' hardware; CPU only",{"c":18,"m":8983,"d":46,"f":8984,"v":8985,"n":23,"y":233,"u":8986},"laptop with Intel i7-8750H (12 cores)",[98],[],[8987],[1405,28,29,8988,717],"runs the experiments reported in the paper",{"c":18,"m":8990,"d":46,"f":8991,"v":8992,"n":23,"y":318,"u":8993},"laptop, 2.4 GHz quad cores",[],[],[8994],[2391,28,29,8995,478],"8 GiB memory; ROS in Ubuntu",{"c":18,"m":8997,"d":46,"f":8998,"v":8999,"n":23,"y":270,"u":9000},"laptop, 2.5 GHz quad cores",[],[],[9001],[2630,28,29,9002,4496],"6 GiB memory; two cores used",{"c":18,"m":9004,"d":46,"f":9005,"v":9006,"n":23,"y":100,"u":9007},"laptop, 2.9 GHz 8-core CPU (model not stated)",[],[],[9008],[4756,28,29,9009,478],"16 GiB memory",{"c":18,"m":9011,"d":46,"f":9012,"v":9013,"n":23,"y":1421,"u":9014},"laptop, Intel quad-core 2.50 GHz CPU, 8 GB RAM",[98],[],[9015],[9016,28,29,9017,478],"m2dp2016","quad-core 2.50 GHz, 8 GB RAM",{"c":33,"m":9019,"d":46,"f":9020,"v":9021,"n":23,"y":100,"u":9022},"laser 3D tracking, motion capture and RTK receivers (models not stated)",[],[],[9023],[5070,41,4757,9024,1204],"ground-truth trajectories",{"c":108,"m":9026,"d":46,"f":9027,"v":9028,"n":41,"y":554,"u":9029},"laser range sensor (model not stated)",[],[],[9030,9032,9035,9038,9041,9044],[3371,23,3372,9031,167],"laser-range data; 3640 tree landmark measurements extracted",[3371,23,9033,9034,1204],"Intel dataset","laser range data preprocessed by scan matching into 910 poses and 4453 constraints",[3371,23,9036,9037,1204],"MIT Killian Court","laser range data preprocessed into 1941 poses and 2190 pose constraints",[7609,23,98,9039,9040],"laser range data converted to a 2D pose graph (910 poses, 4453 measurements)","Fig. 11; Table 1",[7609,23,9042,9043,9040],"Killian Court","laser range data converted to a 2D pose graph (1941 poses, 2190 measurements)",[7609,23,9045,9046,9047],"Victoria Park","laser range data with 151 landmarks, 6969 poses, 10608 measurements","Fig. 12; Table 1",{"c":108,"m":9049,"d":46,"f":9050,"v":9051,"n":41,"y":142,"u":9053},"laser rangefinder (model not reported)",[],[9052],"laser range-finder (model not reported)",[9054,9059],[9055,23,9056,9057,9058],"barfoot2014gp","Tong et al. indoor tube-landmark dataset (paper ref. [46])","range and bearing to 17 plastic-tube landmarks; odometry and landmark measurements at 1 Hz","Sec. IV-B; Sec. IV-D",[9060,23,9061,9062,9063],"dellaert2012gtsamtr","Victoria Park, Sydney (dataset due to Jose Guivant)","trees detected in laser scans used as landmarks","Sec. 5.4; Fig. 13",{"c":33,"m":9049,"d":46,"f":9065,"v":9066,"n":23,"y":132,"u":9067},[],[],[9068],[405,41,29,9069,9070],"described as accurate to four decimal places","Sec. 4.2, Table 2",{"c":108,"m":9072,"d":46,"f":9073,"v":9074,"n":23,"y":299,"u":9075},"laser scanner (model and scanning mechanism not reported)",[],[],[9076],[7000,23,9077,9078,9079],"Freiburg campus","81 dense 3D scans, 20 million end points, full laser range up to 50 m","Sec. 5.2, Sec. 5.5.1",{"c":108,"m":9081,"d":46,"f":9082,"v":9083,"n":23,"y":49,"u":9084},"laser scanner (model not reported)",[],[],[9085],[9086,53,29,9087,9088],"slamtoolbox2021","laser scans are the mapping input (scan matching, pose-graph nodes, manual scan-to-map matching in Fig. 2); 2D per the repository README, not stated in the paper","Summary; Features; Fig. 2",{"c":2807,"m":9090,"d":46,"f":9091,"v":9092,"n":23,"y":132,"u":9093},"laser scanner map with ICP (scanner models not reported)",[],[],[9094],[2221,41,9095,9096,1104],"Newer College, Hilti 2022, Oxford Spires, Botanic Garden, MCD","ground truth method for Newer College, Hilti 2022, Oxford Spires and Botanic Garden; laser scanner with continuous-time optimization for MCD; INS fusion for HeLiPR",{"c":108,"m":9098,"d":46,"f":9099,"v":9100,"n":23,"y":346,"u":9101},"laser scanner on FALCON (model not reported)",[],[],[9102],[4101,23,9103,9104,9105],"autonomous flight experiment","used for mapping only; global laser point cloud registered with S-MSCKF poses","Fig. 1; Sec. IV-C; Fig. 6",{"c":33,"m":9107,"d":46,"f":9108,"v":9109,"n":23,"y":1421,"u":9110},"laser tape measure",[],[],[9111],[2638,41,29,9112,9113],"used for five reference straight-line lengths compared with the Revo LDS floor plan","Sec. VI.B, Table I",{"c":5895,"m":9115,"d":46,"f":9116,"v":9117,"n":41,"y":122,"u":9118},"laser-tracker total station (model not stated)",[],[],[9119,9121],[5070,41,184,9120,1204],"centimetre-level ground truth",[1219,41,184,9120,332],{"c":522,"m":9123,"d":46,"f":9124,"v":9125,"n":23,"y":100,"u":9126},"left camera",[],[],[9127],[211,23,212,9128,9129],"grayscale images","Sec. VI, Sec. VI-F",{"c":522,"m":9131,"d":46,"f":9132,"v":9133,"n":23,"y":233,"u":9134},"left camera (model not stated)",[],[],[9135],[3255,23,212,569,167],{"c":372,"m":9137,"d":46,"f":9138,"v":9139,"n":41,"y":122,"u":9141},"legged robot (model not reported)",[],[9140],"Legged robot (model not reported)",[9142,9146],[9143,53,29,9144,9145],"sgraphsplus2023","robot shown navigating a construction site of four adjacent houses","Fig. 1 caption",[1371,23,1372,9147,5508],"max 2 m\u002Fs (Multi Floor, Block LiDAR, Over Exposure, Flash Light, Outdoor Night)",{"c":944,"m":9149,"d":20,"f":9150,"v":9151,"n":23,"y":132,"u":9152},"Leica BLK ARC",[911],[],[9153],[2071,53,29,9154,9155],"compact and lightweight; gives no time information for trajectory points","Sec. 1, 3.1.1, 4.1",{"c":944,"m":9157,"d":20,"f":9158,"v":9160,"n":952,"y":233,"u":9161},"Leica BLK2GO",[911,9159],"Leica Geosystems",[],[9162,9164,9167,9169],[4224,952,29,9163,4689],"handheld, 2018, indoor and outdoor; 3-camera system 300° x 150° FoV; LiDAR up to 25 m, 360 x 270; IMU and GPS columns marked no; ±1 cm indoors with a 2 min scan (manufacturer)",[2101,53,29,9165,9166],"wavelength 830 nm; FOV 360 deg (h) x 270 deg (v); range min 0.5 to 25 m; 420,000 pts\u002Fs; 3-camera panoramic vision system, 4.8 Mpixel, 300 x 135 deg, global shutter; stated indoor accuracy 0.010 m; hand carried at shoulder height","Table 1; Sec. 3.1; Sec. 3.3; Sec. 5",[1549,41,29,9168,167],"used with the RTC360 to record the ground-truth map of the mine tunnel",[1520,41,29,9170,9171],"vision-aided handheld scanner; failed to register in the degenerate open field, so only the revisited area was kept","Sec. V-B, V-C",{"c":2807,"m":9173,"d":20,"f":9174,"v":9175,"n":2212,"y":152,"u":9176},"Leica BLK360",[911,9159],[],[9177,9180,9183,9186,9189,9192],[2352,41,29,9178,9179],"placed at 10 scan locations; described as one order of magnitude more precise than depth cameras","Sec. 4.1, ref. [81]",[1562,41,1563,9181,9182],"high-resolution colourized dense maps with millimeter accuracy (as stated), scanned from multiple locations in small and middle-scale environments","Sec. IV-B; Fig. 4",[1811,41,1812,9184,9185],"high-resolution point cloud of the static part of the test environment","Sec. IV-B, IV-C; Fig. 12b",[896,41,815,9187,9188],"47 static scans over ~135x225 m in over 8 h, >90% inliers in matching; datasheet 6 mm at 10 m and 8 mm at 20 m; ~290 million points, downsampled to 1 cm (~17 million) for ICP","Sec. V, Fig. 2 bottom",[678,41,679,9190,9191],"0.68 million points\u002Fs; 4 mm within 10 m; some indoor areas","Sec. 3.3.1",[1247,41,1248,9193,9194],"survey-grade 3D imaging laser scanner; prior maps of New College (extended) and Maths Institute; GT poses by point-to-point ICP of IMU-undistorted scans to the prior map at 10 Hz","Sec. IV; Fig. 3",{"c":33,"m":9196,"d":20,"f":9197,"v":9198,"n":23,"y":299,"u":9199},"Leica blue and white HDS targets",[911],[],[9200],[9201,41,9202,9203,1176],"lague2013m3c2","Rangitikei river TLS surveys (2009 to 2011)","target-centre measurement error quoted at 2 mm (1 std) at 50 m; 3 to 5 common targets per station pair within 75 m; 5 targets bolted on rocks via machined adaptors (uncertainty \u003C 0.1 mm)",{"c":2807,"m":9205,"d":20,"f":9206,"v":9207,"n":23,"y":1819,"u":9208},"Leica C10",[911],[],[9209],[9210,23,9211,9212,9213],"khoshelham2017isprsindoor","ISPRS benchmark on indoor modelling","max range 300 m; 50 x 10^3 points\u002Fs; 0.01 deg horizontal and vertical; FOV 270 x 360 deg; relative accuracy 2 mm; absolute accuracy not given (Table 2); Fire Brigade: 14.1 x 10^6 points, 0.011 m spacing, colour, no trajectory, occlusion gaps (Table 1; Sec. 2)","Table 1; Table 2; Sec. 2",{"c":2807,"m":9215,"d":20,"f":9216,"v":9217,"n":23,"y":49,"u":9218},"Leica C10 (written 'TLS Leica C10')",[911],[],[9219],[9220,23,9211,9221,691],"khoshelham2021isprsindoorresults","Fire Brigade: 14.1 M points, 1.1 cm spacing, no trajectory, high clutter; median point-model distance 2.6 cm",{"c":33,"m":9223,"d":20,"f":9224,"v":9225,"n":23,"y":132,"u":9226},"Leica GZT21 4.5 inch black-and-white targets",[9159],[],[9227],[3843,41,29,9228,1212],"georeferencing of individual P40 scans; centres measured with the MS60",{"c":33,"m":9230,"d":20,"f":9231,"v":9232,"n":23,"y":132,"u":9233},"Leica GZT21 black-and-white targets and Leica GMP111 mini prism",[9159],[],[9234],[1990,41,29,9235,9236],"15 GCPs for P40 registration and georeferencing; mini prism used to signal reflective SLAM targets","Measurement and processing of the reference dataset",{"c":2807,"m":9238,"d":20,"f":9239,"v":9240,"n":23,"y":37,"u":9241},"Leica HDS6000",[911],[],[9242],[40,53,29,9243,9244],"range-model limits d_min 0 m and d_max 80 m used (Sec. 4.2.2); experiments under near-laboratory conditions","Sec. 4, 4.2.2",{"c":2807,"m":9246,"d":20,"f":9247,"v":9248,"n":23,"y":318,"u":9249},"Leica HDS6100",[911],[],[9250],[771,23,9251,9252,3230],"FGI-TLS forest (part of WHU-TLS benchmark)","field of view 360 x 310 deg; measurement accuracy ±2 mm at 25 m; 15.7 mm point spacing at 25 m in high density mode",{"c":651,"m":9254,"d":20,"f":9255,"v":9256,"n":23,"y":233,"u":9257},"Leica iCON iXE3 GNSS system",[911],[],[9258],[3787,53,29,9259,9260],"two antennas; RTK corrections from permanently installed base stations; 20 Hz; publishes covariance that grows when reliability drops; also used as the reference for Table I","Sec. IV-D, V-a, V-A1",{"c":33,"m":9262,"d":20,"f":9263,"v":9264,"n":23,"y":233,"u":9265},"Leica mini prism on a rod",[911],[],[9266],[3742,41,29,9267,332],"low-weight prism mounted on top of a rod for good visibility throughout the flight; its 3-DoF position measured by the MS60 is the ground truth",{"c":5895,"m":9269,"d":20,"f":9270,"v":9271,"n":41,"y":346,"u":9272},"Leica MS50",[911],[],[9273,9275],[1078,41,1079,9274,1081],"ground-truth states",[1083,41,1079,9274,1084],{"c":5895,"m":9277,"d":20,"f":9278,"v":9279,"n":23,"y":1819,"u":9280},"Leica MS50 laser tracking system",[911],[],[9281],[3618,41,1079,9282,9283],"provides the ground-truth trajectory","Sec. XI-B-1",{"c":5895,"m":9285,"d":20,"f":9286,"v":9287,"n":28,"y":233,"u":9288},"Leica MS60",[911,9159],[],[9289,9291,9293],[1201,41,2519,9290,3256],"millimeter-level accuracy; position ground truth for the flight tests",[5228,41,5229,9292,469],"ground-truth positions with approximately 3 cm accuracy",[678,41,679,9294,9295],"tracks prism mounted atop LiDAR; 3-DoF position at 5 to 8 Hz, accuracy 1 mm; resampled to 20 Hz by cubic spline; loses prism when occluded or out of range","Sec. 3.3.2-3.3.3; Sec. 6.2.1",{"c":5895,"m":9297,"d":20,"f":9298,"v":9299,"n":23,"y":1421,"u":9300},"Leica Nova MS50",[911],[],[9301],[1053,41,1054,9302,9303],"laser tracker mode on a prism on the MAV: about 20 Hz, accuracy about 1 mm (3D position only); scanner mode: Vicon-room point cloud from seven positions, accuracy about 1 mm","Table 1, Sec. 2, Sec. 3.1-3.2, Fig. 4",{"c":5895,"m":9305,"d":20,"f":9306,"v":9308,"n":2256,"y":100,"u":9309},"Leica Nova MS60",[911,9159,9307],"Leica Geosystems AG, Switzerland",[],[9310,9313,9316,9319,9321],[2135,41,2136,9311,9312],"listed as laser tracker; prism tracking at 10 Hz, accuracy 1 mm; 3-DoF position ground truth in metro tunnels","Table 2, Sec. 4.4.2, Sec. 4.5",[2298,41,29,9314,9315],"tracks a reflective prism on the robot; angular accuracy 0.0003 deg, range accuracy 3 mm or better; initially time-synchronized with the robot","Sec. V-A-1; Fig. 2",[1986,41,29,9317,9318],"distance std 1.0 mm + 1.5 ppm (prism), 2 mm + 2 ppm (any surface); angle std 0.3 mgon; geodetic network from three stations in two groups, 83 redundant observations, expected point accuracy 1 mm","The used devices and software; Measurement and processing of the reference dataset",[905,41,184,9320,332],"ground truth trajectory",[3843,41,29,9322,9323],"distance SD 1.0 mm + 1.5 ppm (prism), 2 mm + 2 ppm (any surface); angle SD 1 arcsec; 47 targets from 8 standpoints in two sets; network SD 1.2 mm (X, Y) and 0.6 mm (Z)","Sec. 2.2; Sec. 2.3; Sec. 3.2",{"c":5895,"m":9325,"d":20,"f":9326,"v":9327,"n":28,"y":233,"u":9328},"Leica Nova MS60 MultiStation",[911,9159],[],[9329,9331,9334],[201,41,184,9330,1204],"tracks a crystal prism on the UAV for ground-truth positions",[3460,41,184,9332,9333],"tracks a crystal prism on top of the UAV at 20 Hz; position only (orientation not set); prism ~0.4 m from body origin; max prism displacement under payload tilt ~2 cm","Sec. 2.5, Table 2, Sec. 5.3, Sec. 6.1",[3742,41,29,9335,9336],"measures ground-truth 3-DoF position (no rotation) of a mini prism on a rod","Sec. IV-A, Fig. 3b",{"c":2807,"m":9325,"d":20,"f":9338,"v":9339,"n":23,"y":233,"u":9340},[911],[],[9341],[3742,53,29,9342,9343],"its scanning functionality generated the B737 reference point cloud: over 1.6 M points, mean nearest-neighbour distance 1.4 cm (Sec. IV-A, Fig. 1)","Sec. IV-A, Fig. 1",{"c":5895,"m":9345,"d":20,"f":9346,"v":9347,"n":23,"y":152,"u":9348},"Leica Nova TM50",[911],[],[9349],[6800,41,29,9350,469],"measures commanded task locations and final end-effector placements",{"c":5895,"m":9352,"d":20,"f":9353,"v":9354,"n":23,"y":132,"u":9355},"Leica Nova TS60",[9307],[],[9356],[1990,41,29,9357,9318],"angle std 0.15 mgon; distance std 0.6 mm + 1.0 ppm (prism), 2 mm + 2 ppm (any surface); spatial polar method, two repetitions; GCP coordinates determined twice with 2 mm average difference",{"c":944,"m":9359,"d":20,"f":9360,"v":9361,"n":23,"y":318,"u":9362},"Leica Pegasus",[911],[],[9363],[951,952,29,9364,9365],"backpack with low-cost 3D LiDAR; 13 kg; 300,000-600,000 pts\u002Fs; relative accuracy 3 cm; 4 h; 5 CCD cameras, 4 Mp, 360° x 200° (listed as 'Leica Pegasus' in Tables 1-4 and 6, as 'Leica Pegasus Backpack' in Fig. 5g and Table 5)","Sec. 2.2.2; Tables 1-6; Fig. 5g",{"c":944,"m":9367,"d":20,"f":9368,"v":9369,"n":23,"y":233,"u":9370},"Leica Pegasus: Backpack",[9159],[],[9371],[4224,952,29,9372,4689],"wearable, 2017, indoor and outdoor; 360° x 200° FoV camera; dual Velodyne VLP-16, 100 m; IMU and GPS; 2-3 cm relative and 5 cm absolute accuracy (manufacturer)",{"c":944,"m":9374,"d":20,"f":9375,"v":9376,"n":23,"y":233,"u":9377},"Leica Pegasus: Two Ultimate",[9159],[],[9378],[4224,952,29,9379,9380],"vehicle-mounted, released 2018, outdoor; 360° FoV camera; ZF9012 profiler 360° x 41.33°, 100 m; IMU and GPS; manufacturer-reported 2 cm horizontal and 1.5 cm vertical accuracy; LiDAR produces 1,000,000 points\u002Fs (Sec. 1)","Table 4; Sec. 1, 4.1; Figs. 1-2",{"c":944,"m":9382,"d":20,"f":9383,"v":9384,"n":41,"y":318,"u":9386},"Leica ProScan",[911,9159],[9385],"Leica Proscan",[9387,9390],[4224,952,29,9388,9389],"trolley, 2017, indoor and outdoor; no camera; Leica ScanStation P40, P30 or P16; IMU and GPS; 0.12 cm range accuracy for ScanStation P40 (manufacturer)","Table 4; Sec. 4.3; Fig. 4",[951,952,29,9391,9392],"trolley integrating a TLS (Leica ScanStation P30, P40 or P16; 1 million pts\u002Fs; 1.2 mm + 10 ppm) and an IMU; 40 kg; 4 h; internal camera plus Canon EOS 60D, 70D or 80D","Sec. 2.3.3; Tables 1-6; Fig. 8a",{"c":2807,"m":9394,"d":20,"f":9395,"v":9396,"n":9398,"y":233,"u":9399},"Leica RTC360",[911,9159],[9397],"Leica RTC 360",14,[9400,9403,9407,9410,9412,9414,9416,9418,9420,9422,9425,9428,9431,9433],[2135,41,2136,9401,9402],"range 130 m, vertical FOV 300 deg, horizontal FOV 360 deg, accuracy 1 mm; ground-truth map of the stairs building","Table 2, Sec. 4.4.1-4.4.2",[114,23,9404,9405,9406],"MCD VIRAL (part)","survey 3D laser scanner; about 1.3 million points per scan, 300 deg vertical FoV; discrete stations with large height differences","Sec. I, Sec. IV-A, Fig. 1",[657,41,658,9408,9409],"fixed-station laser scanner used for ground-truth maps; MS-dataset ground truth stated with 6 mm precision","Sec. III-A1; Sec. IV-A1; Fig. 3(b)",[4660,41,29,9411,4662],"63 scans with 24 target spheres; 6 mm resolution at 10 m; 3.5 h acquisition; registered in Leica Cyclone Register 360 by cloud-to-cloud plus target alignment, absolute mean error 3 mm",[1219,41,1220,9413,745],"survey scanner used to build a static map with centimetre accuracy; Ouster scans registered to it for ground-truth poses",[3787,41,29,9415,4103],"ground-truth map of the construction task site for a qualitative overlay",[1549,41,29,9417,167],"used with the BLK2GO to record the ground-truth map of the mine tunnel",[790,41,791,9419,793],"millimetre-accurate reference map",[1296,41,1280,9421,1298],"ConSLAM ground-truth TLS; registration RMSE 0.902-0.994 cm per [51]",[2662,41,791,9423,9424],"max range 130 m, FoV 360 x 300 deg, 3D point accuracy 1.9 mm at 10 m and 5.3 mm at 40 m, colour from 432 MP images; registered in Leica Cyclone REGISTER 360 Plus; site cloud-to-cloud error 3-7 mm; merged map downsampled to 1 cm","Sec. 3.2, Sec. 5.1.6",[1279,41,1280,9426,9427],"static scans by land surveyors, stitched in proprietary software; max 10 m between stations; multi-view registration with bundle adjustment","Sensors and devices; Ground-truth scans; Fig. 1b",[1553,41,9429,569,9430],"Seemuhle mine and Opfikon City Park ground-truth maps","Sec. VII-D, VII-F, Fig. 6-c",[678,41,679,9432,9191],"up to 2 million points\u002Fs; accuracy under 5.3 mm within 40 m radius; outdoor map scans",[2071,41,29,9434,9435],"written 'Leica RTC 360'; reference point clouds","Sec. 4.1, Table 2",{"c":2807,"m":9437,"d":20,"f":9438,"v":9439,"n":23,"y":299,"u":9440},"Leica Scanstation 2",[911],[],[9441],[9201,53,9202,9442,147],"manufacturer accuracy (1 std at 50 m): 4 mm range, 60 urad angular; measured repeatability 1.4 mm at 50 m and accuracy about 0.2 mm at 50 m; laser footprint 4 mm between 1 and 50 m; dual-axis compensation always on; high-resolution scans aimed at 10 mm horizontal and 5 mm vertical spacing at 50 m",{"c":2807,"m":9444,"d":20,"f":9445,"v":9446,"n":23,"y":318,"u":9447},"Leica ScanStation C5",[911],[],[9448],[771,23,9449,569,9450],"WHU-TLS (mountain)","Table 2; Sec. 4.3",{"c":2807,"m":9452,"d":20,"f":9453,"v":9454,"n":952,"y":318,"u":9455},"Leica ScanStation P40",[911,9159,9307],[],[9456,9459,9461,9464],[771,23,9457,569,9458],"WHU-TLS (residence)","Table 2; Sec. 4.7",[1986,41,29,9460,9318],"distance std 1.2 mm + 10 ppm; angle std 2.4 mgon; range 0.4-270 m at 34% reflectance; FOV 360 x 270 deg; up to 1 M pts\u002Fs; compensator 0.45 mgon; three positions 2 m apart on each of seven profiles; georeferencing errors 1 mm or better; Leica Cyclone 2023.0.1",[1990,41,29,9462,9463],"angle std 2.4 mgon; distance std 1.2 mm + 10 ppm; up to 1 M pts\u002Fs; 0.4-270 m; FOV 360 x 270 deg; 12 stations at 3.1 mm per 10 m; four Leica GZT21 targets per station; average target std 6 mm; Cyclone Register 360+ 2023.1.0","The used devices and software; Measurement and processing of the reference dataset; Results",[3843,41,29,9465,9466],"1550 nm, FoV 360 x 270 deg, 1 Mpts\u002Fs, range 270 m, range accuracy 1.2 mm + 10 ppm, angle 8 arcsec; range limited to 30 m here; each scan georeferenced to at least three, mostly four GZT21 targets; checked against 24 total-station points in three profiles (one removed after visual check), RMSD 1.4 mm","Sec. 2.2; Sec. 2.3; Sec. 3.2; Table 1",{"c":5895,"m":9468,"d":46,"f":9469,"v":9470,"n":23,"y":233,"u":9471},"Leica total station (model not reported)",[911],[],[9472],[3947,41,4487,9473,469],"ground-truth position",{"c":5895,"m":9475,"d":20,"f":9476,"v":9477,"n":23,"y":1819,"u":9478},"Leica TPS MS50",[911],[],[9479,9482],[1764,41,9480,9481,214],"cow dataset","described as a laser scanner; 3 scans merged for structure ground truth",[1764,41,9483,9484,214],"EuRoC MAV (V1_01_easy)","scans used as structure ground truth",{"c":5895,"m":9486,"d":20,"f":9487,"v":9488,"n":23,"y":122,"u":9489},"Leica TS16 (called a laser tracker in the paper)",[911],[],[9490],[1462,41,29,9491,9492],"tracks the robot; orientation estimated by an optimization-based method (SMR, FSC)","Sec. VI-B, Fig. 15",{"c":5895,"m":9494,"d":20,"f":9495,"v":9496,"n":23,"y":132,"u":9497},"Leica TS30",[911],[],[9498],[537,41,29,9499,9500],"tracks a 360-degree prism mounted directly above the LiDAR with millimetre accuracy (as stated); time set via GNSS; local reference point network","Hardware Setup; Data Collection",{"c":651,"m":9502,"d":20,"f":9503,"v":9504,"n":23,"y":289,"u":9505},"Leica Viva GS14",[911],[],[9506],[1074,41,29,9507,9508],"post-processed DGPS 3D ground truth at 1 Hz; measurements with position uncertainty above 1 m discarded","Sec. VII-A3, VII-B2; Table II",{"c":5895,"m":9510,"d":20,"f":9511,"v":9512,"n":23,"y":299,"u":9513},"Leica Viva TS15",[911],[],[9514],[4141,41,29,9515,863],"control network, target and scanner-station coordinates and element measurements; processed in Leica Geo Office 8.1",{"c":18,"m":9517,"d":46,"f":9518,"v":9520,"n":23,"y":318,"u":9521},"Lenovo ThinkPad workstation",[9519],"Lenovo",[],[9522],[4124,28,29,9523,8665],"Intel Core i7 2.5 GHz, 16 GB RAM; algorithms in C++",{"c":33,"m":9525,"d":20,"f":9526,"v":9527,"n":23,"y":1008,"u":9528},"Lenovo USB-C Hub (F1-C03)",[9519],[],[9529],[2018,53,2019,9530,2021],"integrates all inputs and power for the computer",{"c":522,"m":9532,"d":20,"f":9533,"v":9535,"n":23,"y":100,"u":9536},"Leopard Imaging RGB fisheye cameras x4 (Fig. 2: 'LI-XAVIER')",[9534],"Leopard Imaging",[],[9537],[1371,23,1372,9538,3908],"24 Hz, 686 x 816 pixels; synchronized by an FPGA board; Kalibr calibration with radial-tangential distortion",{"c":944,"m":9540,"d":20,"f":9541,"v":9543,"n":23,"y":318,"u":9544},"LiBackPack C50",[9542],"GreenValley International",[],[9545],[4161,952,29,9546,9547],"backpack; 8.8 kg without battery; 300,000 points\u002Fs; FOV 360 deg H, -15 to +15 deg V; indoor range 0.1-100 m, outdoor 100 m; resolution 0.2 deg H, 2.0 deg V; relative accuracy 3 cm; absolute position accuracy 5 cm; panoramic camera; GNSS outdoors, IMU and SLAM indoors","Sec. 3.1, Table 1",{"c":108,"m":9549,"d":46,"f":9550,"v":9551,"n":23,"y":122,"u":9552},"LiDAR (model not named)",[],[],[9553],[1349,53,9554,9555,9556],"Fusion Portable; Newer College","only input sensor; scans decimated to 5 Hz; no IMU used (ICP starts from the identity)","Abstract; Sec. III-A, III-B; Sec. IV-C",{"c":108,"m":9558,"d":46,"f":9559,"v":9560,"n":23,"y":132,"u":9561},"LiDAR (model not reported; labelled 'LiDAR' in Fig. 3(a))",[],[],[9562],[657,23,658,569,660],{"c":108,"m":9564,"d":46,"f":9565,"v":9566,"n":23,"y":132,"u":9567},"LiDAR (model not stated)",[],[],[9568],[1136,53,29,569,9569],"Sec. II; Fig. 8(e) arXiv, Fig. 12(e) T-RO",{"c":108,"m":9571,"d":46,"f":9572,"v":9573,"n":23,"y":318,"u":9574},"LiDAR integrated in the Carnegie Robotics MultiSense SL (model not stated)",[2618],[],[9575],[321,23,322,9576,324],"second LiDAR for redundancy and comparison",{"c":108,"m":9578,"d":46,"f":9579,"v":9580,"n":23,"y":233,"u":9581},"LiDAR scans of the Chilean underground mine dataset (instrument not reported)",[],[],[9582],[585,23,9583,9584,717],"Chilean underground mine dataset","44 scans of about 25 M points each, taken 30 to 40 m apart with large rotations; 152.5 s per scan acquisition",{"c":33,"m":9586,"d":46,"f":9587,"v":9589,"n":23,"y":100,"u":9590},"LiDAR-observable circular GCP fiducial marker (proposed)",[9588],"Hilti (authors)",[],[9591],[9592,41,9593,9594,9595],"nair2024hilti2023","Hilti SLAM Challenge 2023","circular target on floor detected from LiDAR intensity edges with Hough voting; median 2.3 mm, max 4.7 mm relative 3-DoF error","Sec. III-D, Algorithm 1, Fig. 3-4",{"c":944,"m":9597,"d":20,"f":9598,"v":9599,"n":23,"y":1008,"u":9600},"light handheld device with Livox Avia and its internal IMU",[],[],[9601],[2173,53,9602,9603,9604],"private dataset (private1, private2)","used for private1 (forest, fast initial motion) and private2 (campus with elevator and escalator, 11 min 34 s)","Sec. 10; Fig. 16a; Table C1",{"c":372,"m":9606,"d":46,"f":9607,"v":9609,"n":23,"y":346,"u":9610},"Lincoln MKZ (modified, drive-by-wire)",[9608],"Lincoln",[],[9611],[2922,53,29,9612,9613],"test speeds 5 to 30 mph","Sec. IV-A, IV-E",{"c":33,"m":9615,"d":46,"f":9616,"v":9617,"n":23,"y":152,"u":9618},"linear motion table (rail) with rotating wooden board",[],[],[9619],[9620,41,29,9621,9622],"laconte2019lidarbias","rail of about 30 m; board 0.6 x 0.6 m on a graduated disc; 12 board orientations (0 to 60 deg in 10 deg steps, 65 to 85 deg in 5 deg steps) at 8 depths from 1 to 10 m; 45 s per setting; metrology room at 20 C","Sec. IV, Fig. 5",{"c":33,"m":9624,"d":46,"f":9625,"v":9626,"n":23,"y":759,"u":9627},"Linear Variable Differential Transformer (LVDT) on the steering rack",[],[],[9628],[888,53,29,9629,332],"measures steering for vehicle heading",{"c":18,"m":9631,"d":46,"f":9632,"v":9633,"n":23,"y":142,"u":9634},"Linux laptop (model not reported) running Ubuntu 10.10 and ROS Diamondback",[],[],[9635],[990,28,991,9636,478],"data recording",{"c":108,"m":9638,"d":20,"f":9639,"v":9642,"n":9653,"y":49,"u":9654},"Livox Avia",[3801,9640,9641],"Livox (as named)","Livox (written 'LiVOX')",[9643,9644,9645,9646,9647,9648,9649,9650,9651,9652],"AVIA","AVIA (written 'Aivia' in Sec. V-D2)","Avia","LiVOX AVAI","LiVOX AVIA","LiVOX AVIA 3D LiDAR","Livox AVIA","Livox Avia LiDAR","Livox avia","Livox-Avia (as written)",39,[9655,9657,9659,9662,9666,9668,9671,9674,9677,9679,9681,9684,9685,9688,9690,9693,9696,9699,9700,9703,9705,9706,9708,9711,9713,9715,9718,9720,9721,9722,9723,9725,9728,9730,9732,9734,9736,9738,9741,9744,9748,9750,9754,9757,9759,9761,9765,9768],[3483,53,29,9656,1072],"70 degree FoV; frame rate 100 Hz",[1510,53,3345,9658,3347],"on the wheeled bipedal robot",[7474,53,29,9660,9661],"handheld small-FOV solid-state LiDAR (Sec. III-B-3; Sec. III-C-3); captures the self-collected GDUT data (Sec. III-B-1)","Sec. III-B-1; Sec. III-B-3; Sec. III-C-3",[7474,23,9663,9664,9665],"AVIA (from FastLIO2 and R3LIVE); Botanic Garden","handheld; avia_2 and avia_3 sampled at 100 Hz; also used in Botanic Garden '*' sequences","Sec. III; Sec. III-B-1; Sec. III-B-5; Tables II-IV",[2135,23,2136,9667,2691],"non-repetitive scanning, range 450 m, vertical FOV 30 deg, horizontal FOV printed as 360 deg, 10 Hz; device gamma (text describes its FOV as limited)",[2160,53,29,9669,9670],"solid-state; FOV 70.4 deg x 77.2 deg; range precision 2 cm; angular precision \u003C 0.5 deg; 498 g","Sec. 6.1; Table 1",[1145,23,1146,9672,9673],"HeLiPR sensor; non-repetitive pattern shown in Fig. 1","Fig. 1; Table III",[2169,53,29,9675,9676],"solid-state, 70.4 deg x 77.2 deg circular FoV, non-repetitive scanning, 230,000 points\u002Fs, packages at 10 to 100 Hz","Sec. 5.2; Fig. 3a",[7639,53,29,9678,478],"solid-state LiDAR with a built-in IMU",[2054,23,2121,9680,1104],"240,000 points\u002Fs; mechanical, non-repetitive; 3 m to 450 m; FoV 70.4 x 77.2 deg",[2054,53,29,9682,9683],"240,000 points\u002Fs; mechanical, non-repetitive; 3 m to 450 m; FoV 70.4 x 77.2 deg; hardware-synchronized with camera","Table I (Our Device II); Sec. III",[5280,53,6600,569,1104],[4121,53,29,9686,9687],"non-repetitive scan LiDAR; used for the eight flat-wall degeneration sequences and the cross-sensor test","Sec. VI-A; Sec. VI-B; Table III",[2452,23,2453,9689,1979],"10 Hz, solid-state small FoV",[2018,53,2019,9691,9692],"internal tightly coupled IMU; nominal ranging precision +\u002F-20 mm as stated; 0.02 m mechanical tolerance used as RANSAC ring-band width","Sec. 2.1.1; Sec. 3.4",[1747,53,29,9694,9695],"FoV 70.4 x 77.2 deg","VoR Sec. VI-A1",[599,53,1751,9697,9698],"FoV 70.4 x 77.2 deg; 10 Hz; about 240k points per second","VoR Sec. VI-B1; Sec. VI-G",[3153,53,29,9694,4427],[3444,53,29,9701,9702],"solid-state, Risley prism; FoV 70.4 x 77.2 deg; 240,000 points\u002Fs single return; USD 1,599 (Table I)","Sec. VIII-A1, Table I, Fig. 6",[3444,23,2145,9704,1104],"solid-state, Risley prism; FoV 70.4 x 77.2 deg; 240,000 points\u002Fs",[3444,53,29,7411,3446],[201,53,3124,9707,3126],"lidar L3 without FoV overlap with MID-100",[2173,23,1137,9709,9710],"downward-looking on a UAV at about 100 m height; speed up to 12 m\u002Fs","Sec. 10; Sec. 10.2.2",[2173,53,9602,9712,2174],"on the authors' handheld device",[1155,23,1146,9714,332],"non-repetitive scan pattern",[2366,53,29,9716,9717],"built-in IMU; 10 Hz (Fig. 2)","Sec. IV-A; Fig. 1; Fig. 2",[1020,23,2145,9719,332],"with built-in IMU",[1101,53,2142,5694,1843],[1101,23,1137,5694,1104],[1101,23,2145,5694,1104],[3427,23,9724,569,2207],"GEODE (Inlandwaterways, Tunnelingtunnel); MARS-LVIG; R3LIVE",[1966,23,9726,569,9727],"HeLiPR (Town, Roundabout)","Sec. IV-C1; Table I",[2483,23,2484,9729,2486],"10 Hz; handheld scanning",[2423,23,5376,9731,917],"10 Hz; handheld device with built-in IMU and a 15 Hz RGB camera (640 x 512)",[2423,23,2426,9733,917],"second LiDAR on the Botanic Garden robot",[3133,53,29,9735,3135],"solid-state, 70 deg FoV, installed directly on the airframe",[378,53,29,9737,2153],"solid-state, 70.4 deg (H) x 77.2 deg (V) circular FoV, non-repetitive scan pattern, built-in IMU; scan rate 100 Hz unless stated (10 Hz in aerial test)",[7952,53,29,9739,9740],"solid-state, non-repetitive scanning; 20 s accumulation per scene; vertical beam divergence angle 0.28° cited in Sec. IV-B","Fig. 10; Sec. IV, IV-B",[5386,53,29,9742,9743],"solid-state, 10 Hz, non-repetitive scan, FoV 70°×77°","Table I; Sec. IV-C",[7482,53,9745,9746,9747],"self-collected Avia Park1, Avia Park2 and Avia Indoor (multi-floor building)","small-FOV, non-repetitive scanning solid-state LiDAR; 20 frames accumulated per keyframe","Abstract; Fig. 1; Sec. IV-B; Fig. 12",[211,23,2415,9749,214],"with internal IMU; model name as printed, the paper gives no other model designation",[905,23,9751,9752,9753],"Point-LIO dataset","rotating-platform and swinging-rope sequences beyond the IMU range","Sec. IV-A; Sec. IV-E",[3255,53,3509,9755,9756],"solid-state LiDAR with built-in IMU","Sec. VI-B1; Fig. 4",[216,23,1137,9758,157],"with built-in BMI088 IMU; triggered at 10 Hz",[216,53,3513,9694,9760],"Sec. VIII-B1; Fig. 9",[135,23,9762,9763,9764],"unnamed LiDAR-degeneration test sequence of Fig. 9 (dataset not stated)","small-FoV LiDAR, facing a wall in the degeneration test","Sec. IV-A2; Sec. V-D2; Fig. 9",[2239,53,29,9766,9767],"small FoV, non-repetitive scanning; output set to 10 Hz; built-in BMI088 IMU hardware-synchronized in factory","Fig. 1, Sec. IV, Sec. IV-A, Table II to III",[3805,53,3806,569,3807],{"c":662,"m":9770,"d":20,"f":9771,"v":9772,"n":28,"y":233,"u":9773},"Livox Avia built-in IMU",[3801],[],[9774,9775,9777],[2423,23,5376,1272,917],[5386,53,29,9776,745],"200 Hz; used for motion-distortion compensation and as motion prior (LiDAR-inertial mode)",[3255,53,3509,569,4973],{"c":662,"m":9779,"d":20,"f":9780,"v":9781,"n":41,"y":122,"u":9782},"Livox Avia internal IMU",[3801],[],[9783,9784],[2452,23,2453,1272,1979],[2018,53,2019,9785,9786],"built-in LiDAR-IMU hardware synchronization; over 200 static frames for gravity initialization","Sec. 2.1.1; Sec. 2.1.2; Table A1",{"c":33,"m":9788,"d":20,"f":9789,"v":9790,"n":23,"y":1008,"u":9791},"Livox box",[3801],[],[9792],[2018,53,2019,9793,2021],"receives LiDAR data",{"c":108,"m":9795,"d":20,"f":9796,"v":9797,"n":23,"y":100,"u":9799},"Livox Hap",[3801],[9798],"Livox HAP",[9800],[4756,53,9801,9802,9803],"VoxelMap++ own datasets (UESTC forest, grassland and corridors)","solid-state non-repetitive LiDAR, 120 x 25 deg FOV, 10 Hz; described as the first automotive-grade LiDAR for serial production","Sec. IV; Fig. 4",{"c":108,"m":9805,"d":20,"f":9806,"v":9807,"n":6535,"y":1421,"u":9812},"Livox Horizon",[3801],[9808,9809,9810,9811],"Livox Horizon (as written in Table I)","Livox-Horizon","Livox-Horizon (as written)","horizontally mounted LIDAR on the Cartographer backpack (model not reported)",[9813,9816,9819,9821,9824,9826,9828,9831,9832,9836,9839],[2169,23,9814,9815,3364],"lili (LILI-OM dataset)","solid-state 3D LiDAR",[2638,53,29,9817,9818],"provides the laser scans matched to 2D submaps; on the unstable backpack its scans are projected into the 2D world using the IMU gravity estimate; model, range and scan rate not reported","Sec. III, IV, VI",[5280,23,5281,9820,1843],"limited FOV",[1938,53,29,9822,9823],"solid-state, 81.7 x 25.1 deg FoV, 10 Hz; six vertically aligned laser diodes sweeping non-repetitively; 0.2 to 0.4 deg angular resolution over a 100 ms frame","Sec. 1; Sec. 3.1; Fig. 3",[6382,53,29,9825,167],"25 deg x 82 deg FoV; handheld",[1020,23,9827,9719,332],"LiLi-OM dataset",[3761,952,29,9829,9830],"240,000 pts\u002Fs, 90 m, 20 mm at 25 m; hand-carried","Sec. 2.3; Table 1; Fig. 2b",[2483,23,3771,185,469],[378,23,9833,9834,9835],"LiLi-OM dataset (lili)","solid-state, non-repetitive, 81.7 deg (H) x 25.1 deg (V) FoV, 10 Hz","Sec. VI; Table II",[7482,23,9837,9838,469],"KA Urban East (open-sourced with LiLi-OM)","solid-state LiDAR",[1164,53,6758,9840,2440],"non-repeating scan; detection up to 260 m under strong sunlight (cited spec); clock synced with GPS-RTK; sale price 800 USD",{"c":108,"m":9842,"d":46,"f":9843,"v":9845,"n":23,"y":1008,"u":9846},"LIVOX LiDAR (model not_reported)",[9844],"Livox (written 'LIVOX')",[],[9847],[2537,23,2538,569,917],{"c":108,"m":9849,"d":20,"f":9850,"v":9851,"n":2552,"y":233,"u":9857},"Livox MID-360",[3801],[9852,9853,9854,9855,9856],"Livox MID360","Livox Mid-360","Livox Mid360","Livox Mid360 (L2)","Mid360",[9858,9860,9863,9867,9869,9871,9875,9878,9880],[1510,23,1511,9859,5798],"second LiDAR; its built-in IMU used as I",[1510,53,2337,9861,9862],"helmet-mounted, 12 x 12 x 8 m3 space, walking, running, jumping and in-hand waving","Table I; Sec. V-C; Fig. 4A",[114,23,9864,9865,9866],"two-floor structure sequence","two-floor structure","Sec. IV-A, Fig. 7",[3427,53,9868,569,1104],"self-collected (Garage, Library, Yard, Laboratory, Flying Arena)",[2423,53,29,9870,5746],"self-collected sequence 'private-360', images 640 x 512",[2429,23,9872,9873,9874],"SLABIM (HKUST office benchmark)","handheld sensor suite with built-in IMU","Sec. 4.2, Fig. 13",[2429,23,2430,9876,9877],"Floor 06 and Floor 08 sequences; each sequence has LiDAR scans with synchronized IMU measurements","Sec. 4.2, Sec. 4.2.1, Fig. 13",[135,53,1913,569,9879],"Sec. V-A2; Fig. 5",[2239,53,29,9881,9882],"non-repetitive scanning; output set to 10 Hz; built-in BMI088 IMU hardware-synchronized in factory","Fig. 1, Sec. IV, Sec. IV-A, Table II to III, Fig. 3, Fig. 8",{"c":662,"m":9884,"d":20,"f":9885,"v":9886,"n":23,"y":1008,"u":9887},"Livox Mid-360 built-in IMU",[3801],[],[9888],[2429,23,9872,569,9889],"Sec. 4.2, Fig. 13 caption",{"c":108,"m":9891,"d":20,"f":9892,"v":9893,"n":41,"y":318,"u":9896},"Livox MID40",[3801],[9894,9895],"Livox MID-40","Livox Mid-40",[9897,9900],[2501,53,29,9898,9899],"written 'Livox Mid-40' in Fig. 3; front-facing conical FoV of 38.4 deg; rosette-like non-repetitive scanning; 20 ms per frame (Fig. 4); point-selection thresholds stated for MID40 (deflection angle >= 17 deg removed, intensity limits 7e-3 and 1e-1, incidence angle limits 5 and 175 deg). Sec. V and the experiment figures do not name the LiDAR on the hand-held rig; MID40 is inferred from the method parameters","Sec. I, III-A; Figs. 3-4",[6382,53,29,9901,1204],"small 40 deg FoV; mounted on a UGV",{"c":108,"m":9903,"d":20,"f":9904,"v":9905,"n":2212,"y":233,"u":9909},"Livox MID70",[3801],[9906,9907,9908],"Livox MID-70","Livox Mid-70","Livox Mid70",[9910,9913,9916,9919,9921,9924],[1510,23,9911,9912,1104],"MCD","large-scale urban, fast ground vehicle",[1038,23,1039,9914,9915],"70 deg circular field of view, non-repeating scan pattern, 100,000 points\u002Fs, range 0.02 to 200 m (typical 1 to 50 m), range accuracy 2 to 5 cm; recorded as a custom ROS message with extra timing","Sec. III-B, IV-A; Fig. 1",[192,952,29,9917,9918],"scanning solid-state LiDAR with Risley prisms, lotus-shaped non-repetitive pattern","Fig. 2b",[1219,53,1220,9920,745],"Livox LiDAR (the paper describes Livox as prism-based, box-shaped, Sec. I); merged with the Ouster stream",[1020,53,1021,9922,9923],"solid-state LiDAR, 10 Hz; described as containing only one scanning line","Sec. IV-A; Fig. 5",[905,23,6005,9925,1333],"70 deg circular FoV, non-repetitive Risley-prism scan, 10 Hz",{"c":944,"m":9927,"d":20,"f":9928,"v":9930,"n":23,"y":233,"u":9932},"Livox Tele",[9929],"Teledyne Optech",[9931],"Teledyne Optech Lynx HS600-D",[9933],[4224,952,29,9934,5838],"vehicle-mounted, 2017, outdoor; 360° FoV camera; 2 Optech sensors, 130 m; IMU and GPS; ±5 cm absolute accuracy (manufacturer)",{"c":33,"m":9927,"d":20,"f":9936,"v":9938,"n":23,"y":152,"u":9940},[9937],"Teledyne\u002FRDI (as written in the paper)",[9939],"Teledyne\u002FRDI Workhorse Navigator Doppler velocity log",[9941],[2111,53,29,9942,332],"1.2 MHz",{"c":108,"m":9927,"d":20,"f":9944,"v":9945,"n":23,"y":122,"u":9946},[3801],[],[9947],[5280,53,6600,569,1104],{"c":944,"m":9949,"d":20,"f":9950,"v":9952,"n":23,"y":100,"u":9953},"Lixel L1 and L2",[9951],"XGRIDS",[],[9954],[9955,952,29,9956,9957],"zhang2024_3dlidarslam_survey","Listed in the survey's commercial catalogue only (sensor class L, I, V), not tested: handheld real-time 3D reconstruction devices combining visual modules, LiDAR, IMU and computer modules; Table 10 row reads 'Lixel L1 and L2, LixelStudio'","Table 10; Sec. 4.4 (LIV solutions)",{"c":353,"m":9959,"d":20,"f":9960,"v":9962,"n":23,"y":132,"u":9963},"LM13",[9961],"RLS",[],[9964],[225,53,226,2377,228],{"c":522,"m":9966,"d":20,"f":9967,"v":9969,"n":23,"y":270,"u":9970},"Logitech QuickCam Pro 9000",[9968],"Logitech",[],[9971],[2407,53,29,9972,530],"1600 x 1200 video resolution; 10 images per camera per 360 deg",{"c":4378,"m":9974,"d":46,"f":9975,"v":9976,"n":23,"y":100,"u":9977},"LongWave InfraRed cameras",[],[],[9978],[1396,53,29,9979,9980],"16-bit raw data exploited in CompSLAM visual-thermal-inertial odometry","Sec. III-B-1",{"c":662,"m":9982,"d":20,"f":9983,"v":9985,"n":23,"y":100,"u":9986},"LORD Microstrain 3DM-GX5-15",[9984],"LORD Microstrain",[],[9987],[1396,53,29,9988,9989],"pitch and roll accuracy 0.4 deg and gyro 0.3 deg per sqrt(h), as quoted by the authors from [112]","Sec. III-G-3",{"c":662,"m":9991,"d":20,"f":9992,"v":9994,"n":23,"y":132,"u":9995},"LORD MicroStrain 3DM-GX5-25",[9993],"LORD MicroStrain",[],[9996],[1296,53,29,9997,7950],"accelerometer +-8 g, 1 kHz; gyroscope +-300 deg\u002Fs, 4 kHz; magnetometer +-2.5 Gauss, 50 Hz; recorded at 500 Hz",{"c":662,"m":9999,"d":20,"f":10000,"v":10002,"n":23,"y":233,"u":10003},"Lord MicroStrain MV5",[10001],"Lord MicroStrain",[],[10004],[3787,53,29,10005,3789],"mounted on the roof of the second machine; 100 Hz",{"c":522,"m":10007,"d":46,"f":10008,"v":10009,"n":23,"y":71,"u":10010},"low-cost IEEE 1394 webcam with a wide-angle lens (model not stated)",[],[],[10011],[74,53,29,10012,10013],"30 Hz; field of view nearly 100 degrees; calibrated at 320x240 with fku = fkv = 195 px; monochrome images used","Sec. 3.2, 3.5, 4",{"c":372,"m":10015,"d":46,"f":10016,"v":10017,"n":23,"y":122,"u":10018},"low-speed wheeled robot",[],[],[10019],[1020,53,1021,10020,9923],"average speed around 1.5 m\u002Fs",{"c":372,"m":10022,"d":46,"f":10023,"v":10024,"n":23,"y":1008,"u":10025},"low-speed wheeled robot (model not reported)",[],[],[10026],[1101,23,1102,10027,10028],"10 sequences, 17.1 km","Sec. IV-A; Table I",{"c":108,"m":10030,"d":20,"f":10031,"v":10032,"n":23,"y":122,"u":10033},"LS-16C (written LS-C16 in Table VI)",[],[],[10034],[5280,23,2577,10035,10036],"inclined side LiDAR","Table I; Table VI",{"c":522,"m":10038,"d":20,"f":10039,"v":10041,"n":23,"y":152,"u":10042},"LUMIX DMC-GX8 with 8mm fisheye lens",[10040],"Panasonic Corp.",[],[10043],[2936,53,29,10044,398],"30.0 fps; fisheye videos of about 6400 (outdoor) and 6700 (indoor) frames",{"c":1689,"m":10046,"d":46,"f":10047,"v":10049,"n":23,"y":100,"u":10050},"Luxonis OAK-D",[10048],"Luxonis",[],[10051],[9592,23,9593,10052,10053],"four stereo camera pairs around the robot, slightly inclined towards the floor; each pair adjusts exposure independently; exposure start triggered via a PTP-to-trigger board; intrinsics and stereo extrinsics calibrated with Kalibr on a 6x6 AprilTag grid (exposure fixed to 1 ms, ISO 200)","Sec. I, Sec. III, Sec. III-B",{"c":18,"m":10055,"d":46,"f":10056,"v":10057,"n":41,"y":299,"u":10058},"MacBook Pro",[],[],[10059,10061],[1433,28,29,10060,127],"2.4 GHz Intel Core i7, 8 GB RAM; CHOLMOD sparse solver",[10062,28,29,10063,10064],"furgale2015ct","2.66 GHz Core 2 Duo, 4 GB of 1067 MHz DDR3 RAM","Sec. 6.3.2",{"c":18,"m":10066,"d":20,"f":10067,"v":10068,"n":23,"y":270,"u":10069},"MacBook Pro (2.7 GHz i7, 16 GB 1600 MHz DDR3 RAM)",[],[],[10070],[9055,28,29,10071,3518],"all three estimators implemented in MATLAB",{"c":18,"m":10073,"d":20,"f":10074,"v":10075,"n":23,"y":270,"u":10076},"MacBook Pro (2012), 2.6 GHz Intel i7",[1644],[],[10077],[10078,28,29,10079,10080],"zlot_bosse2014_mine","MATLAB\u002FMEX processing, not fully optimised","Sec. 4.1, Table I",{"c":18,"m":10082,"d":20,"f":10083,"v":10085,"n":23,"y":233,"u":10086},"MacBook Pro 2013 (i7 at 2.3GHz)",[10084],"Apple (Intel CPU)",[],[10087],[742,28,29,10088,478],"real-time mode without GPU; same machine as in the VI-DSO paper",{"c":18,"m":10090,"d":20,"f":10091,"v":10092,"n":23,"y":122,"u":10093},"MacBook Pro 2021 (emulated Ubuntu 20.04)",[1644],[],[10094],[1279,28,1280,10095,10096],"records and pre-processes all sensor streams with ROS","Sensors and devices",{"c":372,"m":10098,"d":46,"f":10099,"v":10100,"n":23,"y":289,"u":10101},"Magnebike",[],[],[10102],[861,53,29,10103,6092],"two magnetic wheels, 0.006 m3, 0.34 kg, maximum 0.045 m\u002Fs",{"c":372,"m":10105,"d":46,"f":10106,"v":10107,"n":23,"y":132,"u":10108},"manually operated platform",[],[],[10109],[2325,23,2326,10110,10111],"all devices mounted on a manually operated platform; camera on a tripod","Sec. 2.3, Fig. 4",{"c":522,"m":10113,"d":20,"f":10114,"v":10116,"n":23,"y":289,"u":10117},"Matrix Vision BlueCougar-X012b",[10115],"Matrix Vision",[],[10118],[10062,952,29,10119,10120],"CMOS global-shutter camera, 1280 x 960 grayscale at 15 frames per second, synchronized with the rolling-shutter camera","Fig. 9 caption; Sec. 7.1",{"c":522,"m":10122,"d":20,"f":10123,"v":10124,"n":23,"y":289,"u":10125},"Matrix Vision BlueCougar-X102d",[10115],[],[10126],[10062,53,29,10127,10120],"CMOS rolling-shutter camera, 1280 x 960 grayscale at 15 frames per second",{"c":522,"m":10129,"d":20,"f":10130,"v":10131,"n":23,"y":289,"u":10132},"Matrix Vision mvBlueFOX-200wG",[10115],[],[10133],[861,53,29,10134,10135],"camera on each AscTec Firefly; images reconstructed by the ETH Computer Vision and Geometry Group","Sec. 3.1.4; Fig. 3.7",{"c":522,"m":10137,"d":46,"f":10138,"v":10139,"n":23,"y":455,"u":10140},"Matrix-Vision Blue Fox colour camera",[10115],[],[10141],[1000,53,10142,10143,10144],"Sofa-1, Sofa-2","combined with the time-of-flight camera to colour the point clouds","Sec. 7.3.1",{"c":522,"m":10146,"d":20,"f":10147,"v":10149,"n":23,"y":346,"u":10150},"MatrixVision mvBlueFOX-MLC200w",[10148],"MatrixVision",[],[10151],[1078,53,29,10152,3411],"forward-looking global shutter, 752x480, 20 Hz; hand-held indoor suite",{"c":522,"m":10154,"d":20,"f":10155,"v":10156,"n":23,"y":346,"u":10157},"MatrixVision mvBlueFOX-MLC200w with 190-degree fisheye lens",[10148],[],[10158],[1078,53,29,10159,3404],"forward-looking global shutter, 752x480; MEI camera model",{"c":1776,"m":10161,"d":20,"f":10162,"v":10164,"n":23,"y":132,"u":10165},"Matterport Camera v1",[10163],"Matterport",[],[10166],[10167,23,10168,10169,10170],"sun2025nss","Nothing Stands Still (NSS)","tripod-based 360 deg capture from static locations; three RGB-D sensors, pitch range +\u002F-30 deg, 60 deg rotation steps, 18 RGB-D images per location; proprietary registration; geometry error around 1 inch per specification","Sec. 4.1, Sec. 4.2.2",{"c":372,"m":10172,"d":46,"f":10173,"v":10174,"n":23,"y":289,"u":10175},"MAV multicopter (authors' platform)",[],[],[10176],[3751,53,3752,10177,10178],"micro aerial vehicle carrying the rotating 3D laser scanner; visual odometry supplies initial pose estimates","Sec. III, Fig. 2b, Sec. V-B",{"c":6267,"m":10180,"d":20,"f":10181,"v":10183,"n":23,"y":132,"u":10184},"MDEK1001",[10182],"Decawave (ref. 223)",[],[10185],[5348,41,7967,10186,10187],"ultra-wideband positioning accuracy 10 cm, anchors a few centimetres; ground truth available only at 0-30 s and 160-181 s","Sec. 7.1.1; Fig. 18",{"c":944,"m":10189,"d":46,"f":10190,"v":10192,"n":23,"y":299,"u":10193},"MDL DYNASCAN",[10191],"MDL Laser Systems",[],[10194],[1606,952,29,10195,10196],"integrated IMU, RTK GNSS and laser in one pod; range up to 500 m; up to 30 Hz; PRR 36 kHz; accuracy 5 cm; single or dual module; no DMI as standard","Sec. 3.6, Table 3",{"c":108,"m":10198,"d":46,"f":10199,"v":10200,"n":41,"y":122,"u":10201},"mechanical spinning LiDAR (model not named)",[],[],[10202,10206],[6518,23,10203,10204,10205],"MCD dataset","large-scale MCD dataset; segments of tuhh_day_02, tuhh_day_03 and tuhh_day_04","Sec. IV-A2",[2962,23,10207,569,10208],"KITTI, MulRan, New College, Newer College","Abstract; Sec. IV-A",{"c":108,"m":10210,"d":46,"f":10211,"v":10212,"n":23,"y":122,"u":10213},"mechanical spinning LiDARs (models not reported)",[],[],[10214],[7482,23,10215,10216,332],"KITTI odometry, NCLT, Complex Urban","different numbers of scanning lines; 10 frames accumulated per keyframe",{"c":662,"m":10218,"d":46,"f":10219,"v":10220,"n":23,"y":233,"u":10221},"MEMS-based IMU (model not stated)",[],[],[10222],[3818,53,29,569,1667],{"c":372,"m":10224,"d":46,"f":10225,"v":10227,"n":23,"y":233,"u":10228},"Menzi Muck walking excavator (two machines, including HEAP)",[10226],"Menzi Muck",[],[10229],[3787,53,29,569,10230],"Abstract, Sec. II, V",{"c":33,"m":10232,"d":46,"f":10233,"v":10234,"n":23,"y":71,"u":10235},"Meter rule",[],[],[10236],[547,41,29,10237,10238],"reference distance of a closed-loop span (2080 cm) and pose shifts for matchability tests","Sec. 4.3, Fig. 13",{"c":372,"m":10240,"d":46,"f":10241,"v":10242,"n":23,"y":1819,"u":10243},"micro aerial vehicle (EuRoC)",[],[],[10244],[1783,23,1079,10245,469],"flights in two rooms and a large industrial environment",{"c":372,"m":10247,"d":46,"f":10248,"v":10249,"n":23,"y":49,"u":10250},"micro aerial vehicle (MAV)",[],[],[10251],[92,23,1054,10252,10253],"EuRoC video captured from sensors on board","Sec. 4 (EuRoC)",{"c":372,"m":10255,"d":46,"f":10256,"v":10257,"n":23,"y":1819,"u":10258},"micro aerial vehicle (model not reported)",[],[],[10259],[3618,53,3619,10260,3621],"flown in a motion capture room along a commanded circle",{"c":33,"m":10262,"d":46,"f":10263,"v":10265,"n":23,"y":122,"u":10266},"Microsoft AirSim simulated depth camera",[10264],"Microsoft (simulator)",[],[10267],[3444,23,10268,10269,10270],"AirSim synthetic (Urban city, Cluttered field)","depth images with FoV 120 x 80 deg at 640x480, 320x240 and 160x120, unprojected to simulate LiDAR","Sec. VIII-C2, Fig. 7",{"c":1776,"m":10272,"d":20,"f":10273,"v":10274,"n":28,"y":49,"u":10275},"Microsoft Azure Kinect",[1197],[],[10276,10278,10281],[4121,53,29,10277,7981],"time-of-flight depth camera",[7351,53,7352,10279,10280],"RGB-D camera for real-time scanning of the self-collected Azure dataset","Sec. 1, Sec. 4.1, Supp. C",[10282,53,29,10283,10284],"imap2021","hand-held RGB-D camera; frames processed at 10 Hz in the experiments","Fig. 1; Sec. 4.1",{"c":1776,"m":10286,"d":20,"f":10287,"v":10288,"n":2212,"y":299,"u":10289},"Microsoft Kinect",[1197],[],[10290,10292,10294,10298,10301,10303],[7241,23,991,10291,917],"sensor of the TUM real-world indoor sequences",[1807,23,1808,10293,332],"structured light; two Kinect units used in the benchmark",[7000,23,10295,10296,10297],"TUM RGB-D freiburg1_360","hand-held; colored point clouds, 210 million end points","Sec. 3.5.1, Sec. 5.2",[7393,53,29,10299,10300],"near mode; 640x480 depth input; up to 307,200 points per frame; 30 fps input","Sec. 3, Sec. 7, Table 1",[3053,23,991,10302,3056],"30 Hz, 640x480; handheld and robot platforms",[517,23,991,10304,10305],"colour and depth at 30 Hz, 640 x 480; freiburg1_xyz sequence; qualitative result","Sec. 5.6.6",{"c":1776,"m":10307,"d":20,"f":10308,"v":10309,"n":23,"y":1819,"u":10310},"Microsoft Kinect (original)",[1197],[],[10311],[1764,23,9480,569,214],{"c":1776,"m":10313,"d":20,"f":10314,"v":10316,"n":23,"y":1819,"u":10317},"Microsoft Kinect (stripped down)",[10315],"Microsoft (sensor developed by PrimeSense)",[],[10318],[8835,53,29,10319,10320],"640 x 480 RGB-D image at 30 Hz; 115 g when stripped down; mounted at the base of the vehicle tilted slightly down","Sec. 1; Sec. 3; Fig. 1",{"c":662,"m":10322,"d":20,"f":10323,"v":10324,"n":23,"y":142,"u":10325},"Microsoft Kinect built-in accelerometer",[1197],[],[10326],[990,23,991,10327,478],"recorded at 500 Hz via the kinect_aux driver",{"c":1776,"m":10329,"d":20,"f":10330,"v":10331,"n":23,"y":289,"u":10332},"Microsoft Kinect for XBOX 360",[1197],[],[10333],[1648,53,29,10334,257],"colour and disparity images at 640x480 (teddy sequence)",{"c":1776,"m":10336,"d":20,"f":10337,"v":10338,"n":23,"y":1819,"u":10339},"Microsoft Kinect RGB-D camera",[1197],[],[10340],[3618,23,991,10341,10342],"images of worse quality than the VI-Sensor (rolling shutter, motion blur); fr2_desk 18.8 m and fr2_xyz 7 m trajectories; SVO is not among the methods marked as using the depth sensor in Table IV","Sec. XI-B-2, Table IV",{"c":1776,"m":10344,"d":20,"f":10345,"v":10346,"n":952,"y":1819,"u":10351},"Microsoft Kinect v2",[1197],[10347,10348,10349,10350],"Kinect 2","Kinect v2","Kinect v2 (nine units in an OpenPTrack camera network)","Kinect2",[10352,10355,10357,10361],[1957,952,29,10353,10354],"cover about 2 x 20 m2 of corridor; calibrated with OpenPTrack and registered to the map by ICP","Sec. Comparison with a static sensor-based people tracking system; Fig. 14",[5610,53,29,10356,1325],"RGB 1920x1080; IR depth 512x424; up to 30 Hz; depth FOV 70 deg horizontal, 60 deg vertical; useful range 0.8 to 4.5 m; depth accuracy 5 mm at centre with border deviation rising from 3 mm at 0.8 m to 16 mm at 3 m; depth accuracy 21 mm used in Sec. 4.3; A(0.8 m) = 8 mm and A(3 m) = 21 mm with linear propagation in Sec. 5.2",[8574,23,10358,10359,10360],"CoRBS","pre-registered CoRBS sequences (sensor named in the title of reference [46])","Sec. 5; ref. [46]",[1833,53,1834,10362,10363],"Sampling rate 10 Hz; resolution 512x424 pixels; horizontal 70 deg, vertical 60 deg","Table I; Sec. IV-A; Fig. 4",{"c":18,"m":10365,"d":20,"f":10366,"v":10367,"n":23,"y":346,"u":10368},"Microsoft Surface Pro tablet",[1197],[],[10369],[1825,28,29,10370,10371],"portable tablet; FlashFusion runs on its CPU for live scanning","Sec. V; footnote 1",{"c":33,"m":10373,"d":20,"f":10374,"v":10375,"n":23,"y":346,"u":10376},"Microsoft Xbox controller",[1197],[],[10377],[2739,53,29,10378,1924],"connects to the Control Module; used to drive the Husky manually for initial mapping of an environment",{"c":1776,"m":10380,"d":20,"f":10381,"v":10382,"n":23,"y":142,"u":10383},"Microsoft Xbox Kinect",[1197],[],[10384],[990,23,991,10385,10386],"colour and depth 640 x 480 at 30 Hz; two different units (one for fr1, one for fr2); depth registered to colour by the OpenNI driver; depth scale correction 1.035 (fr1) and 1.031 (fr2)","Abstract, Sec. IV, Table II",{"c":662,"m":10388,"d":20,"f":10389,"v":10391,"n":23,"y":132,"u":10392},"MicroStrain 3DM-CX5-AHRS",[10390],"MicroStrain (Williston, VT, USA)",[],[10393],[5348,23,5349,10394,7928],"IMU up to 1000 Hz, EKF up to 500 Hz; ±0.25° RMS roll and pitch, ±0.8° RMS heading; resolution \u003C0.01°; -40 °C to +85 °C",{"c":662,"m":10396,"d":20,"f":10397,"v":10399,"n":28,"y":142,"u":10400},"MicroStrain 3DM-GX2",[10398],"MicroStrain",[],[10401,10403,10404],[467,53,29,10402,3853],"industrial-grade, triaxial MEMS gyros and accelerometers, 100 Hz output, rotational rate range of at least 600 deg\u002Fs (nonstandard option), 41 x 63 x 32 mm, 50 g",[10062,53,29,569,6318],[10078,53,29,10405,10406],"industrial-grade MEMS IMU fixed to the non-spinning part of the mount; raw rates used with own bias estimates; accelerometer biases not corrected","Sec. 3.1, 4.1, 4.4",{"c":662,"m":10408,"d":20,"f":10409,"v":10411,"n":28,"y":142,"u":10413},"Microstrain 3DM-GX3",[10398,10410],"Microstrain",[10412],"MicroStrain 3DM-GX3",[10414,10416,10417],[467,53,29,10415,3518],"used in the second-generation handheld Zebedee, mounted on the back of the laser",[3868,53,29,569,4496],[2493,53,29,10418,2494],"used in both hand-held payloads",{"c":662,"m":10420,"d":20,"f":10421,"v":10422,"n":23,"y":346,"u":10423},"Microstrain 3DM-GX3-25",[10410],[],[10424],[1091,53,29,10425,5457],"attached to the bottom of the LiDAR, 500 Hz",{"c":662,"m":10427,"d":20,"f":10428,"v":10429,"n":23,"y":1819,"u":10430},"Microstrain 3DM-GX3-45",[10410],[],[10431],[5093,53,29,569,10432],"Sec. 7.3",{"c":662,"m":10434,"d":20,"f":10435,"v":10437,"n":952,"y":318,"u":10439},"MicroStrain 3DM-GX5-25",[10398,10436,10410],"MicroStrain (as named)",[10438],"Microstrain 3DM-GX5-25",[10440,10442,10443,10444],[321,23,322,10441,324],"orientation source replacing wheel-odometry orientation in platform odometry",[2758,53,29,569,478],[2785,53,29,569,898],[378,23,10445,10446,9835],"LIO-SAM dataset (liosam)","9-axis, 1000 Hz",{"c":662,"m":10448,"d":20,"f":10449,"v":10450,"n":23,"y":132,"u":10451},"Microstrain 3DM-GX5-AHRS",[10410],[],[10452],[1904,53,29,569,530],{"c":662,"m":10454,"d":46,"f":10455,"v":10456,"n":41,"y":152,"u":10458},"MicroStrain IMU (model not reported)",[10398,10410],[10457],"Microstrain IMU (model not reported)",[10459,10461],[781,53,29,569,10460],"Sec. 3.1; Fig. 2(a)",[3116,53,29,2377,332],{"c":662,"m":10463,"d":20,"f":10464,"v":10465,"n":23,"y":233,"u":10466},"Microstrain MS25",[10398],[],[10467],[378,23,600,10468,9835],"9-axis, 50 Hz (interpolated to 100 Hz for LIO-SAM)",{"c":108,"m":10470,"d":20,"f":10471,"v":10472,"n":23,"y":122,"u":10473},"MID-100",[],[],[10474],[201,53,10475,10476,3126],"multi-lidar calibration data of [8] and [9]","three internal lidars L0, L1, L2 with 8.4 deg FoV overlap between adjacent ones; in-factory extrinsics used as ground truth",{"c":372,"m":10478,"d":46,"f":10479,"v":10480,"n":23,"y":1819,"u":10481},"mine elevator car roof (Timo's Shaft, Pyhasalmi Mine)",[],[],[10482],[4669,53,29,10483,10484],"elevator speed regulated to 1 m\u002Fs; shaft about 1440 m long, 5 m diameter; about 25 min down and 25 min up","Sec. 3, Figs. 2-3",{"c":33,"m":10486,"d":46,"f":10487,"v":10488,"n":23,"y":270,"u":10489},"mine survey profile (apparatus not reported)",[],[],[10490],[10078,41,29,10491,1176],"17,942 points near floor level at about 1 m spacing, date and method unknown; converted to 35,612 surfels and used as fixed registration targets",{"c":372,"m":10493,"d":46,"f":10494,"v":10495,"n":23,"y":233,"u":10496},"mine utility vehicle (mine buggy)",[],[],[10497],[3818,53,29,10498,10499],"MLS mounted for forward and backward passes at about 10 km\u002Fh (target level) and about 5 km\u002Fh (convergence surveys)","Sec. 3.3, Table 4",{"c":372,"m":10501,"d":46,"f":10502,"v":10503,"n":23,"y":357,"u":10504},"MinesRover",[],[],[10505],[360,53,29,10506,10507],"six wheels: four driving and steering, two free odometry wheels; rocker-bogie; 14.8 V 4.1 Ah LiPo; four 45 W motors; top speed 3 m\u002Fs","Sec. III; Figs. 1-2",{"c":18,"m":10509,"d":46,"f":10510,"v":10511,"n":23,"y":122,"u":10512},"Mini-PC (model not reported)",[],[],[10513],[4780,53,29,10514,4782],"mounted with the 3D LiDAR and batteries in the portable mapping system (Sec. 4.2, Fig. 6); no runtime or processing hardware reported",{"c":18,"m":10516,"d":20,"f":10517,"v":10518,"n":23,"y":1008,"u":10519},"Mini-PC with Intel Core i9-12900",[6867],[],[10520],[2429,28,29,10521,313],"CPU only, no GPU acceleration; integrated with the sensor suite",{"c":18,"m":10523,"d":20,"f":10524,"v":10525,"n":23,"y":3141,"u":10526},"MIPS R4400",[],[],[10527],[3144,28,29,10528,3364],"250 MHz processor",{"c":372,"m":10530,"d":46,"f":10531,"v":10532,"n":23,"y":357,"u":10533},"MIT DARPA Urban Challenge vehicle",[],[],[10534],[1615,53,29,10535,1617],"vehicle on which the algorithm was originally developed and used",{"c":372,"m":10537,"d":46,"f":10538,"v":10539,"n":23,"y":132,"u":10540},"mobile chassis",[],[],[10541],[1136,53,29,10542,10543],"carries the sensor suite and Jetson Orin NX","Fig. 8(e) arXiv; Fig. 12(e) T-RO",{"c":18,"m":10545,"d":20,"f":10546,"v":10547,"n":23,"y":299,"u":10548},"mobile core-i7 CPU",[],[],[10549],[9201,28,29,10550,3677],"about 1 hour for the scripted full-scene M3C2 comparison",{"c":18,"m":10552,"d":46,"f":10553,"v":10554,"n":23,"y":49,"u":10555},"mobile Intel CPUs typically found on robots (models not reported)",[98],[],[10556],[9086,28,29,10557,10558],"real-time mapping of spaces well over 100,000 ft2","Features",{"c":944,"m":10560,"d":46,"f":10561,"v":10563,"n":23,"y":49,"u":10564},"Mobile Measurement System",[10562],"PASCO Corporation",[],[10565],[8484,53,29,10566,10567],"mobile platform with a 3D LIDAR, rangefinders, GNSS and cameras","Sec. IV.A; Fig. 4",{"c":522,"m":10569,"d":46,"f":10570,"v":10571,"n":23,"y":100,"u":10572},"mobile phones (models not named)",[],[],[10573],[5177,23,5178,10574,3230],"824 daytime and 98 nighttime Aachen query images",{"c":372,"m":10576,"d":46,"f":10577,"v":10578,"n":23,"y":554,"u":10579},"mobile robot (Leibniz Universität Hannover; type not stated)",[],[],[10580],[557,23,10581,10582,275],"Hannover (Leibniz Universität Hannover robot run, about 750 m)","robot run of about 750 m",{"c":372,"m":10584,"d":46,"f":10585,"v":10586,"n":23,"y":270,"u":10587},"mobile robot (model not reported)",[],[],[10588],[9055,23,9056,10589,3518],"indoor, planar environment",{"c":372,"m":10591,"d":46,"f":10592,"v":10593,"n":23,"y":49,"u":10594},"mobile robot platform (Fraunhofer IOSB)",[],[],[10595],[1938,53,10596,10597,10598],"FR-IOSB","carries the Livox-Xsens suite and the HDL-64E with MTi-G-700","Sec. 5.3.1; Fig. 7; Acknowledgment",{"c":372,"m":10600,"d":46,"f":10601,"v":10602,"n":23,"y":10603,"u":10604},"mobile vehicle (unnamed)",[],[],1994,[10605],[10606,53,29,10607,10608],"zhang1994icp","carries the trinocular stereo system; context of autonomous navigation in rugged terrain","Sec. 1; Sec. 5.3",{"c":522,"m":10610,"d":46,"f":10611,"v":10612,"n":23,"y":122,"u":10613},"monochrome fisheye cameras (model not named)",[],[],[10614],[1349,23,815,10615,745],"reason NICE-SLAM could not be run on Newer College",{"c":522,"m":10617,"d":46,"f":10618,"v":10619,"n":28,"y":346,"u":10620},"monocular camera (model not reported)",[],[],[10621,10624,10626],[2739,53,29,10622,10623],"input for ORB-SLAM and ENet (512 x 256 segmentation input)","Sec. 1, Sec. 4.2.1",[3237,53,29,10625,5829],"time-synchronized with IMU; feeds ROVIO visual-inertial odometry",[2483,23,3771,10627,469],"640 x 480",{"c":522,"m":10629,"d":46,"f":10630,"v":10631,"n":23,"y":122,"u":10632},"monocular cameras (two)",[],[],[10633],[164,53,184,185,167],{"c":522,"m":10635,"d":46,"f":10636,"v":10637,"n":23,"y":132,"u":10638},"monocular RGB camera (model not named)",[],[],[10639],[3211,53,10640,10641,10642],"TUM RGB-D; 7-Scenes; ETH3D-SLAM; EuRoC","monocular RGB input with no parametric camera model assumed beyond a unique camera centre; EuRoC images undistorted for the uncalibrated run","Abstract; Sec. 3.1; Sec. 4; Sec. 4.1",{"c":522,"m":10644,"d":46,"f":10645,"v":10646,"n":23,"y":100,"u":10647},"monocular RGB camera (model not stated in paper)",[],[],[10648],[646,23,647,10649,649],"intrinsic calibration with a checkerboard, overall mean reprojection error 0.22 px; camera-IMU extrinsics calibrated with the method of ref. [13] (Furgale et al. 2013)",{"c":33,"m":10651,"d":46,"f":10652,"v":10653,"n":23,"y":100,"u":10654},"motion capture (MoCap) system",[],[],[10655],[905,41,6005,10656,332],"alternative ground truth source",{"c":33,"m":10658,"d":46,"f":10659,"v":10660,"n":2256,"y":289,"u":10661},"motion capture system",[],[],[10662,10665,10668,10671,10673],[282,41,212,10663,10664],"accurate ground truth for NTU VIRAL","Sec. 4.5",[2452,41,10666,10667,332],"self-collected mocap sequences (Smooth, Violent, Hybrid)","ground truth at 120 Hz, millimeter-level accuracy",[164,41,10669,10670,167],"CLIC Vicon Room dataset (authors)","ground truth for the indoor Vicon Room dataset",[1155,41,3436,10672,332],"ground truth for nine sequences",[3751,41,3752,10674,10675],"provides ground-truth poses for the indoor training and test datasets","Sec. IV, Sec. V",{"c":33,"m":10677,"d":46,"f":10678,"v":10679,"n":41,"y":132,"u":10680},"motion capture system (MoCap)",[],[],[10681,10684],[1136,41,10682,10683,717],"proprietary Playground sequences","ground truth for small indoor sequences; tracker-odometry alignment calibrated",[216,41,5988,10685,157],"millimeter-accurate ground truth; not public, scored online",{"c":33,"m":10687,"d":46,"f":10688,"v":10689,"n":23,"y":100,"u":10690},"motion capture system (model not named)",[],[],[10691],[507,41,10692,10693,4640],"Hilti-21","reference trajectories for some sequences",{"c":33,"m":10695,"d":46,"f":10696,"v":10697,"n":8521,"y":289,"u":10699},"motion capture system (model not reported)",[],[10698],"Motion capture system (model not reported)",[10700,10702,10704,10707,10710,10712,10714,10716],[3947,41,1054,10701,332],"6-DoF ground truth for the V sequences",[7241,41,991,10703,917],"ground-truth trajectories of TUM",[3618,41,991,10705,10706],"ground truth for TUM RGB-D","Sec. XI-B-2",[8835,41,29,10708,10709],"120 Hz ground truth of MAV position and attitude; room about 11 m x 7 m x 4 m","Sec. 4.1; Fig. 4",[3245,41,8529,10711,3518],"ground-truth poses of the Freiburg dataset",[201,41,5524,10713,1204],"ground-truth trajectory for other Hilti sequences",[1890,41,991,10715,917],"provides TUM RGB-D ground-truth poses",[678,41,679,10717,10718],"infrared cameras tracking three markers around the prism for IMU-prism hand-eye calibration","Sec. 4.2; Fig. 6 (VoR)",{"c":33,"m":10720,"d":46,"f":10721,"v":10722,"n":23,"y":318,"u":10723},"Motion capture system (model not stated)",[],[],[10724],[2501,41,29,10725,10726],"rotation reference; Euler angles compared","Sec. V-B, Fig. 10",{"c":33,"m":10728,"d":46,"f":10729,"v":10730,"n":23,"y":289,"u":10731},"motion capture system (TUM RGB-D ground truth, model not stated)",[],[],[10732],[2830,41,991,10733,398],"synchronised ground-truth poses",{"c":33,"m":10735,"d":46,"f":10736,"v":10737,"n":23,"y":152,"u":10738},"motion capture system with reflective markers",[],[],[10739],[4789,41,29,10740,2153],"provides ground-truth poses for the handheld sequences",{"c":372,"m":10742,"d":46,"f":10743,"v":10744,"n":23,"y":3141,"u":10745},"motion control platform (model not reported)",[],[],[10746],[3144,53,29,10747,3364],"used to reposition objects automatically between scans",{"c":33,"m":10749,"d":46,"f":10750,"v":10751,"n":23,"y":1008,"u":10752},"motion-capture system with eight high-speed cameras",[],[],[10753],[3053,41,991,10754,3056],"100 Hz; provides ground-truth trajectories",{"c":33,"m":10756,"d":46,"f":10757,"v":10759,"n":23,"y":142,"u":10760},"MotionAnalysis Raptor-E motion capture (eight cameras)",[10758],"MotionAnalysis",[],[10761],[990,41,991,10762,10763],"camera resolution 1280 x 1024 at up to 300 Hz; ground-truth poses at 100 Hz; calibrated with Cortex software; four reflective markers per Kinect","Abstract, Sec. IV, Sec. VI-A, Sec. VI-C",{"c":33,"m":10765,"d":46,"f":10766,"v":10767,"n":23,"y":1421,"u":10768},"motor (model not reported)",[],[],[10769],[3860,53,29,10770,10771],"rotates the laser scanner back and forth between -90 and 90 deg at 180 deg\u002Fs average; one 180 deg sweep lasts 1 s","Sec. V-A; Sec. V-B2",{"c":33,"m":10773,"d":46,"f":10774,"v":10775,"n":23,"y":289,"u":10776},"motor and encoder actuating the scanner",[],[],[10777],[5332,53,29,10778,10779],"rotates back-and-forth at 180 deg\u002Fs between -90 and 90 deg; encoder resolution 0.25 deg; one sweep lasts 1 s","Sec. IV, Sec. VII, Fig. 8",{"c":522,"m":10781,"d":20,"f":10782,"v":10784,"n":23,"y":152,"u":10785},"Motorola G 1st generation smartphone camera",[10783],"Motorola",[],[10786],[1174,53,29,10787,10788],"640 x 480, 30 fps, FOV 56.32 deg, sensor 3.63 x 2.72 mm; calibrated beforehand","Sec. 4.1.2; Table 2",{"c":662,"m":10790,"d":20,"f":10791,"v":10793,"n":23,"y":233,"u":10794},"MPU-9150",[10792],"InvenSense (per ref. [17])",[],[10795],[752,53,29,10796,10797],"1 kHz sample rate stabilized by an external quartz reference clock from the MCU; data-ready interrupt used for timestamping; interrupt delay documented with 10 us precision","Sec. II, Sec. IV",{"c":662,"m":10799,"d":20,"f":10800,"v":10801,"n":23,"y":132,"u":10802},"MPU6150 (inside DAVIS346)",[],[],[10803],[678,23,679,10804,1374],"6-axis MEMS, 1000 Hz",{"c":662,"m":10806,"d":20,"f":10807,"v":10808,"n":23,"y":233,"u":10809},"MPU6150 (Table II) \u002F MPU6050 (Sec. III-B3)",[],[],[10810],[1562,23,1563,10811,10812],"internal to each DAVIS346; 1000 Hz, 6-axis MEMS, intrinsic calibrated; some missing measurements linearly interpolated","Table II; Sec. III-B3; Sec. IV-C",{"c":18,"m":10814,"d":20,"f":10815,"v":10817,"n":23,"y":152,"u":10818},"MSI Geforce GTX 1080 Gaming X 8G",[10816],"MSI",[],[10819],[1795,28,29,10820,6936],"BA implemented on the GPU with CUDA 8.0",{"c":1689,"m":10822,"d":20,"f":10823,"v":10824,"n":23,"y":1421,"u":10825},"MT9V034",[],[],[10826],[1053,23,1054,10827,10828],"2 x 20 Hz, WVGA, global shutter, monochrome; independent auto-exposure per camera","Table 1, Sec. 2, Sec. 6",{"c":108,"m":10830,"d":20,"f":10831,"v":10832,"n":23,"y":1008,"u":10833},"multibeam 3-D LiDAR",[],[],[10834],[3053,23,815,10835,3056],"2.2 km trajectory",{"c":372,"m":10837,"d":46,"f":10838,"v":10839,"n":23,"y":289,"u":10840},"multirotor UAV (model not reported)",[],[],[10841],[1050,53,29,10842,3518],"forward-oriented visual-inertial sensor; filter output used for feedback control",{"c":1689,"m":10844,"d":46,"f":10845,"v":10846,"n":23,"y":318,"u":10847},"Multisense SL",[2618],[],[10848],[688,53,6121,10849,10850],"tri-modal ruggedized sensor; stereo 10 Hz, 1024 x 1024 px, 80 x 80 deg FoV (CMV4000 imager) on Atlas; used on Atlas, Valkyrie and HyQ","Table 1; Sec. 6",{"c":522,"m":10852,"d":20,"f":10853,"v":10855,"n":952,"y":49,"u":10856},"MV-CA013-21UC",[10854],"Hikrobot (per footnote link)",[],[10857,10859,10861,10864,10866],[2054,23,2121,10858,1104],"global shutter, 1280 x 1024, FoV 72 x 60 deg, hardware-synchronized",[2054,53,29,10860,2140],"global shutter, 1280 x 1024, FoV 72 x 60 deg",[7952,53,29,10862,10863],"industrial camera","Fig. 10; Sec. IV-C",[3255,53,3509,10865,9756],"two industrial cameras (left and right)",[216,53,3513,10867,9760],"industrial camera, FoV 70.6 x 68.5 deg; fixed exposure with auto gain in most sequences",{"c":522,"m":10869,"d":20,"f":10870,"v":10871,"n":23,"y":1008,"u":10872},"MV-CA013-21UC visible-light camera",[],[],[10873],[2366,23,10874,569,332],"authors' private thermal dataset (self-collected rig)",{"c":522,"m":10876,"d":20,"f":10877,"v":10878,"n":23,"y":49,"u":10879},"MV-CE060-10UC",[],[],[10880],[1164,53,6758,10881,10882],"rolling shutter camera; outputs at each 10 Hz trigger; 1520 x 568 images used","Sec. III-A; Table II",{"c":4378,"m":10884,"d":20,"f":10885,"v":10886,"n":23,"y":1008,"u":10887},"MV-CI003-GL-N15 long-wave thermal imager",[],[],[10888],[2366,53,29,10889,10890],"10 Hz (Fig. 2); rendering evaluated at 640 x 512 per frame (Sec. IV-C); native imager resolution not stated","Sec. IV-A; Sec. IV-C; Fig. 2",{"c":1689,"m":10892,"d":20,"f":10893,"v":10894,"n":23,"y":152,"u":10895},"mvBlueFOX-MLC200w",[],[],[10896],[1083,53,29,10897,3390],"stereo cameras, 20 Hz, self-developed hand-held sensor suite",{"c":1689,"m":10899,"d":20,"f":10900,"v":10901,"n":23,"y":233,"u":10902},"Mynak D1000-IR-120 color binocular camera",[],[],[10903],[4511,23,8773,10904,478],"left camera images at 640 x 480 used in the experiment (their role is not stated)",{"c":1689,"m":10906,"d":46,"f":10907,"v":10908,"n":23,"y":1819,"u":10909},"narrow-baseline grayscale stereo sensor (model not named)",[],[],[10910],[1764,23,9483,569,214],{"c":522,"m":10912,"d":46,"f":10913,"v":10914,"n":23,"y":346,"u":10915},"navigation camera (model not reported)",[],[],[10916],[2316,53,29,10917,917],"navigation view; not used for mapping",{"c":522,"m":10919,"d":46,"f":10920,"v":10921,"n":23,"y":152,"u":10922},"navigation view camera (model not reported)",[],[],[10923],[431,53,29,10924,441],"navigation only",{"c":882,"m":10926,"d":20,"f":10927,"v":10929,"n":23,"y":132,"u":10930},"Navtech CIR304-H",[10928],"Navtech",[],[10931],[1588,23,1589,10932,10933],"mechanical spinning 2D radar, 360 deg horizontal FOV, 1600 Hz azimuth measurements; no Doppler output","Sec. IV-D; Fig. 14",{"c":944,"m":10935,"d":20,"f":10936,"v":10938,"n":28,"y":318,"u":10939},"NavVis M6",[10937],"NavVis",[],[10940,10942,10945],[4224,952,29,10941,4226],"trolley, 2018, indoor; 360° FoV camera; 6 Velodyne Puck LITE, 100 m; IMU yes, GPS no; 0.57 cm absolute accuracy at 68% confidence, 1.38 cm at 95% (manufacturer)",[951,952,29,10943,10944],"trolley with low-cost 3D LiDAR plus 2D LiDARs used to raise point density; 40 kg; 343,200 pts\u002Fs; relative accuracy 3-5 cm (2D) and 3 cm (3D); 3.5 h; 6 cameras, 96 Mp","Sec. 2.3.2; Tables 1-6; Fig. 7",[6069,41,6070,10946,157],"trajectory estimated by the NavVis system through hours of offline fusion is used as ground truth",{"c":662,"m":10948,"d":46,"f":10949,"v":10950,"n":23,"y":49,"u":10951},"NavVis M6 IMU (model not reported)",[],[],[10952],[6069,41,6070,10953,157],"used by NavVis for its reference trajectory",{"c":944,"m":10955,"d":20,"f":10956,"v":10957,"n":23,"y":318,"u":10958},"NavVis Trolley",[10937],[],[10959],[4124,23,10960,10961,362],"not stated (Fig. 2 example)","pushing-cart system; named only in the Fig. 2 caption as the source of the illustrated data",{"c":944,"m":10963,"d":20,"f":10964,"v":10965,"n":23,"y":233,"u":10966},"NavVis VLX",[10937],[],[10967],[4224,952,29,10968,4689],"wearable, 2021, indoor and outdoor; 360° FoV camera; dual Velodyne Puck LITE, 100 m; IMU yes, GPS no; 0.6 cm absolute accuracy at 68% confidence, 1.5 cm at 95% (manufacturer)",{"c":944,"m":10970,"d":20,"f":10971,"v":10972,"n":23,"y":132,"u":10973},"NavVis VLX 2",[10937],[],[10974],[3843,952,29,10975,4242],"wearable frame on shoulders and stomach; two 16-channel sensors, 903 nm, FoV 360 x 360 deg, 2 x 0.3 Mpts\u002Fs in Table 1 (2 x 600,000 pts\u002Fs in text); 4 cameras of 20 MP; 8.7 kg; declared 6 mm in a 500 m2 area",{"c":1689,"m":10977,"d":46,"f":10978,"v":10979,"n":23,"y":289,"u":10980},"NewCollege stereo camera (model not stated)",[],[],[10981],[2595,23,10982,10983,157],"NewCollege","20 fps, 512x382; processed as monocular input",{"c":372,"m":10985,"d":46,"f":10986,"v":10987,"n":23,"y":289,"u":10988},"NiftiBot",[],[],[10989],[861,53,29,10990,10991],"tracked bogies with flippers, 0.17 m3, about 20 kg, 0.3 m\u002Fs","Sec. 3.1; Table 3.7",{"c":662,"m":10993,"d":46,"f":10994,"v":10995,"n":23,"y":1008,"u":10996},"nine-axis IMU",[],[],[10997],[2865,23,10998,10999,11000],"authors' simulation and real-site rosbags","200 Hz; mounted 0.15 m below the LiDAR; recorded but not used by the LiDAR-only method","Sec. 3.2, Sec. 4.2, Sec. 5.1",{"c":662,"m":11002,"d":46,"f":11003,"v":11004,"n":23,"y":132,"u":11005},"nine-axis IMU (model not stated)",[],[],[11006],[2447,53,2448,11007,11008],"3-axis acceleration, angular velocity and magnetometer at 500 Hz; 0.15 m below the LiDAR on a rubber damping pad","Sec. 3.1(3)",{"c":372,"m":11010,"d":46,"f":11011,"v":11012,"n":23,"y":385,"u":11013},"Nomad Scout",[],[],[11014],[388,53,29,569,31],{"c":108,"m":11016,"d":46,"f":11017,"v":11018,"n":23,"y":233,"u":11019},"not_reported (32-ray LiDAR)",[],[],[11020],[5118,23,11021,11022,11023],"NAVER LABS (Pangyo)","32-ray, full HFOV","Table III; Sec. VII-G",{"c":108,"m":11025,"d":46,"f":11026,"v":11027,"n":23,"y":100,"u":11028},"not_reported (3D LiDAR of the ConSLAM handheld system)",[],[],[11029],[5563,53,1280,11030,11031],"scans deskewed with DLIO; rosbags replayed at half speed; method assumes 360-deg horizontal FoV","Sec. 5.1, 5.2.2, 8",{"c":662,"m":11033,"d":46,"f":11034,"v":11035,"n":23,"y":100,"u":11036},"not_reported (9-axis IMU of the ConSLAM handheld system)",[],[],[11037],[5563,53,1280,11038,398],"9-axis; LiDAR-IMU extrinsics estimated with OA-LICalib",{"c":662,"m":11040,"d":46,"f":11041,"v":11042,"n":23,"y":122,"u":11043},"not_reported (built-in IMU of the Livox AVIA)",[],[],[11044],[7639,53,29,569,478],{"c":522,"m":11046,"d":46,"f":11047,"v":11048,"n":23,"y":1008,"u":11049},"not_reported (camera in ground-truth fusion pipeline)",[],[],[11050],[1833,41,1834,569,332],{"c":522,"m":11052,"d":46,"f":11053,"v":11054,"n":23,"y":1421,"u":11055},"not_reported (dataset images used for the GIST visual baseline)",[],[],[11056],[9016,23,11057,569,332],"KITTI; Ford Campus",{"c":33,"m":11059,"d":46,"f":11060,"v":11061,"n":23,"y":100,"u":11062},"not_reported (engineers' on-site measurement of 3-4 structural landmarks as GCPs)",[],[],[11063],[8155,41,29,11064,11065],"GCPs at different heights about 20 m apart; used with CloudCompare v2.13 alpha to build the reference registration","Sec. 4.2.1",{"c":33,"m":11067,"d":46,"f":11068,"v":11069,"n":23,"y":100,"u":11070},"not_reported (external motion capture system)",[],[],[11071],[5748,41,5749,11072,11073],"high-frequency millimeter-accurate ground-truth poses for the LiDAR sensor (as stated)","Sec. 4.3, Sec. 5",{"c":108,"m":11075,"d":46,"f":11076,"v":11077,"n":23,"y":1421,"u":11078},"not_reported (Ford Campus lidar data, 3817 point clouds)",[],[],[11079],[9016,23,925,569,332],{"c":108,"m":11081,"d":46,"f":11082,"v":11083,"n":23,"y":1421,"u":11084},"not_reported (Freiburg Campus lidar data, 77 point clouds)",[],[],[11085],[9016,23,11086,569,332],"Freiburg Campus",{"c":18,"m":11088,"d":46,"f":11089,"v":11090,"n":23,"y":152,"u":11091},"not_reported (GPU running the CUDA direct-alignment kernel)",[],[],[11092],[1063,28,29,569,5221],{"c":108,"m":11094,"d":46,"f":11095,"v":11096,"n":23,"y":122,"u":11097},"not_reported (hand-carried LiDAR)",[],[],[11098],[803,23,815,11099,398],"hand-carried sequence at Oxford University with motion distortion; ground-truth trajectories and mesh provided by the dataset",{"c":108,"m":11101,"d":46,"f":11102,"v":11103,"n":23,"y":233,"u":11104},"not_reported (hand-held LiDAR of the LiLi-OM KA Urban Campus 1 sequence)",[],[],[11105],[5118,23,11106,11107,11108],"LiLi-OM KA Urban Campus 1","narrow front horizontal FOV about 70 deg","Sec. VIII-D",{"c":944,"m":11110,"d":46,"f":11111,"v":11112,"n":23,"y":100,"u":11113},"not_reported (handheld and backpack laser scanners)",[],[],[11114],[8155,23,9211,569,11115],"Sec. 4.1.1",{"c":372,"m":11117,"d":46,"f":11118,"v":11119,"n":23,"y":100,"u":11120},"not_reported (handheld unit)",[],[],[11121],[5748,23,5749,11122,11123],"handheld; relatively fast motions and rotations (Exp06 max angular velocity 263.522 deg\u002Fs, max linear velocity 2.408 m\u002Fs)","Sec. 5, Table 1",{"c":662,"m":11125,"d":46,"f":11126,"v":11127,"n":23,"y":1008,"u":11128},"not_reported (IMU in ground-truth fusion pipeline)",[],[],[11129],[1833,41,1834,569,332],{"c":662,"m":11131,"d":46,"f":11132,"v":11133,"n":23,"y":100,"u":11134},"not_reported (IMU of the dataset handheld unit)",[],[],[11135],[5748,23,5749,11136,441],"raw accelerations and angular velocities; orientation estimates generated with the Madgwick IMU filter; IMU noise parameters set from device specification sheets",{"c":662,"m":11138,"d":46,"f":11139,"v":11140,"n":23,"y":49,"u":11141},"not_reported (IMU, model not named)",[],[],[11142],[11143,53,29,11144,56],"blum2021precisebim","used for smooth state estimation and camera-IMU calibration",{"c":108,"m":11146,"d":46,"f":11147,"v":11148,"n":23,"y":1421,"u":11149},"not_reported (KITTI lidar sequences 00, 05, 06, 07)",[],[],[11150],[9016,23,842,569,332],{"c":108,"m":11152,"d":46,"f":11153,"v":11154,"n":23,"y":318,"u":11155},"not_reported (KITTI LiDAR, 64 rays, 27 deg vertical FOV)",[],[],[11156],[11157,23,11158,11159,332],"removert2020","KITTI odometry; SemanticKITTI","vertical FOV 27 deg, 64 rays",{"c":108,"m":11161,"d":46,"f":11162,"v":11163,"n":23,"y":1008,"u":11164},"not_reported (LiDAR in ground-truth fusion pipeline)",[],[],[11165],[1833,41,1834,569,332],{"c":522,"m":11167,"d":46,"f":11168,"v":11169,"n":23,"y":100,"u":11170},"not_reported (monocular camera of the dataset)",[],[],[11171],[5748,23,5749,11172,441],"monocular images only; not used because of scale ambiguity",{"c":522,"m":11174,"d":46,"f":11175,"v":11176,"n":23,"y":132,"u":11177},"not_reported (monocular cameras for custom office-loop, tabletop and bollards scenes)",[],[],[11178],[11179,53,29,11180,11181],"vggtslam2025","A single camera per scene, different scenes may use different cameras; intrinsics unknown to the method","Sec. 5.5; Appendix B.3; Appendix C.1",{"c":108,"m":11183,"d":46,"f":11184,"v":11185,"n":23,"y":318,"u":11186},"not_reported (MulRan LiDAR, 45 deg vertical FOV)",[],[],[11187],[11157,23,11188,11189,11190],"MulRan (KAIST 02)","vertical FOV 45 deg","Sec. IV-B, Fig. 7",{"c":522,"m":11192,"d":46,"f":11193,"v":11194,"n":23,"y":100,"u":11195},"not_reported (near-infrared camera of the ConSLAM handheld system)",[],[],[11196],[5563,23,1280,11197,398],"not_reported; not used by SLAM2REF",{"c":108,"m":11199,"d":46,"f":11200,"v":11201,"n":23,"y":49,"u":11202},"not_reported (one LiDAR, model not named)",[],[],[11203],[11143,53,29,569,56],{"c":522,"m":11205,"d":46,"f":11206,"v":11207,"n":23,"y":100,"u":11208},"not_reported (RGB camera of the ConSLAM handheld system)",[],[],[11209],[5563,23,1280,11197,398],{"c":2807,"m":11211,"d":46,"f":11212,"v":11213,"n":23,"y":37,"u":11214},"not_reported (Scanner 1, AMCW)",[],[],[11215],[4358,952,29,11216,11217],"amplitude-modulated continuous-wave terrestrial panoramic scanner; FoV 360 x 310 deg; angular resolution 0.036 and 0.018 deg; tested at 3, 6 and 10 m; about 100 times the rate of Scanner 2 and 10 times that of Scanner 3; scan time 202 s at 0.036 deg","Table 4; Comparison of Scanners; Inspection Rates section",{"c":2807,"m":11219,"d":46,"f":11220,"v":11221,"n":23,"y":37,"u":11222},"not_reported (Scanner 2, TOF)",[],[],[11223],[4358,952,29,11224,11225],"time-of-flight terrestrial panoramic scanner; about 5,000 points\u002Fs; angular resolution 0.014 and 0.007 deg; tested at 4, 6, 10 and 20 m","Table 4; Experimental Results",{"c":2807,"m":11227,"d":46,"f":11228,"v":11229,"n":23,"y":37,"u":11230},"not_reported (Scanner 3, TOF)",[],[],[11231],[4358,952,29,11232,11233],"time-of-flight terrestrial panoramic scanner; angular resolution 0.014 deg; tested at 6 and 10 m","Table 4",{"c":2807,"m":11235,"d":46,"f":11236,"v":11237,"n":23,"y":100,"u":11238},"not_reported (stationary laser scanner)",[],[],[11239],[8155,23,9211,569,11115],{"c":522,"m":11241,"d":46,"f":11242,"v":11243,"n":23,"y":49,"u":11244},"not_reported (three cameras, models not named)",[],[],[11245],[11143,53,29,11246,11247],"high field-of-view lenses; calibrated with Kalibr; one wall-facing camera per location","Sec. III-A, IV-A, Fig. 4-5",{"c":2807,"m":11249,"d":46,"f":11250,"v":11251,"n":23,"y":100,"u":11252},"not_reported (TLS point clouds supplied with ConSLAM)",[],[],[11253],[5563,41,1280,11254,11255],"per-sequence TLS clouds used as reference maps; S2 TLS cloud used to model a half-centimetre-accurate BIM","Sec. 1, 5.1, 6",{"c":5895,"m":11257,"d":46,"f":11258,"v":11259,"n":23,"y":49,"u":11260},"not_reported (total station; model not named)",[],[],[11261],[11143,41,29,11262,1547],"measures the position of a prism attached to the robot (the prism is written 'leica prism' in Sec. IV-C; no maker is given for the total station); referenced to the origin of the building plan; prism-to-robot offset calibrated by aligning trajectories; ground truth corrected for measured deviations of the as-built reference walls",{"c":18,"m":11264,"d":20,"f":11265,"v":11266,"n":23,"y":270,"u":11267},"notebook PC with Intel Core i7 3610QM 2.3 GHz (max. 3.3 GHz) QuadCore CPU",[98],[],[11268],[2944,28,29,11269,3364],"timings of all methods at VGA resolution",{"c":18,"m":11271,"d":20,"f":11272,"v":11273,"n":23,"y":11274,"u":11275},"notebook with a 1.7 GHz Centrino processor",[],[],2005,[11276],[11277,28,29,11278,863],"girardeaumontaut2005c2c","1.7 GHz Centrino processor; '1 Go of memory' as printed (1 GB)",{"c":651,"m":11280,"d":20,"f":11281,"v":11283,"n":23,"y":142,"u":11284},"Novatel OEM-4",[11282],"Novatel",[],[11285],[6489,53,29,11286,11287],"dual-frequency GPS receiver; at least eight satellites tracked; tightly coupled forward and reverse processing with separation below 2 cm","Experimental Description",{"c":651,"m":11289,"d":20,"f":11290,"v":11292,"n":23,"y":346,"u":11293},"NovAtel PwrPak7 GNSS Inertial Navigation System",[11291],"NovAtel",[],[11294],[2922,41,29,11295,11296],"near-ground-truth positioning described as centimetre-level","Sec. I; Sec. IV-A",{"c":651,"m":11298,"d":46,"f":11299,"v":11300,"n":23,"y":122,"u":11301},"Novatel SPAN-CPT",[11282],[],[11302],[201,41,11303,11304,1204],"UrbanLoco","navigation system combining RTK and precise IMU; ground truth (authors note false sudden jumps)",{"c":108,"m":11306,"d":46,"f":11307,"v":11308,"n":23,"y":122,"u":11309},"NTU VIRAL horizontal and vertical LiDARs (models not stated)",[],[],[11310],[1219,23,184,11311,332],"two LiDARs merged as input",{"c":18,"m":11313,"d":46,"f":11314,"v":11315,"n":23,"y":132,"u":11316},"NUC",[],[],[11317],[2135,23,2136,11318,8547],"acquisition computer running the ROS drivers, time-synchronized to GNSS time",{"c":18,"m":11320,"d":20,"f":11321,"v":11322,"n":23,"y":132,"u":11323},"NUC with Intel i7-1360P CPU",[98],[],[11324],[216,28,29,11325,11326],"32 GB RAM; UAV onboard computer","Sec. X-A1",{"c":18,"m":11328,"d":20,"f":11329,"v":11330,"n":23,"y":122,"u":11331},"NUC with laptop-grade AMD-4800U CPU",[1302],[],[11332],[11333,28,29,11334,214],"dynablox2023","also used in some of the authors' aerial and ground robots; all experiments",{"c":18,"m":11336,"d":20,"f":11337,"v":11338,"n":23,"y":346,"u":11339},"NUC6i7KYK (quad-core i7-6770HQ)",[98],[],[11340],[4101,28,1054,11341,332],"used to measure CPU load on EuRoC",{"c":18,"m":11343,"d":20,"f":11344,"v":11345,"n":23,"y":152,"u":11346},"NVIDIA 1080 Ti",[702],[],[11347],[6523,28,29,11348,11349],"GPU for training and for 10.2 ms inference","Sec. 4 implementation details; Sec. 4.5; Table 4",{"c":18,"m":11351,"d":20,"f":11352,"v":11354,"n":23,"y":122,"u":11355},"Nvidia 2080 Ti",[11353],"Nvidia",[],[11356],[3444,28,29,11357,7673],"12 GB graphics memory; used only by the TSDF baseline",{"c":18,"m":11359,"d":20,"f":11360,"v":11361,"n":23,"y":1008,"u":11362},"Nvidia 4090 GPU",[11353],[],[11363],[1323,28,29,11364,1325],"single GPU, CUDA 11.6",{"c":18,"m":11366,"d":20,"f":11367,"v":11368,"n":23,"y":132,"u":11369},"NVIDIA 4090D",[702],[],[11370],[11371,28,29,11372,11373],"slam3r2025","single GPU for FPS measurements; training on 8 GPUs with 24 GB each","Sec. 4, Sec. 4.1",{"c":18,"m":11375,"d":20,"f":11376,"v":11377,"n":41,"y":122,"u":11379},"NVidia A6000",[702],[11378],"NVIDIA A6000",[11380,11382],[1349,28,29,11381,1351],"GPU",[790,28,29,11383,11384],"single GPU; overall about 5 s per frame","Sec. IV-A2; Sec. V",{"c":18,"m":11386,"d":20,"f":11387,"v":11389,"n":23,"y":289,"u":11390},"nVidia GeForce 680GTX",[11388],"nVidia",[],[11391],[2830,28,29,11392,11393],"2 GB GPU memory; the 512^3 TSDF volume uses 768 MB","Sec. 2.2, 5.3",{"c":18,"m":11395,"d":20,"f":11396,"v":11397,"n":23,"y":270,"u":11398},"nVidia GeForce GTX 570",[11388],[],[11399],[1807,28,29,11400,6887],"graphics card used for all experiments; SIFT computed on the GPU (SiftGPU)",{"c":18,"m":11402,"d":20,"f":11403,"v":11404,"n":23,"y":233,"u":11405},"Nvidia GeForce GTX 730",[702],[],[11406],[5386,28,29,11407,11408],"2 GB RAM; used only when a method needs a GPU (SuMa)","Sec. IV (intro); Sec. IV-A Runtime",{"c":18,"m":11410,"d":20,"f":11411,"v":11412,"n":23,"y":289,"u":11413},"nVidia GeForce GTX 780 Ti",[11388],[],[11414],[5933,28,29,11415,2777],"3GB of memory",{"c":18,"m":11417,"d":20,"f":11418,"v":11419,"n":23,"y":346,"u":11420},"Nvidia GeForce GTX 960",[11353],[],[11421],[7578,28,29,11422,11423],"4 GB RAM; OpenGL 4.0 implementation","Sec. III-F, IV",{"c":18,"m":11425,"d":20,"f":11426,"v":11427,"n":23,"y":318,"u":11428},"NVIDIA GeForce GTX 980 Ti",[702],[],[11429],[4865,28,29,11430,398],"GPU used for all experiments (TensorFlow)",{"c":18,"m":11432,"d":20,"f":11433,"v":11434,"n":23,"y":299,"u":11435},"NVIDIA GeForce GTX Titan",[702],[],[11436],[1786,28,29,11437,11438],"single GPU; DirectX 11 compute shaders","Sec. 9, Sec. 9.1",{"c":18,"m":11440,"d":20,"f":11441,"v":11442,"n":23,"y":1819,"u":11443},"NVIDIA GeForce GTX Titan X",[702],[],[11444],[1822,28,29,11445,5137],"12 GB; volumetric reconstruction",{"c":18,"m":11447,"d":20,"f":11448,"v":11449,"n":41,"y":318,"u":11451},"NVIDIA GeForce GTX1080",[702],[11450],"Nvidia GeForce GTX 1080",[11452,11454],[7159,28,29,11453,332],"8 GB GPU memory",[636,28,29,11455,478],"GPU in the same system",{"c":18,"m":11457,"d":20,"f":11458,"v":11459,"n":41,"y":346,"u":11461},"Nvidia GeForce GTX1080 Ti",[702,11353],[11460],"NVIDIA GeForce GTX 1080Ti",[11462,11464],[924,28,29,11463,2486],"11 GB memory",[2573,28,29,11465,11466],"about 9 ms inference","Sec. 5.2 Usability",{"c":18,"m":11468,"d":20,"f":11469,"v":11470,"n":23,"y":132,"u":11471},"NVIDIA GeForce RTX 3070 Laptop GPU",[702],[],[11472],[3836,28,29,11473,1667],"8 GB; used for XGBoost training",{"c":18,"m":11475,"d":20,"f":11476,"v":11477,"n":41,"y":122,"u":11478},"NVIDIA GeForce RTX 3090",[702],[],[11479,11482],[11480,28,29,11481,4689],"eslam2023","used to benchmark all methods in Table 4",[678,28,29,11483,7969],"DROID-SLAM at 320 x 240, average 16 FPS with global BA, average GPU memory below 11 GB",{"c":18,"m":11485,"d":20,"f":11486,"v":11487,"n":23,"y":1008,"u":11488},"NVIDIA GeForce RTX 4080",[702],[],[11489],[3578,28,29,11490,11491],"Workstation with Intel Core i9-14900KF CPU and 64 GB RAM, Windows 11; used for 2DGS training, TSDF extraction and the GPU-accelerated MVS baseline","Materials, Hardware Configuration; Table 3",{"c":18,"m":11493,"d":20,"f":11494,"v":11495,"n":23,"y":1008,"u":11496},"NVIDIA GeForce RTX 4080D GPU",[702],[],[11497],[2366,28,29,569,332],{"c":18,"m":11499,"d":20,"f":11500,"v":11501,"n":28,"y":100,"u":11502},"NVIDIA GeForce RTX 4090",[702],[],[11503,11505,11506],[2054,28,29,11504,478],"single GPU",[7185,28,29,11504,917],[3211,28,29,11504,313],{"c":18,"m":11508,"d":20,"f":11509,"v":11510,"n":23,"y":132,"u":11511},"NVIDIA GeForce RTX 4090 (24 GB)",[702],[],[11512],[11179,28,29,11513,11514],"With AMD Ryzen Threadripper 7960X CPU; VGGT limited to about 60 images at once on this GPU","Sec. 1; Sec. 5.1; Table 4",{"c":18,"m":11516,"d":20,"f":11517,"v":11518,"n":28,"y":49,"u":11521},"NVIDIA Geforce RTX2080Ti",[702],[11519,11520],"NVIDIA GeForce RTX 2080 Ti","NVIDIA GeForce RTX 2080Ti",[11522,11524,11526],[7253,28,29,11523,332],"GPU for VGICP GPU version",[7256,28,29,11525,332],"GPU in the same desktop PC; used for all experiments (use by LiTAMIN2 itself not stated)",[5523,28,29,11527,332],"11 GB GPU RAM; the method runs mainly on the GPU",{"c":18,"m":11529,"d":46,"f":11530,"v":11531,"n":23,"y":100,"u":11532},"Nvidia GPUs with a maximum memory of 12 GB (models not named)",[702],[],[11533],[103,28,29,11534,11535],"all results gathered on various GPUs of at most 12 GB","Sec. 4.4; App. B",{"c":18,"m":11537,"d":20,"f":11538,"v":11539,"n":23,"y":318,"u":11540},"NVIDIA GTX 1080",[702],[],[11541],[11542,28,29,11543,4496],"deepfactors2020","single GPU running the network, CUDA kernels and visualization at 256x192",{"c":18,"m":11545,"d":20,"f":11546,"v":11547,"n":23,"y":37,"u":11548},"NVIDIA GTX 480",[702],[],[11549],[6374,28,29,11550,1924],"commodity GPU running mapping and tracking",{"c":18,"m":11552,"d":20,"f":11553,"v":11554,"n":23,"y":299,"u":11555},"NVidia GTX 680",[702],[],[11556],[7393,28,29,11381,7395],{"c":18,"m":11558,"d":20,"f":11559,"v":11560,"n":23,"y":289,"u":11561},"Nvidia GTX Titan X GPU",[11353],[],[11562],[1648,28,29,11563,11564],"up to 910 Hz with visualisation, beyond 1.1 kHz without","Sec. 1; Table 1; Fig. 12",{"c":18,"m":11566,"d":20,"f":11567,"v":11568,"n":23,"y":132,"u":11569},"NVIDIA H100",[702],[],[11570],[11571,28,29,11572,11573],"vggt2025","Single GPU with flash attention v3 for runtime and memory measurements; also used for Table 1 timings","Table 1 caption; Table 9",{"c":18,"m":11575,"d":20,"f":11576,"v":11577,"n":23,"y":100,"u":11578},"NVIDIA Jetson AGX Orin Developer Kit",[702],[],[11579],[7209,28,29,11580,11581],"embedded platform; about 18 tracking FPS and about 100 rendering FPS on Replica","Sec. 4.1; Table 1; Supp. Fig. 11",{"c":18,"m":11583,"d":20,"f":11584,"v":11585,"n":952,"y":233,"u":11586},"NVIDIA Jetson AGX Xavier",[702],[],[11587,11589,11591,11593],[1396,28,29,11588,3816],"onboard computer of the CatPack running Wildcat",[7410,28,29,11590,127],"embedded GPU platform",[668,28,29,11592,3280],"perception pack onboard computer; odometry about 1 to 4 Hz",[1371,23,1372,11594,3908],"32 GB RAM, 8 CPU cores; part of the sensor pack with 1 Hz PPS hardware time sync; sensor time gaps at most 3 ms",{"c":18,"m":11596,"d":20,"f":11597,"v":11598,"n":23,"y":132,"u":11599},"NVIDIA Jetson Orin NX",[702],[],[11600],[1136,28,29,11601,11602],"8-core CPU, 1024 CUDA cores, 16 GB LPDDR5","Abstract footnote; Sec. III-D",{"c":18,"m":11604,"d":20,"f":11605,"v":11606,"n":952,"y":1819,"u":11607},"NVIDIA Jetson TX1",[702],[],[11608,11611,11614,11617],[2739,28,29,11609,11610],"four boards, one module per board; quad-core CPU fixed at 1.734 GHz (maximum below 70 degC); integrated GPU; Ubuntu 16.04 LTS and ROS Kinetic","Sec. 3, Sec. 6",[3311,28,29,11612,11613],"runs the proposed SLAM and perspective processing on the UGV","Sec. Initial Frame Registration and Camera Localization; Sec. Computation Time",[3618,28,29,11615,11616],"ARM processor; component timings of SVO Mono","Table III",[3418,28,29,11618,11619],"onboard SLAM computer running ROS and RTAB-Map, connected to the R200 by USB 3.0 (Fig. 2); memory size not stated for the TX1 itself","System architecture; Fig. 2",{"c":18,"m":11621,"d":20,"f":11622,"v":11623,"n":41,"y":346,"u":11625},"NVIDIA Jetson TX2",[702,11353],[11624],"Nvidia Jetson TX2",[11626,11628],[781,28,29,11627,530],"embedded board inside the robot body; controls motion, runs IMU and LiDAR drivers, stores rosbag data",[2680,28,29,11629,3853],"embedded device with ARM Cortex-A57 CPU; CPU only",{"c":18,"m":11631,"d":20,"f":11632,"v":11633,"n":41,"y":132,"u":11634},"NVIDIA Jetson Xavier NX",[702],[],[11635,11638],[2447,28,29,11636,11637],"onboard main computer, Ubuntu 18.04 LTS, ROS Melodic, CAN bus to motor controller","Sec. 3.1(2)",[2865,28,29,11639,11640],"6-core Carmel ARM v8.2 CPU, 384-core Volta GPU, 8 GB LPDDR4x; Ubuntu 18.04, ROS Melodic","Sec. 3.2, Sec. 5.2.4, Table 8",{"c":18,"m":11642,"d":20,"f":11643,"v":11644,"n":23,"y":1008,"u":11645},"NVIDIA Orin NX",[702],[],[11646],[1101,28,2142,11647,1333],"6-core CPU, 8 GB RAM",{"c":18,"m":11649,"d":20,"f":11650,"v":11651,"n":23,"y":132,"u":11652},"NVIDIA Orin NX (on a drone)",[702],[],[11653],[3944,28,29,11654,6391],"system including networks and depth or LiDAR integration can run with parameter trade-offs (supplementary video; not quantified)",{"c":18,"m":11656,"d":20,"f":11657,"v":11658,"n":23,"y":100,"u":11659},"NVIDIA Quadro A4000",[702],[],[11660],[507,28,29,11661,11662],"single GPU; 7.1 Hz full and 11.3 Hz light on KITTI","Sec. V-F2; Table XVI",{"c":18,"m":11664,"d":20,"f":11665,"v":11666,"n":23,"y":1819,"u":11667},"Nvidia Quadro K5200",[702],[],[11668],[8080,28,29,11669,313],"8GB VRAM; runs CNN depth prediction and semantic segmentation",{"c":18,"m":11671,"d":20,"f":11672,"v":11673,"n":23,"y":152,"u":11674},"Nvidia Quadro P4000",[702],[],[11675],[4572,28,29,11676,478],"8 GB RAM",{"c":18,"m":11678,"d":20,"f":11679,"v":11680,"n":23,"y":100,"u":11681},"NVIDIA RTX 1660 Ti",[702],[],[11682],[4121,28,29,11683,6951],"consumer-grade GPU used for the processing-time measurement; model string printed exactly as 'NVIDIA RTX 1660 Ti' in Sec. VI-C",{"c":18,"m":11685,"d":20,"f":11686,"v":11687,"n":23,"y":122,"u":11688},"Nvidia RTX 2080 GPU",[11353],[],[11689],[1029,28,29,569,332],{"c":18,"m":11691,"d":20,"f":11692,"v":11693,"n":41,"y":49,"u":11695},"NVIDIA RTX 2080 Ti",[702],[11694],"NVIDIA RTX 2080Ti",[11696,11699],[4049,28,29,11697,11698],"single GPU used to profile Point-SLAM and NICE-SLAM runtimes","Sec. 4.4 Memory and Runtime Analysis",[11700,28,29,11701,147],"pwclonet2021","single GPU, TensorFlow 1.9.0, used for training and evaluation",{"c":18,"m":11703,"d":20,"f":11704,"v":11705,"n":23,"y":1008,"u":11706},"NVIDIA RTX 3060",[702],[],[11707],[3667,28,29,11708,11709],"12 GB (Sec. 3.6); Table 2 densification timings and the ~25 min 150-frame 2DGS-to-mesh example (Sec. 4.2.3) are on it; Table 5 runs do not name hardware","Sec. 3.6; Table 2; Sec. 4.2.3",{"c":18,"m":11711,"d":20,"f":11712,"v":11713,"n":23,"y":100,"u":11714},"NVIDIA RTX 3080 Ti",[702],[],[11715],[3701,28,29,11716,11717],"Runtime comparison on Replica room0","Table 6; Sec. 5",{"c":18,"m":11719,"d":20,"f":11720,"v":11721,"n":23,"y":100,"u":11722},"NVIDIA RTX 3080ti 16 GB Laptop GPU",[702],[],[11723],[7209,28,29,11724,917],"laptop with Intel Core i9-12900HX and 32 GB RAM",{"c":18,"m":11726,"d":20,"f":11727,"v":11728,"n":2552,"y":233,"u":11731},"NVIDIA RTX 3090",[702,11353],[11729,11730],"NVidia RTX 3090","Nvidia RTX3090",[11732,11734,11736,11738,11741,11743,11746],[6518,28,29,11733,793],"24 GB VRAM",[1549,28,29,11735,1072],"training in about 4 h",[4049,28,29,11737,3230],"GPU used for the Vox-Fusion runtime in Table 6",[3053,28,29,11739,11740],"Single GPU used to measure peak GPU memory and average FPS of methods with public code","Sec. IV; Sec. IV-C; Table XI",[8258,28,29,11742,3807],"single video card used for profiling",[7201,28,29,11744,11745],"GPU; 15.63 GB peak memory used on Replica RGB-D (Table 9)","Sec. 4.1; Table 9",[7457,28,29,11747,917],"GPU of the desktop PC used for all NICE-SLAM runs",{"c":18,"m":11749,"d":20,"f":11750,"v":11751,"n":23,"y":122,"u":11752},"NVIDIA RTX 3090 Ti",[702],[],[11753],[1890,28,29,11754,6987],"desktop GPU used for all timings",{"c":18,"m":11756,"d":20,"f":11757,"v":11758,"n":23,"y":132,"u":11759},"NVIDIA RTX 4060 GPU (consumer-grade laptop)",[702],[],[11760],[4679,28,29,11761,11762],"used for Inria 3DGS training at 1512 x 2016 px, 30,000 steps","Sec. 3.2.2(a)",{"c":18,"m":11764,"d":20,"f":11765,"v":11766,"n":23,"y":132,"u":11767},"NVIDIA RTX 4060 Laptop 8 GB GPU",[702],[],[11768],[2423,28,29,11769,11770],"8 GB","Sec. 4.1 Implementation Details; Sec. 4.3",{"c":18,"m":11772,"d":20,"f":11773,"v":11774,"n":952,"y":100,"u":11776},"NVIDIA RTX 4090",[702],[11775],"Nvidia RTX 4090",[11777,11780,11782,11783],[7241,28,29,11778,11779],"stated as 48 GB VRAM (Sec. 4.1); MonoGS reproduced on it (Table 8 note); Orbeez-SLAM, Point-SLAM and SplaTAM FPS cited from GS-ICP-SLAM, which also used an RTX 4090; hardware for the Table 9 baseline memory values not stated","Sec. 4.1; Table 8 note",[7351,28,29,11781,917],"desktop GPU running SLAM",[7245,28,29,11381,917],[7177,28,29,11784,332],"GPU for all evaluations",{"c":18,"m":11786,"d":20,"f":11787,"v":11788,"n":41,"y":100,"u":11790},"NVIDIA RTX 4090 24GB",[702],[11789],"NVIDIA RTX 4090 24 GB",[11791,11794],[11792,28,29,11793,917],"gsicpslam2024","24 GB GPU",[7209,28,29,11795,917],"desktop GPU with Intel Core i9-13900K and 64 GB RAM; used for Photo-SLAM and all baselines",{"c":18,"m":11797,"d":20,"f":11798,"v":11799,"n":41,"y":132,"u":11800},"NVIDIA RTX 4090 GPU",[702],[],[11801,11803],[2706,28,29,11802,11065],"PyTorch 2.0.1, CUDA 11.8",[2483,28,29,11804,332],"desktop; all compared algorithms run on it",{"c":18,"m":11806,"d":20,"f":11807,"v":11808,"n":23,"y":1008,"u":11809},"NVIDIA RTX 5090 GPU (Sec. IV-A); RTX 5080 GPU (Sec. IV-D)",[702],[],[11810],[1101,28,29,11811,1333],"CUDA-accelerated OpenCV for visual processing",{"c":18,"m":11813,"d":20,"f":11814,"v":11815,"n":23,"y":233,"u":11816},"Nvidia RTX3070",[11353],[],[11817],[1549,28,29,11818,1072],"GPU inference 13 ms",{"c":18,"m":11820,"d":20,"f":11821,"v":11822,"n":23,"y":1008,"u":11823},"NVIDIA RTX4080 Super",[702],[],[11824],[6226,28,29,11825,917],"desktop GPU",{"c":944,"m":11827,"d":20,"f":11828,"v":11829,"n":23,"y":289,"u":11830},"Nvidia Shield Tablet (Nvidia Tegra K1)",[11353],[],[11831],[1648,28,29,11832,1650],"runs the full pipeline at up to 47 Hz on 320x240 depth with IMU",{"c":18,"m":11834,"d":20,"f":11835,"v":11836,"n":23,"y":1819,"u":11837},"NVIDIA Tesla K40",[702],[],[11838],[11839,28,29,11840,332],"deepvo2017","GPU used for training in Theano; inference runtime not reported",{"c":18,"m":11842,"d":20,"f":11843,"v":11844,"n":23,"y":122,"u":11845},"NVIDIA Titan RTX (24 GB)",[702],[],[11846],[803,28,29,11847,7002],"24 GB memory",{"c":18,"m":11849,"d":20,"f":11850,"v":11851,"n":41,"y":346,"u":11853},"Nvidia Titan-X Pascal",[11353],[11852],"Nvidia TitanX Pascal",[11854,11855],[7384,28,29,569,7386],[492,28,29,11856,214],"GPU running the modified ResNet38 semantic segmentation at 100 ms per image",{"c":18,"m":11858,"d":20,"f":11859,"v":11860,"n":23,"y":318,"u":11861},"NVIDIA V100",[702],[],[11862],[706,28,29,11863,11864],"single GPU; 100k to 300k iterations take about 1 to 2 days; about 30 s per rendered frame","Sec. 5.3; App. A",{"c":33,"m":11866,"d":46,"f":11867,"v":11868,"n":41,"y":346,"u":11869},"object avoidance sensors (type not reported)",[],[],[11870,11872],[2316,53,29,11871,917],"obstacle avoidance on lower platform",[431,53,29,10924,441],{"c":1776,"m":11874,"d":46,"f":11875,"v":11877,"n":23,"y":289,"u":11878},"Occipital Structure Sensor",[11876],"Occipital",[],[11879],[1648,53,29,11880,257],"depth images at 320x240 (couch sequence)",{"c":372,"m":11882,"d":46,"f":11883,"v":11884,"n":23,"y":1819,"u":11885},"Octo-rotor micro aerial vehicle",[],[],[11886],[5093,53,29,11887,6045],"manually flown at 1 m\u002Fs",{"c":372,"m":11889,"d":46,"f":11890,"v":11891,"n":23,"y":233,"u":11892},"Octocopter UAV (model not stated)",[],[],[11893],[3742,53,29,11894,11895],"controlled by an operator inside a hangar along one side of a B737","Sec. IV-A, Fig. 3a",{"c":33,"m":11897,"d":46,"f":11898,"v":11899,"n":23,"y":61,"u":11900},"odometry",[],[],[11901],[245,23,3362,11902,11903],"described as relatively inaccurate; raw-odometry average RMS error 93.6 m","Sec. 6, Fig. 1a",{"c":353,"m":11905,"d":46,"f":11906,"v":11907,"n":23,"y":49,"u":11908},"odometry encoders",[],[],[11909],[615,53,29,11910,478],"used with IMU and scans in Cartographer",{"c":353,"m":11912,"d":46,"f":11913,"v":11914,"n":23,"y":252,"u":11915},"odometry provided by the ATRV-Mini robot",[8382],[],[11916],[255,53,29,11917,265],"standard deviations 0.02 m on x and y and 0.02 rad on yaw",{"c":18,"m":11919,"d":20,"f":11920,"v":11921,"n":23,"y":1008,"u":11922},"off-board Linux desktop, Intel i5-14400 CPU, 64 GB DDR5 RAM",[],[],[11923],[2272,28,29,11924,11925],"mapping, NBV planning and path planning; path planning typically 50-200 ms","Sec. 3.3.1; Sec. 3.5.1",{"c":18,"m":11927,"d":46,"f":11928,"v":11929,"n":23,"y":1819,"u":11930},"offboard laptop (model not reported)",[],[],[11931],[8835,28,29,11932,1667],"receives RGB-D data from the MAV; detects loop closures, computes global pose corrections and builds the occupancy voxel map",{"c":33,"m":11934,"d":20,"f":11935,"v":11937,"n":23,"y":318,"u":11938},"Omega85",[11936],"ATI",[],[11939],[688,53,689,11940,691],"foot force and torque sensing for contact detection",{"c":522,"m":11942,"d":46,"f":11943,"v":11944,"n":23,"y":100,"u":11945},"omnidirectional camera (front-facing camera, one of five, used)",[],[],[11946],[599,23,600,11947,602],"5 Hz image rate",{"c":944,"m":11949,"d":20,"f":11950,"v":11951,"n":23,"y":132,"u":11952},"OmniSLAM R6",[],[],[11953],[528,53,29,11954,11955],"commercial backpack SLAM LiDAR; rotating 32-beam LiDAR, maximum range 120-300 m, peak 640,000 points\u002Fs, up to 10,000 points\u002Fm2; SLAM with inertial fusion (LIO); absolute accuracy 3 cm, relative better than 1 cm; embedded processing and power unit","Sec. 3.1, Fig. 5",{"c":353,"m":11957,"d":20,"f":11958,"v":11960,"n":23,"y":132,"u":11961},"Omron E6B2-CWZ6C (x2)",[11959],"Omron",[],[11962],[678,23,679,11963,11964],"incremental rotary encoders, 1000 P\u002FR, about 100 Hz, at rear wheels of UGV","Table 2; Sec. 3.2.2",{"c":522,"m":11966,"d":20,"f":11967,"v":11969,"n":23,"y":100,"u":11970},"OMRON SENTECH STC-MBS202POE",[11968],"OMRON SENTECH",[],[11971],[4121,41,29,11972,167],"images recorded with LiDAR-IMU data; synchronized via IEEE 1588 PTP; with wall AprilTags gives ground-truth trajectories",{"c":18,"m":11974,"d":46,"f":11975,"v":11976,"n":23,"y":152,"u":11977},"on-board computer (model not reported)",[],[],[11978],[414,28,29,11979,11980],"runs ROS, Hector SLAM, navigation and scan stitching in real time","Collection Platform, Experimental Setup and Results",{"c":33,"m":11982,"d":46,"f":11983,"v":11984,"n":23,"y":233,"u":11985},"onboard attitude and heading reference system (AHRS) of the drone (model not reported)",[],[],[11986],[1201,41,2519,11987,3256],"fuses magnetometer measurements; used as orientation ground truth for Table VII because the Leica station gives no orientation",{"c":18,"m":11989,"d":20,"f":11990,"v":11991,"n":23,"y":1008,"u":11992},"onboard computer with Intel i3-N305 at 3.0 GHz, 16 GB memory",[98],[],[11993],[2173,28,29,11994,11995],"used for the online relocalization experiment (private2)","Sec. 10; Sec. 10.4",{"c":18,"m":11997,"d":46,"f":11998,"v":11999,"n":23,"y":1819,"u":12000},"onboard flight computer (Pixhawk project, ETH Zurich)",[],[],[12001],[8835,28,29,12002,12003],"1.86 GHz Core2Duo processor, 4 GB RAM; runs VO, state estimation and control; roughly 25 ms per VO frame","Sec. 3; Sec. 4.1",{"c":33,"m":12005,"d":46,"f":12006,"v":12007,"n":23,"y":132,"u":12008},"onboard illuminator",[],[],[12009],[135,23,1913,12010,12011],"15 W, used in extremely dark environments","Sec. V-A2",{"c":662,"m":12013,"d":46,"f":12014,"v":12015,"n":23,"y":1819,"u":12016},"onboard IMU (model not reported)",[],[],[12017],[8835,53,29,12018,12019],"fused with visual-odometry motion estimates in an Extended Kalman Filter for position and velocity","Sec. 1; Sec. 3.3",{"c":662,"m":12021,"d":20,"f":12022,"v":12023,"n":23,"y":1008,"u":12024},"onboard IMU of Unitree Go1",[],[],[12025],[1155,23,12026,690,332],"Leg-KILO dataset",{"c":18,"m":12028,"d":46,"f":12029,"v":12030,"n":23,"y":1008,"u":12031},"onboard passively cooled Linux computer (model not_reported)",[],[],[12032],[2272,28,29,12033,9191],"real-time localization and data processing on the robot",{"c":18,"m":12035,"d":46,"f":12036,"v":12037,"n":23,"y":132,"u":12038},"onboard system-on-module (model not reported)",[],[],[12039],[405,28,29,12040,12041],"stores ROS bags, runs 2D mapping and collision avoidance; limited memory capacity","Sec. 3.1, 4.2, 5",{"c":108,"m":12043,"d":20,"f":12044,"v":12046,"n":23,"y":71,"u":12047},"Optab prototype 3D laser range finder",[12045],"Optab Optronikinnovation AB",[],[12048],[1306,53,12049,12050,12051],"JUNCTION and TUNNEL","modulated infrared laser projected onto a rotating mirror, phase-shift ranging; JUNCTION data scan 139,642 and model 72,417 points; TUNNEL about 27,500 points per scan","Sec. 5.1; Fig. 9",{"c":108,"m":12053,"d":20,"f":12054,"v":12056,"n":23,"y":71,"u":12057},"Optech 3100",[12055],"Optech",[],[12058],[1573,952,29,12059,1659],"range error 0.02 m, angular resolution 0.001 deg, beam divergence 0.3 mrad, total angular error 0.0044 deg; used for fixed-wing simulations",{"c":944,"m":12061,"d":46,"f":12062,"v":12064,"n":23,"y":299,"u":12065},"OPTECH LYNX MOBILE MAPPER",[12063],"OPTECH",[],[12066],[1606,952,29,12067,12068],"iFLEX LiDAR head, 500,000 measurements\u002Fs, 360 deg FOV; max range 200 m (reflectivity 20%); 75 to 500 kHz; 80 to 200 Hz; up to four returns; APPLANIX POS 520 with two GNSS antennas; up to four 5 Mpx cameras at up to 3 fps","Sec. 3.7, Table 3",{"c":33,"m":12070,"d":46,"f":12071,"v":12072,"n":23,"y":233,"u":12073},"optical motion capture (Optitrack in the Lab location; RPG Tracking Area system model not reported)",[],[],[12074],[1038,41,1039,12075,12076],"6-DoF pose from targets on the stick, position accuracy below 1 mm at 200 Hz; clock not hardware-synchronised with the logger (1 to 3 ms offset)","Sec. III-D; Sec. IV (e), (h); Sec. VI",{"c":33,"m":12078,"d":46,"f":12079,"v":12080,"n":28,"y":346,"u":12081},"OptiTrack",[],[],[12082,12084,12086],[1562,41,1563,12083,469],"motion capture of reflective-ball centre at 120 Hz with millimeter accuracy (as stated); connected to the recording PC; hand-eye calibration to body frame",[225,41,854,12085,228],"indoor ground truth",[1078,41,29,12087,7571],"motion capture ground truth for the flight",{"c":33,"m":12089,"d":20,"f":12090,"v":12091,"n":23,"y":152,"u":12092},"Optitrack Prime 13 motion capture system",[12078],[],[12093],[1811,41,1812,12094,469],"ground-truth sensor trajectories",{"c":4378,"m":12096,"d":20,"f":12097,"v":12099,"n":23,"y":346,"u":12100},"Optris PI 450 thermal-infrared camera",[12098],"Optris",[],[12101],[3868,23,12102,12103,3870],"authors' hand-held spinning LiDAR data","382 x 288 pixels; mounted on the device; Fig. 2a caption states the thermal camera is not used in the paper",{"c":4378,"m":12105,"d":20,"f":12106,"v":12108,"n":23,"y":270,"u":12109},"optris PI160",[12107],"optris",[],[12110],[2407,53,29,12111,530],"160 x 120 px, thermal resolution 0.1 degC, 7.5 to 13 um, 120 Hz, accuracy 2 degC, FOV about 40 deg x 64 deg",{"c":108,"m":12113,"d":20,"f":12114,"v":12115,"n":23,"y":100,"u":12116},"OS1",[],[],[12117],[1396,53,29,12118,469],"only the OS1 LIDAR and an IMU used in the 2.2 km MARBLE run",{"c":108,"m":12120,"d":20,"f":12121,"v":12122,"n":23,"y":233,"u":12123},"OS1 (two units)",[180],[],[12124],[1201,53,2519,12125,12126],"one horizontal and one vertical LiDAR, each processed by A-LOAM as an OSL input","Sec. VI-C; Fig. 10",{"c":108,"m":12128,"d":20,"f":12129,"v":12130,"n":23,"y":233,"u":12131},"OS1 gen1 (16-channel)",[180],[],[12132],[3255,23,212,12133,167],"horizontal 16-channel spinning LiDAR",{"c":662,"m":12135,"d":20,"f":12136,"v":12137,"n":23,"y":233,"u":12138},"OS1 internal IMU",[180],[],[12139],[3255,23,212,569,167],{"c":108,"m":180,"d":20,"f":12141,"v":12142,"n":23,"y":132,"u":12143},[180],[],[12144],[1145,23,1146,12145,9673],"HeLiPR scanner, called 'the Ouster scanner'; model not stated",{"c":108,"m":12147,"d":46,"f":12148,"v":12149,"n":23,"y":1008,"u":12150},"Ouster (model not named)",[180],[],[12151],[3427,23,12152,569,2207],"GEODE (Stairs, Offroad)",{"c":108,"m":12154,"d":46,"f":12155,"v":12156,"n":23,"y":132,"u":12157},"Ouster 64-beam LiDAR (model not stated)",[180],[],[12158],[1588,23,824,12159,1072],"handheld sensor mast",{"c":108,"m":12161,"d":20,"f":12162,"v":12163,"n":23,"y":233,"u":12164},"Ouster 64-channel LiDAR",[180],[],[12165],[2979,23,12166,12167,12168],"Newer College Dataset (NCD)","handheld, mounted on a stick","Sec. V-A-2",{"c":108,"m":12170,"d":46,"f":12171,"v":12172,"n":23,"y":122,"u":12173},"Ouster 64-channel LiDAR (model not stated)",[180],[],[12174],[1219,23,824,12175,469],"64 channels, 90-degree vertical field of view",{"c":662,"m":12177,"d":20,"f":12178,"v":12179,"n":23,"y":122,"u":12180},"Ouster built-in IMU",[180],[],[12181],[1219,23,824,2377,469],{"c":662,"m":12183,"d":20,"f":12184,"v":12185,"n":41,"y":122,"u":12186},"Ouster internal IMU",[180],[],[12187,12189],[1588,23,824,12188,1072],"100 Hz; used to avoid LiDAR-IMU synchronization problems",[2995,23,824,2377,12190],"Sec. IV-B-1",{"c":108,"m":12192,"d":46,"f":12193,"v":12194,"n":23,"y":100,"u":12195},"Ouster LiDAR (model not named)",[180],[],[12196],[3805,23,846,185,441],{"c":108,"m":12198,"d":46,"f":12199,"v":12200,"n":23,"y":122,"u":12201},"Ouster LiDAR (model not stated in DLIO)",[180],[],[12202],[2995,23,824,185,12190],{"c":108,"m":12204,"d":20,"f":12205,"v":12206,"n":23,"y":132,"u":12207},"Ouster OS-0 (128 beams)",[180],[],[12208],[2221,23,12209,12210,1104],"Newer College Multi-Cam (2022)","handheld, outdoor campus",{"c":108,"m":12212,"d":20,"f":12213,"v":12214,"n":23,"y":132,"u":12215},"Ouster OS-1 (128 beams)",[180],[],[12216],[2221,23,12217,12218,1104],"Multi-Campus (MCD)","ATV and handheld, outdoor campus",{"c":108,"m":12220,"d":20,"f":12221,"v":12222,"n":23,"y":132,"u":12223},"Ouster OS-1 (64 beams)",[180],[],[12224],[2221,23,12225,12210,1104],"Newer College Stereo-Cam (2020)",{"c":108,"m":12227,"d":20,"f":12228,"v":12229,"n":23,"y":132,"u":12230},"Ouster OS-2 (128 beams)",[180],[],[12231],[2221,23,1146,12232,1104],"car, urban road",{"c":108,"m":12234,"d":20,"f":12235,"v":12236,"n":952,"y":122,"u":12238},"Ouster OS0",[180],[12237],"Ouster OS-0",[12239,12241,12243,12246,12250],[1155,23,3436,12240,332],"mounted on a drone",[5228,53,5229,12242,469],"128 beam, hand-held, with integrated IMU; intensity returns projected into images",[5228,23,12244,12245,332],"Newer College Dataset (multi-camera extension)","128-beam, hand-held",[11333,53,12247,12248,12249],"Dynablox newly recorded sequences","128 beams (written 'OS0 128' in Sec. VI and '128-beam Ouster OS0' in Sec. IV-C), high-range, high-resolution, 90 deg vertical FoV, 10 Hz; state estimates from FAST-LIO2","Sec. IV-C, Sec. VI",[2272,53,29,12251,12252],"64 beams, 90 deg vertical and 360 deg horizontal FoV; mounted with custom 3D printed brackets; primary sensor for SLAM and mapping","Sec. 3.3.1; Fig. 2",{"c":662,"m":12254,"d":46,"f":12255,"v":12256,"n":23,"y":100,"u":12257},"Ouster OS0 integrated IMU (model not reported)",[180],[],[12258],[5228,53,5229,569,469],{"c":108,"m":12260,"d":20,"f":12261,"v":12262,"n":8,"y":49,"u":12266},"Ouster OS0-128",[180],[12263,12264,12265],"OS0-128","Ouster OS0-128 Gen 2","Ouster OS0-128 RevD",[12267,12271,12274,12275,12277,12281,12284,12287,12290,12292,12294,12296,12299,12303,12306,12308,12310],[282,23,12268,12269,12270],"Newer College extension (Zhang et al. 2021)","128 rings; handheld (Table 1; Sec. 4.9 text names it OS1-128, inconsistent with Table 1)","Table 1; Sec. 4.9",[1904,23,12272,569,12273],"ASPAR scaffold BEV detection dataset (650 BEV images, six sites)","Sec. 4.2, Sec. 4.3.1",[1029,53,29,569,469],[6422,23,12276,569,745],"Newer College (NC0)",[1263,53,12278,12279,12280],"Fyllingsdalen Bicycle Tunnel dataset","10 Hz; full 360 degree FOV","Sec. IV-E, Fig. 5",[4121,23,12282,12283,3256],"Multi-Camera Newer College","10 Hz point clouds with 100 Hz IMU; PTP-synchronized with cameras",[2325,41,2326,12285,12286],"spinning, FoV 360 x 90 deg, range 50 m, 10 Hz, built-in IMU; externally powered, Ethernet to laptop; used with a modified FAST-LIO2 to produce the reference trajectory","Abstract, Sec. 2.2, Sec. 2.3, Table 2, Fig. 4b",[3787,53,29,12288,12289],"LiDAR odometry via CompSLAM at 5 Hz","Sec. IV-D, V-a",[1549,53,29,12291,3256],"128 beams, 90 deg vertical field of view; unseen in training, used for the generalization test",[1476,53,5593,12293,3280],"LiDAR on HEAP; feeds CompSLAM scan-to-map registration and Coin-LIO",[507,23,815,12295,4640],"handheld, used for the shorter sequences",[2265,53,29,12297,12298],"128 channels, 90 deg vertical FOV; resolution 512x20 (env. 1) or 1024x20 (env. 2); up to 131,072 points per frame; raw data recorded as .pcap with Ouster Studio","Sec. 3.3, 4.1, 4.3",[1553,53,12300,12301,12302],"Seemuhle mine repeat run","much higher point density and larger FoV than VLP-16; 128-beam (dense) LiDAR","Sec. VII-D2, VII-G3",[1520,53,29,12304,12305],"older and noisy variant OS0 Rev-1 on HEAP (Sec. IV-C); large field of view (Sec. VI-A)","Sec. IV-C, V, VI-A",[1247,23,1248,12307,1250],"10 Hz, 128 channels, 50 m range, 90 deg vertical FoV, 1024 horizontal resolution; mounted above the cameras",[8155,53,29,12309,11065],"on a handheld sensor suite; operator walked each floor; clouds reconstructed per floor with FAST-LIO2",[2429,23,2430,12311,12312],"handheld sensor suite; seven sequences, Floors 06 to 12","Sec. 4.2.1, Fig. 13",{"c":108,"m":12314,"d":20,"f":12315,"v":12316,"n":41,"y":100,"u":12318},"Ouster OS0-32",[180],[12317],"OS0-32",[12319,12323],[282,53,12320,12321,12322],"Almeria forests (Aguilar et al. 2024)","backpack kit with the LiDAR as the only sensor; forests","Table 1; Sec. 5.3",[4121,53,29,12324,7981],"spinning LiDAR",{"c":108,"m":12326,"d":20,"f":12327,"v":12328,"n":6535,"y":233,"u":12331},"Ouster OS0-64",[180],[12329,12330],"OS0-64","Ouster OS0-64 RevD",[12332,12336,12337,12339,12342,12345,12346,12347,12349,12351,12354],[282,23,12333,12334,12335],"HILTI 2021","drone testing arena, industrial unit","Table 1; Sec. 4.6",[1029,23,1030,569,332],[6422,23,6005,12338,745],"sequences acquired with a quadrotor and handheld",[1263,53,12340,12341,3518],"RelyOn Nutec dataset","10 Hz; FOV reduced from 360 to 180 degrees; noise model sigma_p = 1.5 cm from datasheet",[1038,23,1039,12343,12344],"360 deg scanning, 10 Hz, 1,300,000 points\u002Fs, range 0.3 to 50 m (lowest-noise returns beyond 1 m), range accuracy 1.5 to 5 cm; internal IMU hardware-synchronised","Sec. III-B, III-E; Fig. 1",[5280,23,5281,569,2229],[4121,53,29,12324,7981],[201,23,5524,12348,1204],"handheld; lidar data of the Hilti dataset",[507,23,10692,12350,4640],"handheld; indoor offices, labs, basements and outdoor construction sites",[905,23,6005,12352,12353],"360 deg FoV at 10 Hz, handheld","Sec. IV-A; Table III footnote",[5523,23,5524,12355,12356],"360 deg field of view, 10 Hz; only this sensor's data used","Sec. IV-B; Sec. IV-C; Table III (VoR)",{"c":108,"m":12358,"d":20,"f":12359,"v":12360,"n":2256,"y":318,"u":12362},"Ouster OS1",[180],[12361],"Ouster OS-1",[12363,12366,12368,12371,12373],[715,53,29,12364,12365],"360 deg LiDAR, 20 Hz as stated; on custom quadrotor","Fig. 1A; Sec. II-B; Sec. III-C",[2995,53,2996,12367,8209],"10 Hz, 32 channels recorded with 512 horizontal resolution",[2281,53,29,12369,12370],"3D LiDAR, maximum range 150 m, 45 deg vertical FOV; used for navigation; converted to a 2D laser scan for the SLAM node","VoR Sec. 4.2.1, 4.2.4, Fig. 8b",[3237,53,29,12372,5829],"64 beam (Fig. 5 caption)",[5523,23,815,12374,12375],"multi-beam 3D LiDAR on a handheld device at Oxford; dataset provides a millimetre-accurate 3D map; NCD-QUAD sequence","Sec. IV-B; Table IV (VoR)",{"c":108,"m":12377,"d":20,"f":12378,"v":12379,"n":23,"y":100,"u":12380},"Ouster OS1 (64-beam)",[180],[],[12381],[7410,23,12382,12383,12384],"drone LiDAR dataset of Voxgraph [6]","64-beam; integrated up to 25 m","Sec. V-E, Fig. 7",{"c":108,"m":12386,"d":20,"f":12387,"v":12389,"n":6535,"y":49,"u":12394},"Ouster OS1-128",[180,12388],"Ouster (sensor specification simulated)",[12390,12391,12392,12393],"OS1-128","OS1-128-Rev-07 (Ouster OS1)","Ouster OS1-128 (simulated in Gazebo)","Ouster OS1-128 Gen5",[12395,12397,12399,12402,12403,12405,12408,12412,12414,12416,12419],[405,53,29,12396,10406],"horizontal FOV 360 deg, vertical FOV 45 deg; mounted on the robot with device height limited to 0.5 m; battery powered, data via pigtail cable",[2054,23,1563,12398,1104],"2,621,440 points\u002Fs; mechanical, repetitive; 1 m to 120 m; FoV 45 x 360 deg",[1562,23,1563,12400,12401],"120 m range at 10 Hz; FOV 45 deg vertical, 360 deg horizontal; image output 1028 x 128 at 10 Hz (as printed); internal IMU at 100 Hz; also outputs depth, signal and ambient images","Sec. III-A1; Table II; Fig. 1",[781,53,29,569,10460],[1155,53,2376,12404,1538],"car-mounted",[1219,53,1220,12406,12407],"spinning LiDAR, merged with the Livox stream","Sec. IV-C; Sec. I",[52,53,12409,12410,12411],"R-LOAM simulated datasets 2, 4, 6","128 scan lines, up to 262,144 points per scan, 10 Hz, zero-mean Gaussian noise sigma 0.05","Sec. IV-A, Table I",[790,53,2000,12413,793],"128 beams, 45 deg vertical FOV, 10 Hz, mounted horizontally on a robot car",[537,53,29,12415,8164],"128 channels, 1024 horizontal resolution, 360 x 45 deg FoV, 10 Hz, precision +-1 cm (1-20 m) and +-2 cm (20-50 m)",[1520,23,3890,12417,12418],"ENWIDE handheld payload per Sec. V; Sec. V-C names Ouster OS0-128 for the same payload","Sec. V, V-C",[678,23,679,12420,12421],"10 Hz; 45 deg vertical x 360 deg horizontal FOV; built-in 9-axis IMU; outputs range, near-ir, reflectivity and signal images; timestamp at end of scan","Table 2; Sec. 3.1.2",{"c":108,"m":12423,"d":20,"f":12424,"v":12425,"n":2552,"y":233,"u":12432},"Ouster OS1-16",[180],[12426,12427,12428,12429,12430,12431],"OS1 16-channel (two units)","Ouster OS1 16-channel (horizontal)","Ouster OS1-16 (horizontal)","Ouster OS1-16 Gen1","Ouster OS1-16 Gen1 (x2, horizontal and vertical)","Ouster OS1-16-gen-1 (two units, horizontal and vertical)",[12433,12436,12439,12441,12443,12445,12448],[1510,23,184,12434,12435],"drone; adopted LiDAR for RESPLE","Table I; Sec. V-B1",[5070,23,184,12437,12438],"16,384 points per scan","Sec. VI-B; Table I",[4121,23,184,12440,5457],"two units on a UAV",[3444,23,184,12442,1104],"mechanical spinning 16-line; FoV 360.0 x 33.2 deg; 327,680 points\u002Fs; USD 3,500",[201,23,184,12444,1204],"one horizontal and one vertical on a UAV; only the horizontal one used",[3460,23,184,12446,12447],"16 channels, 10 Hz point clouds, internal IMU at 100 Hz, V2 firmware (reflectivity, time, ambient fields); per-point time relative to scan start","Sec. 2.2, Table 2, Sec. 4.2",[1191,23,184,569,12449],"Sec. IV-A-2; Sec. IV-C-1",{"c":108,"m":12451,"d":20,"f":12452,"v":12453,"n":23,"y":122,"u":12454},"Ouster OS1-32",[180],[],[12455],[4780,53,29,12456,4782],"mounted with a mini-PC and batteries in a portable mapping system usable handheld or on a robot",{"c":108,"m":12458,"d":20,"f":12459,"v":12460,"n":9398,"y":318,"u":12464},"Ouster OS1-64",[180],[12461,12462,12463],"OS1 64","OS1-64","OS1-64 (2020) and OS1-128 (2023)",[12465,12468,12470,12474,12477,12479,12481,12483,12486,12487,12489,12491,12494,12496,12498,12501,12504,12506],[282,23,12466,12467,12270],"Newer College (Ramezani et al. 2020)","64 rings; handheld (Table 1; Sec. 4.9 text names it OS0-64, inconsistent with Table 1)",[282,23,846,12469,4444],"vehicle; dataset also provides consumer-grade GNSS used only in loop closure",[282,23,12471,12472,12473],"Voxgraph","drone; RTK-based ground truth","Table 1; Sec. 4.7",[7474,23,12475,12476,9980],"Newer College (NCD)","360 deg mechanical LiDAR",[2135,23,2136,12478,2138],"64 scan lines, range 100 m, vertical FOV 45 deg, horizontal FOV 360 deg, 10 Hz; reflectivity and range images; device beta",[6422,23,12480,569,745],"MulRan; Newer College (NC1)",[4412,53,29,12482,469],"64 beams; used with LOAM in the TRJV mine deployment",[5118,23,846,12484,12485],"64-ray, 290 deg usable HFOV in Table III","Sec. VI-A2; Table III",[5783,23,846,569,2162],[2173,23,846,12488,2174],"robotcar; LiDAR loses about 70 deg of FoV",[1536,23,8414,12490,469],"carried by a tracked robot at Satsop Business Park; Alpha course used for training, Beta course for testing",[507,23,12492,12493,4640],"MulRAN","car-mounted; field of view partly blocked by the radar sensor; reference poses from GNSS-INS",[507,23,815,12495,4640],"handheld, used for the two longer sequences",[507,23,4638,12497,4640],"robot car in Bonn; self-collected",[896,23,815,12499,12500],"10 Hz, 64 channels, 120 m range, 45 deg vertical FOV, 1024 horizontal resolution; mounted on top rotated 45 deg; PTP-synchronized to recording computer","Sec. III, Table II",[668,23,4281,12502,12503],"10 Hz, range 120 m, vehicle-mounted","Sec. VI-A2; Table I",[321,23,322,12505,324],"primary high-density LiDAR point cloud; relocated on the Husky to avoid occlusion",[11333,23,115,12507,214],"high-range, high-resolution, 10 Hz; 8 sequences in 4 environments",{"c":108,"m":12509,"d":20,"f":12510,"v":12511,"n":41,"y":122,"u":12513},"Ouster OS2-128",[180],[12512],"OS2-128",[12514,12516],[5280,53,6600,12515,10036],"central LiDAR",[1966,23,9726,569,9727],{"c":108,"m":12518,"d":20,"f":12519,"v":12520,"n":23,"y":49,"u":12521},"Ouster OS2-64",[180],[],[12522],[7952,53,29,12523,10863],"spinning multi-line LiDAR with lower resolution at stationary",{"c":108,"m":12525,"d":20,"f":12526,"v":12527,"n":23,"y":132,"u":12528},"Ouster-16",[180],[],[12529],[2423,23,212,12530,917],"multi-line spinning LiDAR, sparser than Livox; images 752 x 480",{"c":372,"m":12532,"d":46,"f":12533,"v":12534,"n":23,"y":61,"u":12535},"outdoor vehicle",[],[],[12536],[245,23,3362,569,3364],{"c":651,"m":12538,"d":20,"f":12539,"v":12541,"n":41,"y":142,"u":12542},"OXTS RT 3003",[12540],"OXTS",[],[12543,12545],[4511,41,578,12544,639],"high-accuracy GPS\u002FINS localization system providing ground truth",[12546,41,842,12547,12548],"geiger2012kitti","GPS, GLONASS, IMU and RTK correction; open-sky localization errors below 5 cm; its output is the odometry ground truth","Sec. 1, Sec. 2.1, Sec. 2.3",{"c":651,"m":12550,"d":20,"f":12551,"v":12552,"n":23,"y":37,"u":12553},"OXTS RT 3003 GPS\u002FIMU",[12540],[],[12554],[6365,41,12555,12556,469],"Karlsruhe dataset (cvlibs.net)","'weak' ground truth; errors up to two metres possible in inner-city scenarios",{"c":651,"m":12558,"d":46,"f":12559,"v":12560,"n":23,"y":132,"u":12561},"OXTS RTK GPS (model not stated)",[12540],[],[12562],[1588,41,12563,4758,1084],"KITTI-raw",{"c":6267,"m":12565,"d":20,"f":12566,"v":12567,"n":23,"y":233,"u":12568},"P440",[6277],[],[12569],[1201,53,2519,12570,12571],"UWB ranging nodes; two UAV nodes with two antennae each; three anchors in field tests","Sec. VI-C; Figs. 10-11",{"c":33,"m":12573,"d":46,"f":12574,"v":12575,"n":23,"y":1008,"u":12576},"painted ground control points (circular marker with crosshair on white spray coating, Roman numeral ID)",[],[],[12577],[6226,53,29,12578,12579],"four stable sidewall regions per scan location","Sec. 2.1, Fig. 1",{"c":1689,"m":12581,"d":46,"f":12582,"v":12583,"n":23,"y":24,"u":12584},"pair of synchronized analog cameras (two stereo heads on the vehicle; model not reported)",[],[],[12585],[27,53,29,12586,12587],"50 deg horizontal field of view, 720 x 240 image fields, tilted about 10 deg to the side, 28 cm baseline","Sec. 5.1; Fig. 4",{"c":33,"m":12589,"d":46,"f":12590,"v":12592,"n":23,"y":152,"u":12593},"Paroscientific Digiquartz depth sensor",[12591],"Paroscientific",[],[12594],[2111,53,29,12595,332],"drift-free depth measurement",{"c":372,"m":12597,"d":20,"f":12598,"v":12599,"n":23,"y":233,"u":12600},"Parrot Bebop2",[2025],[],[12601],[1657,53,29,12602,398],"compact off-the-shelf UAV with onboard flight controller, IMU, sonar and vertical camera for height, forward-looking camera; no hardware modification",{"c":33,"m":12604,"d":46,"f":12605,"v":12607,"n":23,"y":233,"u":12608},"Parrot-Sphinx simulator with Gazebo",[12606],"Parrot (Sphinx)",[],[12609],[1657,53,29,12610,12611],"photo-realistic BIM-enabled simulation with simulated IMU, ultrasound, vertical and front cameras","Sec. 5.1.2, Sec. 5.2.2",{"c":372,"m":12613,"d":46,"f":12614,"v":12615,"n":41,"y":1819,"u":12617},"Passenger vehicle",[],[12616],"passenger vehicle",[12618,12621],[5093,53,29,12619,12620],"streets; mostly 11-18 m\u002Fs on 3.6 km run","Sec. 7.4, Fig. 17b",[2905,53,29,12622,12623],"street driving; both sensor suites attached","Fig. 16c",{"c":372,"m":12625,"d":46,"f":12626,"v":12627,"n":23,"y":132,"u":12628},"Passenger vehicle (model not reported)",[],[],[12629],[678,23,679,12630,12631],"suite on luggage rack with custom aluminium frame; 10 to 100 km\u002Fh","Sec. 3.2.3; Sec. 5.1.4",{"c":18,"m":12633,"d":46,"f":12634,"v":12635,"n":23,"y":152,"u":12636},"PC carried in the backpack",[],[],[12637],[1957,28,29,569,12638],"Sec. System overview",{"c":18,"m":12640,"d":46,"f":12641,"v":12643,"n":23,"y":318,"u":12644},"PC with Intel Core i7 6700K and MSI Geforce GTX 1080 Gaming X 8G",[12642],"Intel, MSI (Nvidia GPU)",[],[12645],[8574,28,29,12646,12647],"surfel reconstruction and denoising in CUDA 8.0 on the GPU; meshing on CPU","Sec. 4; Sec. 5",{"c":18,"m":12649,"d":46,"f":12650,"v":12651,"n":23,"y":1008,"u":12652},"PC with Intel i9 13900K",[],[],[12653],[1476,28,29,12654,1204],"all evaluations",{"c":372,"m":12656,"d":46,"f":12657,"v":12659,"n":23,"y":1819,"u":12660},"Pelican quadrotor",[12658],"Ascending Technologies GmbH",[],[12661],[8835,53,29,12662,5466],"maximal dimension 70 cm, payload up to 1000 g",{"c":372,"m":12664,"d":46,"f":12665,"v":12666,"n":23,"y":122,"u":12667},"pendulum (sensor suite on a rope)",[],[],[12668],[2169,53,4316,12669,12670],"circling motion, acceleration up to 40 m\u002Fs2","Sec. 5.2; Sec. 5.5.2; Fig. 3d",{"c":18,"m":12672,"d":20,"f":12673,"v":12674,"n":23,"y":554,"u":12675},"Pentium IV 1.8GhZ",[],[],[12676],[12677,28,29,569,12678],"censi2008_plicp","Sec. I; Sec. V.B",{"c":18,"m":12680,"d":46,"f":12681,"v":12682,"n":23,"y":554,"u":12683},"Pentium M",[],[],[12684],[3371,28,29,12685,214],"2 GHz; laptop computer; timings of the OCaml implementation",{"c":18,"m":12687,"d":20,"f":12688,"v":12689,"n":23,"y":71,"u":12690},"Pentium-Centrino-1400 with 768 MB RAM, Linux (robot core computer)",[98],[],[12691],[547,28,29,12692,398],"on-board computer of Kurt3D; no processing times are reported on it",{"c":18,"m":12694,"d":20,"f":12695,"v":12696,"n":23,"y":61,"u":12697},"Pentium-III-600",[],[],[12698],[448,28,29,12699,12700],"offline polygon creation and object segmentation need around 1 s per typical indoor scene","Sec. 2.2.1",{"c":18,"m":12702,"d":20,"f":12703,"v":12704,"n":23,"y":61,"u":12705},"Pentium-III-800 MHz",[],[],[12706],[448,28,29,12707,12708],"384 MB RAM, real-time Linux","Sec. 2.1; Table 1",{"c":18,"m":12710,"d":20,"f":12711,"v":12712,"n":23,"y":71,"u":12713},"Pentium-IV-2800 MHz",[98],[],[12714],[547,28,29,12715,3807],"scan registration and loop detection for 77 scans about 10 min; global relaxation run for 2 h",{"c":372,"m":12717,"d":46,"f":12718,"v":12720,"n":23,"y":455,"u":12721},"Permobil electric wheelchair ('Alfred')",[12719],"Permobil",[],[12722],[1000,23,12723,12724,147],"part of the loop-detection data (data set not specified)","custom platform with hydraulic lift",{"c":372,"m":12726,"d":46,"f":12727,"v":12729,"n":23,"y":100,"u":12730},"Phasma",[12728],"Hilti (prototype)",[],[12731],[9592,23,9593,12732,12733],"handheld scanner prototype with calibrated steel tip for GCP contact; same hardware and calibration as Hilti SLAM Challenge 2022","Sec. I, Sec. III, Fig. 1b",{"c":372,"m":12735,"d":46,"f":12736,"v":12738,"n":23,"y":122,"u":12739},"Phasma handheld device",[12737],"Hilti (produced and calibrated by Hilti staff per acknowledgement)",[],[12740],[1252,23,1253,12741,12742],"milled case with dowel pins; metal needle tip at the bottom for placing on ground-truth crosshairs; URDF released; no separate LiDAR-IMU extrinsic calibration","Sec. III; Fig. 1; Sec. VI-C; Acknowledgment",{"c":372,"m":12744,"d":46,"f":12745,"v":12747,"n":23,"y":233,"u":12748},"Phasma stick",[12746],"custom sensor suite created by the authors (Sec. I: 'we have created a suite of sensors'); building organisation not stated",[],[12749],[1038,23,1039,12750,12751],"handheld sensor stick; mounted on a moving base (type not stated) in Basement 3 and 4; extrinsics from the CAD model, camera-IMU extrinsics refined with the method of Furgale et al. [25]","Sec. III, III-F; Sec. IV (a); Fig. 1",{"c":33,"m":12753,"d":46,"f":12754,"v":12755,"n":23,"y":122,"u":12756},"physical cylinder test object with pre-marked scale for the movement-rate test",[],[],[12757],[3733,41,29,569,12758],"Sec. 4.2.2, Figs. 6, 7",{"c":651,"m":12760,"d":46,"f":12761,"v":12763,"n":23,"y":152,"u":12764},"Piksi Multi RTK GPS",[12762],"Swift Navigation",[],[12765],[3036,53,29,12766,12767],"with antenna and base station; 1 Hz PPS used for synchronization; GPS measurements only in initial tests","Sec. Sensors; Sec. Mapping",{"c":372,"m":12769,"d":20,"f":12770,"v":12772,"n":23,"y":1819,"u":12773},"Pilotfly H2 gimbal",[12771],"Pilotfly",[],[12774],[2091,23,2092,12775,147],"stabilizes the Sony a7S II",{"c":372,"m":12777,"d":46,"f":12778,"v":12780,"n":23,"y":385,"u":12781},"Pioneer",[12779],"RWI\u002FISR",[],[12782],[388,53,29,12783,9145],"robots used for multi-robot mapping; one Pioneer carries two laser range finders for 3D mapping",{"c":372,"m":12785,"d":20,"f":12786,"v":12787,"n":23,"y":61,"u":12788},"Pioneer 2",[],[],[12789],[83,53,29,569,12790],"Sec. IV.A",{"c":372,"m":12792,"d":20,"f":12793,"v":12794,"n":23,"y":71,"u":12795},"Pioneer 2 DX-8",[],[],[12796],[981,53,29,982,983],{"c":372,"m":12798,"d":20,"f":12799,"v":12800,"n":23,"y":270,"u":12801},"Pioneer 3",[],[],[12802],[1807,23,12803,12804,332],"TUM RGB-D Robot SLAM sequences","Kinect mounted on the robot; wheel odometry available but not used",{"c":372,"m":12806,"d":46,"f":12807,"v":12808,"n":23,"y":1920,"u":12809},"Pioneer I",[],[],[12810],[1923,53,29,569,1924],{"c":372,"m":12812,"d":46,"f":12813,"v":12814,"n":23,"y":1920,"u":12815},"Pioneer II",[],[],[12816],[1923,53,29,569,1924],{"c":372,"m":12818,"d":46,"f":12819,"v":12820,"n":23,"y":71,"u":12821},"Pioneer II robot",[],[],[12822],[981,23,972,12823,167],"equipped with a SICK sensor",{"c":372,"m":12825,"d":46,"f":12826,"v":12827,"n":23,"y":12828,"u":12829},"Pioneer robot",[],[],2002,[12830],[12831,53,29,569,12832],"fastslam2002","Experimental Results, Fig. 4",{"c":372,"m":12834,"d":46,"f":12835,"v":12836,"n":23,"y":49,"u":12837},"Pioneer robot (virtual)",[],[],[12838],[1850,23,12839,12840,12841],"Gazebo simulated warehouse","joystick-controlled, maximum 2 m\u002Fs","Sec. IV-C1; Fig. 4b",{"c":372,"m":12843,"d":20,"f":12844,"v":12845,"n":23,"y":299,"u":12846},"Pioneer2 AT",[],[],[12847],[7000,23,12848,12849,1416],"FR-079 corridor","mobile robot carrying the pan-tilt laser",{"c":33,"m":12851,"d":46,"f":12852,"v":12853,"n":23,"y":233,"u":12854},"Pixhawk flight controller",[],[],[12855],[2239,53,29,12856,12857],"houses the BMI088 IMU; fixed at two poses I1 and I2 with CAD relative pose of 0 deg rotation and 0.25 m translation (CAD accuracy stated as 0.01 deg and millimetres)","Fig. 1, Sec. IV, Sec. IV-B.1",{"c":662,"m":12859,"d":46,"f":12860,"v":12861,"n":23,"y":100,"u":12862},"PixRacer Pro autopilot IMU",[],[],[12863],[1263,53,12340,2377,3518],{"c":108,"m":12865,"d":46,"f":12866,"v":12867,"n":23,"y":100,"u":12868},"planar LiDAR (not used)",[],[],[12869],[599,23,600,569,4427],{"c":353,"m":12871,"d":46,"f":12872,"v":12873,"n":23,"y":318,"u":12874},"platform wheel odometry (PackBot tracks and Husky wheels)",[],[],[12875],[321,53,322,12876,12877],"used by OmniMapper and Cartographer; faulty Husky encoder in configuration A runs","Sec. II-A, Sec. II-B, Sec. IV",{"c":33,"m":12879,"d":46,"f":12880,"v":12881,"n":23,"y":71,"u":12882},"plumb-line of known length over a precisely measured rectangular desktop track",[],[],[12883],[74,41,29,12884,12885],"ground-truth camera coordinates at four waypoints with an assessed 1 cm precision","Sec. 6.1; Fig. 11",{"c":33,"m":12887,"d":46,"f":12888,"v":12890,"n":23,"y":299,"u":12891},"PMD CamBoard",[12889],"PMD",[],[12892],[7393,53,29,12893,12894],"time-of-flight camera; 200x200 frames; per-pixel amplitude used in sample confidence; 27 fps input","Sec. 3, Sec. 7, Table 1, Fig. 10",{"c":33,"m":12896,"d":20,"f":12897,"v":12898,"n":23,"y":455,"u":12899},"PMD[vision] 19k time-of-flight camera",[],[],[12900],[1000,53,10142,12901,12902],"maximum range 7.5 m, 40 deg viewing angle, about 288 000 points per second","Sec. 3.1.6, 7.3.1",{"c":522,"m":12904,"d":20,"f":12905,"v":12906,"n":23,"y":37,"u":12907},"Point Grey Flea2",[8802],[],[12908],[6374,53,29,12909,1924],"30 Hz, 640x480, 24-bit RGB colour, pre-calibrated intrinsics",{"c":522,"m":12911,"d":20,"f":12912,"v":12913,"n":23,"y":318,"u":12914},"Point Grey Flea3",[8802],[],[12915],[2248,53,29,12916,12917],"wide-angle camera on a servo tilt unit on the UGV; tracks a Whycon marker (inner 50 mm, outer 122 mm) on the blimp","Sec. 3.1, 4.3.1",{"c":1689,"m":12919,"d":20,"f":12920,"v":12922,"n":23,"y":289,"u":12923},"Point Grey Research Bumblebee XB3",[12921],"Point Grey Research",[],[12924],[10062,53,29,12925,12926],"wide-baseline (24 cm) stereo cameras","Fig. 4 caption; Sec. 6.3.2",{"c":1689,"m":12928,"d":20,"f":12929,"v":12930,"n":23,"y":142,"u":12931},"Point Grey Research Bumblebee2",[12921],[],[12932],[5263,53,29,12933,12934],"2.1 mm wide angle lenses; 6.25 Hz; 640 x 480 pixels; field of view 97 x 80 deg; angular resolution 0.2 deg at centre; stereo baseline 12 cm; range estimates accurate only to about 4 or 5 m (Sec. VIII-E)","Sec. VIII-A; Sec. VIII-E; Table III",{"c":1689,"m":12936,"d":20,"f":12937,"v":12938,"n":23,"y":357,"u":12939},"Point Grey Research Flea",[12921],[],[12940],[12941,53,29,12942,12943],"sibley2010swf","two Flea cameras forming one stereo rig; about 10 cm baseline; narrow-field-of-view lenses (about 25 deg); grayscale images 1024 x 768 px; calibrated and rectified with CAHVOR camera models","Sec. 4 (Experimental Results); Fig. 10(c)",{"c":522,"m":12945,"d":20,"f":12946,"v":12948,"n":23,"y":100,"u":12949},"PointGrey (Grey) x2 and PointGrey (Colour) x2",[12947],"PointGrey",[],[12950],[9955,23,842,12951,12952],"KITTI row of Table 7; no other specs","Table 7",{"c":1689,"m":12954,"d":20,"f":12955,"v":12956,"n":23,"y":299,"u":12957},"PointGrey Bumblebee2",[12947],[],[12958],[2604,53,29,12959,1788],"stereo pair, only one camera's images used, stored at 20 Hz",{"c":1689,"m":12961,"d":20,"f":12962,"v":12963,"n":23,"y":270,"u":12964},"PointGrey Bumblebee2 stereo pair (only one camera used)",[12947],[],[12965],[339,53,29,4413,257],{"c":1689,"m":12967,"d":20,"f":12968,"v":12969,"n":23,"y":346,"u":12970},"PointGrey CM3-U3-13Y3M-CS (two, forward-looking)",[12947],[],[12971],[4101,53,4884,12972,12973],"40 Hz, 960 x 800, synchronised by the IMU trigger, external auto-exposure controller applying identical shutter and gain; Sec. III-E mentions a 20 cm baseline stereo configuration when discussing KLT stereo matching, without naming the rig","Secs. III-E, IV-B",{"c":522,"m":12975,"d":46,"f":12976,"v":12978,"n":23,"y":71,"u":12979},"Pointgrey FireFly",[12977],"Pointgrey (as written)",[],[12980],[2564,53,29,12981,478],"640 x 480 pixels at 3 Hz",{"c":1689,"m":12983,"d":20,"f":12984,"v":12985,"n":23,"y":142,"u":12986},"PointGrey Flea2",[12947],[],[12987],[12546,23,842,12988,12989],"two colour and two greyscale cameras, 10 Hz, 1392 x 512 pixels, opening 90 x 35 deg; baseline about 54 cm between same-type cameras, 6 cm between colour and greyscale; benchmarks use greyscale","Sec. 2.1, Sec. 2.4",{"c":372,"m":12991,"d":46,"f":12992,"v":12993,"n":23,"y":122,"u":12994},"PointPix prototypical hand-held scanner",[],[],[12995],[1279,23,1280,12996,12997],"sensors fixed to a custom aluminium frame with a handle","Sensors and devices; Fig. 1a",{"c":372,"m":12999,"d":20,"f":13000,"v":13002,"n":23,"y":1008,"u":13003},"Polaris RZR S4 1000 Turbo (customized, RACER)",[13001],"Polaris",[],[13004],[1476,53,13005,13006,5457],"RACER off-road dataset","three LiDARs, mm-wave radar, single GNSS antenna and wheel encoder; 4.1 km at up to 9.66 m\u002Fs",{"c":6267,"m":13008,"d":46,"f":13009,"v":13011,"n":23,"y":152,"u":13012},"Pozyx UWB target board",[13010],"Pozyx",[],[13013],[3116,53,29,13014,13015],"100 Hz, 100 m range with clear line of sight; one anchor board placed on the tunnel floor at a position measured in the prior map","Sec. IV-A, IV-C; Fig. 5",{"c":1776,"m":13017,"d":46,"f":13018,"v":13019,"n":23,"y":152,"u":13020},"PR2 head RGB-D camera (model not reported)",[],[],[13021],[2555,23,13022,13023,3230],"MIT Stata Center","replayed at 15 Hz; depth scaled by 1.043; used for loop closure in lidar configurations",{"c":1689,"m":13025,"d":46,"f":13026,"v":13027,"n":23,"y":152,"u":13028},"PR2 head stereo camera (model not reported)",[],[],[13029],[2555,23,13022,13030,3230],"replayed at 15 Hz; baseline scaled by 1.091664 to match lidar scale",{"c":372,"m":13032,"d":20,"f":13033,"v":13034,"n":41,"y":37,"u":13035},"PR2 robot",[],[],[13036,13038],[2555,23,13022,13037,3230],"teleoperated in an office building; lidar on the base, cameras on the head",[13039,53,29,13040,13041],"rusu2011pcl","platform of the door and handle identification, NARF object recognition and grasping examples, which are reproduced from cited works [8]-[11]","Sec. V; Figs. 9-10",{"c":1776,"m":13043,"d":20,"f":13044,"v":13046,"n":23,"y":132,"u":13047},"PrimeSense Carmine 1.09 (cited as the RGB-D sensor type of the Matterport Camera)",[13045],"PrimeSense",[],[13048],[10167,23,10168,13049,13050],"statistical noise model of the Primesense sensor used to simulate fragments; simulated max depth 4.5 m, sensor height 1.5-1.75 m","Sec. 4.2.2, ref. [158]",{"c":1776,"m":13052,"d":20,"f":13053,"v":13054,"n":23,"y":142,"u":13055},"PrimeSense RGB-D camera (equivalent to the sensor in the Microsoft Kinect)",[13045],[],[13056],[145,53,29,13057,13058],"640 x 480 registered image and depth points at 30 frames per second; active stereo with an IR emitter and IR camera; RGB-D cameras described as having depth typically less than 5 m, noise about 3 cm at 3 m and a field of view of about 60 deg","Sec. 1 (p. 647); Secs. 3.1.1, 4.5; Acknowledgements",{"c":33,"m":13060,"d":46,"f":13061,"v":13062,"n":23,"y":318,"u":13063},"professional mine survey of artifacts and fiducials (AprilTags, IR reflectors, prisms, spheres); instruments not reported",[],[],[13064],[321,41,322,13065,13066],"surveyed positions in the darpa frame; surveyed ground-truth point clouds shown in Fig. 6","Sec. II, Fig. 4, Fig. 6",{"c":33,"m":13068,"d":20,"f":13069,"v":13070,"n":23,"y":132,"u":13071},"Prophesse EVALUATION KIT Gen3M VGA-CD 1.1",[],[],[13072],[5348,23,7967,13073,7969],"name given in parentheses directly after 'an event-based camera, and a 360-degree camera'; the text does not make clear whether it names the event-based or the 360-degree camera, so no camera category is assigned",{"c":372,"m":13075,"d":46,"f":13076,"v":13077,"n":23,"y":142,"u":13078},"prototype mobile scanning platform (vehicle; model not reported)",[],[],[13079],[6489,53,29,13080,13081],"carries the HDL-64E S2, IMU and GPS receiver with raw data logging","Experimental Description; Fig. 2",{"c":33,"m":13083,"d":46,"f":13084,"v":13085,"n":23,"y":759,"u":13086},"prototype video-rate structured-light range scanner",[],[],[13087],[762,53,29,13088,13089],"captured the two elephant-figurine scans aligned in Fig. 17","Sec. 4; Fig. 17",{"c":372,"m":13091,"d":46,"f":13092,"v":13093,"n":23,"y":1819,"u":13094},"Pushed cart (indoor)",[],[],[13095],[5093,53,29,13096,5095],"carries lidar, battery and laptop; pushed by a walking person at 0.5 m\u002Fs",{"c":18,"m":13098,"d":20,"f":13099,"v":13100,"n":23,"y":289,"u":13101},"quad-core 3.50 GHz Intel Core i7",[98],[],[13102],[292,28,29,13103,294],"single thread per registration, four batches in parallel; used for NDT",{"c":18,"m":13105,"d":20,"f":13106,"v":13107,"n":23,"y":1819,"u":13108},"quad-core Intel i7 2.5 GHz",[98],[],[13109],[1764,28,29,13110,214],"one thread used",{"c":372,"m":13112,"d":46,"f":13113,"v":13114,"n":23,"y":49,"u":13115},"Quadcopter UAV (simulated in Gazebo)",[],[],[13116],[52,53,54,13117,12411],"manual flight in dataset 1; datasets 2-4 follow this trajectory with an autonomous flight controller, max 0.5 m\u002Fs (Sec. IV-A); control mode for the Eiffel Tower datasets 5-6 not stated",{"c":18,"m":13119,"d":20,"f":13120,"v":13121,"n":23,"y":270,"u":13122},"quadcore notebook with Intel i7-Q720 CPU (Cosero's main computer)",[98],[],[13123],[2944,28,29,13124,2946],"RGB-D images subsampled to QVGA for tracking during RoboCup@Home demonstrations",{"c":372,"m":13126,"d":46,"f":13127,"v":13128,"n":23,"y":152,"u":13129},"quadrotor (model not reported)",[],[],[13130],[5455,53,29,13131,5457],"flew aggressive autonomous paths planned on the map reconstructed from the handheld monocular camera sequence (Sec. VI-D)",{"c":372,"m":13133,"d":20,"f":13134,"v":13135,"n":23,"y":318,"u":13136},"quadrotor based on DJI Matrice M100",[3112],[],[13137],[4412,53,29,13138,332],"aerial robot used in the self-similar underpass experiment",{"c":372,"m":13140,"d":46,"f":13141,"v":13142,"n":23,"y":122,"u":13143},"quadruped (model not named)",[],[],[13144],[1349,23,5484,13145,745],"carried the sensors for MCR Slow 01, a small indoor lab scene",{"c":372,"m":13147,"d":46,"f":13148,"v":13149,"n":41,"y":233,"u":13150},"quadruped robot (model not reported)",[],[],[13151,13154],[405,53,29,13152,13153],"three motors per leg (knee, thigh, hip); safe distance 0.3 m from walls and furniture during planning","Sec. 3.1, 4.1, Fig. 1",[1562,23,1563,13155,13156],"planar but jerky motion","Fig. 1c; Table III",{"c":33,"m":13158,"d":46,"f":13159,"v":13161,"n":23,"y":1008,"u":13162},"Qualisys mocap system",[13160],"Qualisys",[],[13163],[1476,41,13164,13165,1204],"ANYmal parkour and indoor (authors)","ground truth for parkour and indoor experiments",{"c":33,"m":13167,"d":20,"f":13168,"v":13169,"n":23,"y":132,"u":13170},"Qualisys motion capture: 12 Oqus 700+ and 8 Arqus A12 cameras with passive markers",[13160],[],[13171],[1510,41,2337,13172,294],"submillimeter, low latency",{"c":108,"m":13174,"d":20,"f":13175,"v":13177,"n":23,"y":346,"u":13178},"Quanergy M8",[13176],"Quanergy",[],[13179],[1091,53,29,13180,5457],"eight-channel LiDAR operating at 10 Hz",{"c":108,"m":13182,"d":46,"f":13183,"v":13184,"n":23,"y":346,"u":13185},"Quanergy M8 (simulated)",[13176],[],[13186],[1091,23,1092,13187,1094],"simulator modelled on it: 0.25 deg angular resolution, 8 zenith angles from 3.2 to -18.3 deg, 1 cm and 3 cm point deviation, 5 Hz",{"c":944,"m":13189,"d":20,"f":13190,"v":13192,"n":23,"y":100,"u":13193},"R100 and S100",[13191],"DASPATIAL",[],[13194],[9955,952,29,13195,9957],"Listed in the survey's commercial catalogue only (sensor class L, I, V), not tested: LiDAR, IMU and panoramic cameras; handheld and backpack modes",{"c":372,"m":13197,"d":46,"f":13198,"v":13199,"n":23,"y":122,"u":13200},"racing quadrotor drone",[],[],[13201],[2169,53,29,13202,13203],"thrust-to-weight ratio up to 5.4; carries Livox Avia and FPV camera; angular velocity up to 59.37 rad\u002Fs","Sec. 7.1; Fig. 18a",{"c":33,"m":13205,"d":46,"f":13206,"v":13207,"n":23,"y":759,"u":13208},"radar reflectors (10, surveyed)",[],[],[13209],[888,41,29,13210,1426],"omni-directional point landmarks whose locations were accurately surveyed; used to evaluate the map and to compute the reference vehicle path (about 5 cm absolute accuracy)",{"c":33,"m":13212,"d":46,"f":13213,"v":13214,"n":23,"y":61,"u":13215},"range finder",[],[],[13216],[245,23,3362,13217,3364],"low-noise; used for landmark detection (type not stated)",{"c":33,"m":13219,"d":46,"f":13220,"v":13221,"n":23,"y":6352,"u":13222},"range finder set-up described by Sato and Inokuchi (projector with programmable liquid crystal mask and CCD camera)",[],[],[13223],[13224,53,29,13225,13226],"chen1992pointtoplane","space coding with projected stripe pattern and triangulation; accuracy in the neighbourhood of 1 mm; spatial resolution of range images 0.5 mm","p. 146; p. 151",{"c":33,"m":13228,"d":46,"f":13229,"v":13230,"n":23,"y":61,"u":13231},"range scanner used for Forma Urbis Romae scans (model not named in the paper)",[],[],[13232],[734,23,13233,13234,13235],"Forma Urbis Romae","meshes of about 300,000 points","Sec. 5; Acknowledgements",{"c":33,"m":13237,"d":46,"f":13238,"v":13239,"n":23,"y":346,"u":13240},"Raspberry Pi",[],[],[13241],[2739,53,29,13242,1924],"relays joystick commands to the Husky and returns wheel encoder data over TCP",{"c":18,"m":13244,"d":20,"f":13245,"v":13246,"n":23,"y":318,"u":13247},"Raspberry Pi (UGV) and Raspberry Pi 3 (blimp)",[],[],[13248],[2248,53,29,13249,13250],"actuator control and sensor streaming over a router on the UGV (blimp via Wi-Fi)","Sec. 3, 3.2",{"c":18,"m":13252,"d":20,"f":13253,"v":13254,"n":23,"y":132,"u":13255},"Raspberry Pi 4",[],[],[13256],[537,53,29,13257,13258],"on-robot interface between LiDAR and robot running custom middleware (Spot API); stores all sensor data on its internal memory; powered by the robot battery; processing hardware for SLAM not reported","Hardware Setup; Software and Algorithms",{"c":522,"m":13260,"d":20,"f":13261,"v":13262,"n":23,"y":318,"u":13263},"Raspberry Pi camera v2",[],[],[13264],[2248,53,29,13265,13266],"rolling shutter; 19 g; downward-looking on the blimp; streamed at 410 x 308 and 30 fps","Sec. 3.2, 3.2.1, 4.3.2",{"c":18,"m":13268,"d":20,"f":13269,"v":13270,"n":23,"y":132,"u":13271},"Raspberry Pi, two Jetson Nano and one Jetson NX (Go1 Edu onboard processors)",[],[],[13272],[2352,28,29,13273,6342],"run the navigation and localization modules onboard the robot (Sec. 3, Sec. 3.1); the split of work with the added DJI Manifold-2C is not stated",{"c":18,"m":13275,"d":20,"f":13276,"v":13278,"n":23,"y":233,"u":13279},"Razer Blade 15",[13277],"Razer",[],[13280],[2265,28,29,13281,863],"11th Gen Intel Core i7 (8 cores), 32 GB RAM, NVIDIA GeForce RTX 3080, Windows 11; runs all components in the handheld setup and localization plus visualization in the robot setup",{"c":18,"m":13283,"d":20,"f":13284,"v":13286,"n":23,"y":132,"u":13287},"RB5 (ARM) with Qualcomm Kryo585 CPU",[13285],"Qualcomm",[],[13288],[216,28,29,11676,3273],{"c":18,"m":13290,"d":20,"f":13291,"v":13292,"n":23,"y":233,"u":13293},"RB5 with Qualcomm Kryo585 CPU",[13285],[],[13294],[3255,28,29,13295,13296],"embedded ARM platform","Sec. VI-C; Table III",{"c":372,"m":13298,"d":46,"f":13299,"v":13300,"n":23,"y":100,"u":13301},"RC car (model not reported)",[],[],[13302],[1371,23,1372,13303,5508],"max 4 m\u002Fs (Long Corridor), 2 m\u002Fs (Smoke Room)",{"c":651,"m":13305,"d":46,"f":13306,"v":13307,"n":23,"y":318,"u":13308},"Reach M",[],[],[13309],[2758,53,29,13310,13311],"optional GPS factor; also the RMSE reference in the Park dataset","Sec. IV; Sec. IV-F",{"c":651,"m":13313,"d":46,"f":13314,"v":13316,"n":23,"y":132,"u":13317},"Reach M+ (RTK)",[13315],"Emlid",[],[13318],[225,41,854,13319,228],"5 Hz; outdoor ground truth",{"c":651,"m":13321,"d":46,"f":13322,"v":13323,"n":23,"y":49,"u":13324},"Reach RS+ GPS",[],[],[13325],[2785,41,29,13326,13327],"used as ground truth where reception is available","Sec. III; Fig. 8",{"c":662,"m":13329,"d":20,"f":13330,"v":13331,"n":23,"y":100,"u":13332},"Realsense D435i IMU (written 'VI-sensor Realsense d435i')",[],[],[13333],[4756,23,4757,1272,478],{"c":522,"m":13335,"d":46,"f":13336,"v":13337,"n":23,"y":346,"u":13338},"regular digital camera (model not stated)",[],[],[13339],[13340,53,29,13341,13342],"kim2018construction","eight pictures per 360 deg scan; intrinsics from pinhole model, extrinsics from mounting kinematics","Data Acquisition and Fusion; Eq. 2",{"c":18,"m":13344,"d":46,"f":13345,"v":13346,"n":23,"y":132,"u":13347},"remote computation layer (laptop in Fig. 1; model not reported)",[],[],[13348],[405,28,29,13349,13350],"scan planning optimization; communicates with robot via portable Wi-Fi","Sec. 3.1, Fig. 1",{"c":18,"m":13352,"d":20,"f":13353,"v":13354,"n":23,"y":132,"u":13355},"remote laptop with Intel i7-9750H and RTX 2060",[],[],[13356],[2352,28,29,13357,6342],"visualization, commanding, reconstruction and DL inference",{"c":18,"m":13359,"d":46,"f":13360,"v":13361,"n":23,"y":122,"u":13362},"remote server (model not reported)",[],[],[13363],[8013,28,29,13364,8015],"computes scan positions and starts or stops navigation over Wi-Fi",{"c":33,"m":13366,"d":20,"f":13367,"v":13369,"n":23,"y":152,"u":13370},"Renishaw XL-80 interferometer",[13368],"Renishaw",[],[13371],[9620,41,29,13372,13373],"ground-truth board distance with accuracy of the order of 1 micrometre","Sec. IV, Figs. 5-6",{"c":33,"m":13375,"d":46,"f":13376,"v":13377,"n":23,"y":554,"u":13378},"retro-reflective targets of known coordinates",[],[],[13379],[557,41,558,13380,257],"used for manual alignment that serves as ground truth",{"c":108,"m":13382,"d":46,"f":13383,"v":13385,"n":23,"y":1421,"u":13386},"Revo LDS",[13384],"Neato Robotics",[],[13387],[2638,53,29,13388,13389],"laser distance sensor used in Neato vacuum cleaners, costs under $30; scans taken at approximately 2 Hz over its debug connection","Sec. VI.B",{"c":522,"m":13391,"d":46,"f":13392,"v":13393,"n":23,"y":233,"u":13394},"RGB camera (four per SpinningPack, model not stated)",[],[],[13395],[668,23,669,13396,671],"part of the SpinningPack; not used in the described Wildcat pipeline",{"c":522,"m":13398,"d":46,"f":13399,"v":13400,"n":23,"y":132,"u":13401},"RGB camera (model not named)",[],[],[13402,13405],[6518,23,13403,13404,10205],"FAST-LIVO dataset; R3LIVE dataset","640x512 images; only left images used when stereo is provided",[6518,23,10203,13406,10205],"640x480 images; only left images used when stereo is provided",{"c":522,"m":13408,"d":46,"f":13409,"v":13410,"n":28,"y":132,"u":13411},"RGB camera (model not reported)",[],[],[13412,13414,13417],[610,53,611,13413,1667],"pinhole-plus-distortion projection model; calibrated LiDAR-to-camera extrinsics",[1101,53,2142,13415,13416],"1800x1200, 10 Hz; resized to 900x600 on the ARM computer (Table I; Sec. IV-D)","Table I; Sec. IV-D",[2423,23,5376,13418,917],"15 Hz, 640 x 512",{"c":522,"m":13420,"d":46,"f":13421,"v":13422,"n":23,"y":1008,"u":13423},"RGB camera (model not stated)",[],[],[13424],[2018,53,2019,13425,5448],"mounted on top of the frame; used only for point colorization",{"c":522,"m":13427,"d":46,"f":13428,"v":13429,"n":23,"y":233,"u":13430},"RGB camera (single-beam hand-held device, model not stated)",[],[],[13431],[2493,53,29,13432,2494],"used for colourisation and visual loop detection",{"c":522,"m":13434,"d":46,"f":13435,"v":13436,"n":23,"y":233,"u":13437},"RGB camera for visual validation images (model not reported)",[],[],[13438],[3821,41,29,569,13439],"Sec. 5.2, Fig. 18",{"c":522,"m":13441,"d":46,"f":13442,"v":13443,"n":23,"y":152,"u":13444},"RGB texture camera on the rig (model not reported)",[],[],[13445],[3089,23,3054,13446,898],"exposure times cycled to build 16-bit floating point HDR textures",{"c":1776,"m":13448,"d":46,"f":13449,"v":13450,"n":23,"y":49,"u":13451},"RGB-D camera",[],[],[13452],[92,23,13453,13454,13455],"ETH3D SLAM","ETH3D-SLAM benchmark input; 'dark' datasets without image data skipped","Sec. 4 (ETH3D-SLAM)",{"c":1776,"m":13457,"d":46,"f":13458,"v":13459,"n":23,"y":122,"u":13460},"RGB-D camera (model not reported)",[],[],[13461],[4780,23,29,13462,13463],"labelled in Fig. 6 as part of the portable mapping system; its use is not described in the text","Fig. 6",{"c":1776,"m":13465,"d":46,"f":13466,"v":13467,"n":23,"y":346,"u":13468},"RGB-D camera (RealSense colour camera; exact model not stated)",[],[],[13469],[13470,41,29,13471,13472],"legentil2018lidarimucalib","rolling-shutter colour camera; not used by the proposed method, only for the chained Kalibr and camera-LiDAR reference calibration","Sec. IV-B, Fig. 8",{"c":1776,"m":13474,"d":46,"f":13475,"v":13476,"n":23,"y":233,"u":13477},"RGB-D camera of the Cow and Lady dataset (model not named)",[],[],[13478],[517,23,5925,13479,441],"points within 0.1-5 m used, 2 mm voxels, truncation 3 voxels",{"c":1776,"m":13481,"d":46,"f":13482,"v":13483,"n":23,"y":299,"u":13484},"RGB-D camera of the TUM RGB-D benchmark (model not named in this paper)",[],[],[13485],[4061,23,991,13486,127],"not_reported (the method processes the images coarse-to-fine at three resolutions up to 320 x 240 pixels)",{"c":522,"m":13488,"d":46,"f":13489,"v":13490,"n":23,"y":122,"u":13491},"RGB-inertial sensor (cited as VersaVIS, ref. [52])",[],[],[13492],[1029,53,13493,569,3518],"custom indoor office semantic dataset",{"c":522,"m":13495,"d":20,"f":13496,"v":13497,"n":23,"y":100,"u":13498},"RGB-zoom camera on 3-axis gimbal (RA2)",[],[],[13499],[2281,53,29,13500,13501],"mounted with a thermal camera on a 3-axis gimbal; the obstacle photo in Sec. 4.3 was taken with RA2's 'front camera', which the paper does not identify further","VoR Sec. 4.2.2, 4.3",{"c":1776,"m":13503,"d":46,"f":13504,"v":13505,"n":23,"y":100,"u":13506},"RGBD camera (model not named)",[],[],[13507],[103,53,13508,13509,13510],"Replica; TUM-RGBD; ScanNet","RGBD stream is the only input; TUM-RGBD and ScanNet are real-world data, the Replica trajectories come from a simulated RGBD sensor","Abstract; Fig. 2; Sec. 4 Datasets",{"c":108,"m":13512,"d":20,"f":13513,"v":13515,"n":23,"y":142,"u":13516},"Riegl LMS-Q120i",[13514],"Riegl",[],[13517],[6489,952,29,13518,13519],"range accuracy 15 mm, beam divergence 2.7 mrad, angular resolution 0.01 deg, 10,000 Hz; same platform and trajectory, boresight-only adjustment","Table 6; Table 7",{"c":108,"m":13521,"d":20,"f":13522,"v":13523,"n":23,"y":71,"u":13524},"Riegl Q-140",[13514],[],[13525],[1573,53,29,13526,13527],"helicopter system with Honeywell HG1700 IMU; 100 m AGL, four directions, 115 tie points","Comparison section: helicopter system; Table 7",{"c":108,"m":13529,"d":20,"f":13530,"v":13531,"n":23,"y":71,"u":13532},"Riegl Q-240",[13514],[],[13533],[1573,952,29,13534,1659],"range error 0.02 m, angular resolution 0.005 deg, beam divergence 2.7 mrad, total angular error 0.039 deg; used for helicopter simulations",{"c":108,"m":13536,"d":20,"f":13537,"v":13538,"n":23,"y":71,"u":13539},"Riegl Q-280",[13514],[],[13540],[1573,952,29,13541,1659],"range error 0.02 m, angular resolution 0.0025 deg, beam divergence 0.5 mrad, total angular error 0.0076 deg; used for helicopter simulations",{"c":944,"m":13543,"d":20,"f":13544,"v":13546,"n":23,"y":299,"u":13547},"RIEGL VMX-250",[13545],"RIEGL",[],[13548],[1606,952,29,13549,13550],"two RIEGL VQ-250 and APPLANIX IMU-31 (POS LV 510); post-processed X-Y 0.020 m, Z 0.050 m, roll\u002Fpitch 0.005 deg, true heading 0.015 deg; mounting in no more than 10 min","Sec. 3.5, Table 1",{"c":108,"m":13552,"d":20,"f":13553,"v":13554,"n":23,"y":299,"u":13555},"RIEGL VQ-250",[13545],[],[13556],[1606,952,29,13557,13558],"TOF full-circle 360 deg scanner; up to 200 m (300 kHz, reflectivity 80%); 50 to 300 kHz; up to 100 Hz; range accuracy 10 mm at 150 m (1 sigma); full-waveform echo digitization; about 11 kg","Sec. 3.3 to 3.5, Table 3",{"c":2807,"m":13560,"d":20,"f":13561,"v":13562,"n":23,"y":318,"u":13563},"Riegl VZ series et al.",[13545],[],[13564],[771,23,13565,569,691],"UNAVCO TLS Archive (reviewed)",{"c":2807,"m":13567,"d":20,"f":13568,"v":13569,"n":952,"y":270,"u":13571},"Riegl VZ-400",[13545,13514],[13570],"RIEGL VZ-400",[13572,13575,13578,13581,13586],[2407,53,29,13573,13574],"field of view 360 deg x 100 deg; stated accuracy 5 mm; head rotation used to carry the cameras","Sec. 3.1, Sec. 6",[771,23,13576,569,13577],"WHU-TLS (railway, park, campus, riverbank, heritage building, underground excavation, tunnel)","Table 2; Sec. 4.2, 4.5-4.6, 4.8-4.11",[771,23,13579,569,13580],"Robotic 3D Scan Repository and Jacobs University Bremen campus data (reviewed)","Sec. 1; Table 1; Sec. 3.3",[13582,23,13583,13584,13585],"schauer2018peopleremover","lecturehall, campus, wrzburg","used by the authors to record the lecturehall, campus (146 scans, about 15 million points per scan) and wrzburg (6 scans, 86 million points) datasets","Sec. IX, Table I",[7554,53,29,13587,13588],"multiple echo returns; angular resolution 0.06° azimuthal and 0.06° polar; about 5 to 6 million points per model","Sec. 2, 5",{"c":108,"m":13567,"d":20,"f":13590,"v":13591,"n":23,"y":142,"u":13592},[13514],[],[13593],[6489,952,29,13594,13519],"range accuracy 5 mm, beam divergence 0.3 mrad, angular resolution 0.0005 deg, 125,000 Hz; same platform and trajectory, boresight-only adjustment",{"c":2807,"m":13596,"d":20,"f":13597,"v":13598,"n":41,"y":122,"u":13599},"RIEGL VZ-400i",[13545],[],[13600,13603],[537,41,29,13601,13602],"terrestrial laser scanner, millimetre accuracy (as stated)","Hardware Setup; Metrics for Evaluation",[3761,41,29,13604,13605],"500,000 pts\u002Fs, 800 m, accuracy 5 mm and precision 3 mm at 100 m; 60 stations about 5 m apart, 9 mm grid at 10 m, over 931 million points; RiSCAN PRO plane-to-plane registration with 171,197 patches, SD 2.6 mm; cropped and subsampled to about 11 million points","Sec. 2.4; Table 1",{"c":2807,"m":13607,"d":20,"f":13608,"v":13609,"n":23,"y":132,"u":13610},"RIEGL VZ-600i",[13545],[],[13611],[537,41,29,13601,13602],{"c":2807,"m":13613,"d":20,"f":13614,"v":13616,"n":23,"y":49,"u":13617},"Rigel VZ-1000",[13615],"RIEGL (spelled 'Rigel' in the paper)",[],[13618],[1976,41,1977,13619,13620],"high accuracy TLS point cloud of floors 1 and 2 used as map ground truth","Sec. IV-B1, Fig. 9",{"c":18,"m":13622,"d":20,"f":13623,"v":13624,"n":23,"y":132,"u":13625},"RK3588",[],[],[13626],[135,28,29,13627,13628],"octa-core 4x Cortex-A76 + 4x Cortex-A55, max 2.4 GHz, about 100 USD; CPU only","Sec. V-A3; Fig. 1",{"c":372,"m":13630,"d":46,"f":13631,"v":13632,"n":23,"y":100,"u":13633},"RMF-Owl",[],[],[13634],[1396,53,29,13635,332],"aerial robot relying on a single-board computer, running CompSLAM",{"c":372,"m":13637,"d":20,"f":13638,"v":13639,"n":23,"y":122,"u":13640},"RoboMaster 2019 AI robot car",[3112],[],[13641],[2169,53,4316,13642,13643],"sensor suite mounted on chassis without vibration absorber","Sec. 5.2; Sec. 5.3; Fig. 3b",{"c":108,"m":13645,"d":46,"f":13646,"v":13648,"n":23,"y":122,"u":13649},"RoboSense 16-line LiDAR (model not stated)",[13647],"RoboSense",[],[13650],[1191,53,2307,185,2309],{"c":108,"m":13652,"d":20,"f":13653,"v":13654,"n":41,"y":100,"u":13656},"Robosense Bpearl",[2294],[13655],"Robosense BPearl",[13657,13660],[9592,23,9593,13658,13659],"horizontally mounted hemispherical LiDAR, 32 rays, +\u002F-3 cm noise; synchronized to the PC directly via PTP; triggering on a time basis rather than encoder angles; a GCP within 90 cm always crossed by at least 3 scan lines","Sec. I, Sec. III, Sec. III-D",[216,23,3780,13661,157],"10 Hz (robot-mounted sequences)",{"c":108,"m":13663,"d":20,"f":13664,"v":13665,"n":23,"y":152,"u":13666},"Robosense RS-LiDAR-16",[13647],[],[13667],[9620,53,29,13668,13669],"3D LiDAR lent by Robosense; fitted aperture half-angle 0.085 deg (Table I)","Sec. IV, V, Table I",{"c":372,"m":13671,"d":46,"f":13672,"v":13673,"n":23,"y":132,"u":13674},"robot car",[],[],[13675],[790,53,2000,13676,13677],"drives of 5.0 km (about 10,000 scans and 40,000 images) and 3.7 km","Sec. IV-A1; Table III",{"c":353,"m":13679,"d":46,"f":13680,"v":13681,"n":23,"y":122,"u":13682},"robot encoders (model not reported)",[],[],[13683],[9143,53,29,13684,13685],"odometry source for all in-house real sequences","Sec. VI-A (In-House Dataset)",{"c":372,"m":13687,"d":46,"f":13688,"v":13689,"n":23,"y":233,"u":13690},"robot equipped with McLam wheels (Experiment I)",[],[],[13691],[236,53,29,13692,13693],"remote-controlled with a handle; carries the LiDAR, IMU and wheel encoders","Sec. IV-A (Experiment I)",{"c":33,"m":13695,"d":46,"f":13696,"v":13697,"n":23,"y":554,"u":13698},"robot odometry (x, z, theta_y), source not stated",[],[],[13699],[557,53,29,13700,1924],"planar 3-DoF odometry used only to extrapolate the initial 6-DoF pose",{"c":372,"m":13702,"d":46,"f":13703,"v":13704,"n":23,"y":289,"u":13705},"robot traversing a campus and adjacent parks (NewCollege)",[],[],[13706],[2595,23,10982,13707,157],"2.2 km sequence with several loops and fast rotations",{"c":108,"m":13709,"d":46,"f":13710,"v":13711,"n":23,"y":554,"u":13712},"robot-mounted 3D laser range finder (model not stated)",[],[],[13713],[557,23,10581,13714,13715],"468 scans of 14,000 to 18,000 points each; stop-and-scan acquisition","Sec. 6.1; Sec. 7.2; Acknowledgments",{"c":372,"m":13717,"d":20,"f":13718,"v":13719,"n":23,"y":346,"u":13720},"robotic hybrid 3D LiDAR system (customized, on a mobile robot platform)",[],[],[13721],[13340,53,29,13722,13723],"body rotation angle theta1 and scanner angle theta2 kinematics; base frame at ground level of the mobile robot platform","Fig. 2; Eq. 1",{"c":372,"m":13725,"d":46,"f":13726,"v":13727,"n":23,"y":152,"u":13728},"robotic powerchair",[],[],[13729],[1674,53,29,13730,13731],"localized in real time on the OGM for A* path planning and navigation demonstrations","Sec. 6.1, Fig. 13",{"c":372,"m":13733,"d":46,"f":13734,"v":13735,"n":23,"y":554,"u":13736},"robotic wheel-chair",[],[],[13737],[12677,23,13738,13739,127],"Minguez et al. (2006) scan log","scans taken about every 0.3 m with considerable odometry slip",{"c":372,"m":13741,"d":20,"f":13742,"v":13743,"n":23,"y":152,"u":13744},"robotic wheelchair equipped with a Kinect",[],[],[13745],[1674,53,29,13746,4867],"moved for five complete loops along an 80 m basement corridor",{"c":372,"m":13748,"d":46,"f":13749,"v":13751,"n":41,"y":122,"u":13752},"Robotnik SUMMIT XL (simulated)",[13750],"Robotnik",[],[13753,13755],[6102,53,6103,13754,3677],"holonomic; commanded at about 1 m\u002Fs and 1 deg\u002Fs in Gazebo",[4780,53,13756,13757,917],"Gazebo simulated sequence","simulated robot in Gazebo navigating the BIM-derived path",{"c":372,"m":13759,"d":20,"f":13760,"v":13761,"n":23,"y":100,"u":13762},"Robotnik SUMMIT-XL (modified, RA1)",[13750],[],[13763],[2281,53,29,13764,13765],"four-wheeled ground robot with mecanum wheels for omnidirectional motion","VoR Sec. 4.2.1, Fig. 8",{"c":372,"m":13767,"d":46,"f":13768,"v":13769,"n":23,"y":6352,"u":13770},"rotary table",[],[],[13771],[13224,53,29,13772,13773],"4 to 8 side views (8 views at 45 deg in the results) plus top and bottom views","pp. 152-153",{"c":108,"m":13775,"d":46,"f":13776,"v":13777,"n":23,"y":49,"u":13778},"rotating 3D LiDAR of the KITTI odometry benchmark (model not named in the paper)",[],[],[13779],[8131,23,832,13780,13781],"real-world odometry, registration and memory experiments on KITTI sequences 00-10","Sec. IV-A; Tables II-III",{"c":372,"m":13783,"d":20,"f":13784,"v":13786,"n":23,"y":122,"u":13787},"rotating platform driven by Nimotion STM4260A step motor",[13785],"Nimotion (motor)",[],[13788],[2169,53,4316,13789,13790],"peak yaw rate 75 rad\u002Fs in Satu-1","Sec. 5.2; Sec. 5.5.1; Fig. 3c",{"c":108,"m":13792,"d":20,"f":13793,"v":13795,"n":23,"y":142,"u":13796},"rotating SICK laser",[13794],"SICK",[],[13797],[13798,23,13799,13800,13801],"stoyanov2012d2dndt","AASS loop and Hannover2 (3D scans online repository)","real-world data sets acquired with SICK laser scanners on a rotating mount; AASS: 60 point clouds of about 90,000 points; Hannover: 923 point clouds of about 15,000 points (Table 1)","Sec. 6.1, 6.2, Table 1",{"c":108,"m":13803,"d":20,"f":13804,"v":13805,"n":23,"y":132,"u":13806},"RPLiDAR A2",[],[],[13807],[5348,23,7967,13808,7969],"2D LiDAR; no specifications given",{"c":108,"m":13810,"d":20,"f":13811,"v":13812,"n":23,"y":132,"u":13813},"RPLiDAR S1",[],[],[13814],[5348,23,7967,13808,7969],{"c":108,"m":13816,"d":20,"f":13817,"v":13818,"n":41,"y":132,"u":13819},"RS-Helios-16P",[],[],[13820,13822],[2447,53,2448,13821,11008],"360 deg horizontal, 30 deg vertical (-15 to +15 deg) FoV; 0.4 deg horizontal and 2 deg vertical resolution; 10 Hz; +\u002F-2 cm accuracy; 0.2 to 150 m range; mounted horizontally at 0.88 m",[2865,53,29,13823,13824],"16 beams; 360 deg horizontal and +-15 deg vertical FOV; 0.4 deg horizontal and 2 deg vertical resolution; 10 Hz; +-2 cm ranging; 0.2 to 150 m; mounted horizontally at 0.88 m","Sec. 3.2, Fig. 2",{"c":108,"m":13826,"d":20,"f":13827,"v":13828,"n":23,"y":318,"u":13829},"RS-LiDAR-16",[],[],[13830],[2391,53,29,13831,6549],"mounted on top of a bus (indoor parking lot and urban tests)",{"c":651,"m":13833,"d":46,"f":13834,"v":13835,"n":23,"y":132,"u":13836},"RTK",[],[],[13837],[135,41,13838,13839,13840],"MARS-LVIG HKIsland03","RTK trajectory used as ground truth","Sec. V-E1",{"c":651,"m":13842,"d":46,"f":13843,"v":13844,"n":23,"y":1008,"u":13845},"RTK (low-rate, provided with MARS-LVIG) post-processed with RTK\u002FINS software into high-rate ground truth",[],[],[13846],[1101,41,1137,569,6265],{"c":651,"m":13848,"d":46,"f":13849,"v":13850,"n":23,"y":233,"u":13851},"RTK (model not reported)",[],[],[13852],[1340,41,13853,13854,13855],"NCLT; UTBM robocar dataset","reference ground-truth trajectories of the selected sequences","Sec. V-C; Fig. 11",{"c":651,"m":13857,"d":46,"f":13858,"v":13859,"n":23,"y":132,"u":13860},"RTK GNSS",[],[],[13861],[282,41,13862,13863,13864],"UAL VLP-16 campus","3D positioning used as ground truth","Sec. 4.10",{"c":651,"m":13866,"d":46,"f":13867,"v":13868,"n":41,"y":152,"u":13869},"RTK GPS",[],[],[13870,13872],[1029,53,29,13871,469],"optional absolute pose constraints where available",[2080,41,2987,13873,717],"centimeter-level accuracy, used as outdoor ground truth",{"c":651,"m":13875,"d":46,"f":13876,"v":13877,"n":23,"y":132,"u":13878},"RTK-GNSS (model not named)",[],[],[13879],[790,41,2000,13880,793],"incorporated into offline LiDAR bundle adjustment for reference poses",{"c":651,"m":13882,"d":46,"f":13883,"v":13884,"n":23,"y":318,"u":13885},"RTK-GNSS system (model not reported)",[],[],[13886],[3237,41,29,13887,13888],"attached to the MAV; used for trajectory evaluation only","Sec. VIII-B1; Fig. 5",{"c":651,"m":13890,"d":46,"f":13891,"v":13892,"n":23,"y":1008,"u":13893},"RTK-GPS INS based system",[],[],[13894],[1155,41,1146,13895,332],"ground truth trajectories for each sensor",{"c":651,"m":13897,"d":46,"f":13898,"v":13899,"n":23,"y":233,"u":13900},"RTK-GPS system (model not reported)",[],[],[13901],[3947,41,4940,13902,745],"3D position ground truth",{"c":651,"m":13904,"d":46,"f":13905,"v":13906,"n":23,"y":100,"u":13907},"RTK-GPS\u002FINS",[],[],[13908],[192,41,1146,13909,1659],"ground-truth source listed for HeLiPR",{"c":651,"m":13911,"d":20,"f":13912,"v":13913,"n":23,"y":132,"u":13914},"RTK-SLAM positioning module of OmniSLAM R6",[],[],[13915],[528,53,29,13916,530],"not_reported (use inside the tunnel not described)",{"c":18,"m":13918,"d":20,"f":13919,"v":13920,"n":23,"y":122,"u":13921},"RTX 2080 Ti",[702],[],[13922],[13923,28,29,13924,13925],"nerfslam2023","GPU with 11 Gb memory, used for tracking and mapping","Sec. III-D",{"c":18,"m":13927,"d":20,"f":13928,"v":13929,"n":23,"y":122,"u":13930},"RTX 3090 GPU",[],[],[13931],[13932,28,29,13933,917],"qin2023geotransformer","used for training and testing in PyTorch",{"c":18,"m":13935,"d":20,"f":13936,"v":13937,"n":23,"y":132,"u":13938},"RTX-3080 10 GB",[702],[],[13939],[3944,28,29,13940,13941],"desktop GPU running the stereo and MVS networks and depth fusion; Ours-vid uses 3.51 GB GPU memory for the networks","Sec. VI-H; Fig. 12 note",{"c":18,"m":13943,"d":20,"f":13944,"v":13945,"n":41,"y":122,"u":13946},"RTX-3090",[702],[],[13947,13950],[13948,28,29,13949,313],"dpvslam2024","GPU used for all timing experiments",[13951,28,29,13952,13953],"dpvo2023","GPU for runtime measurements and for training (single GPU, 3.5 days)","Sec. 1, Sec. 3.2",{"c":18,"m":13955,"d":20,"f":13956,"v":13957,"n":23,"y":49,"u":13958},"RTX-3090 GPU (x4)",[],[],[13959],[92,28,29,13960,313],"training compute, not runtime: 250k steps, batch 4, 1 week",{"c":33,"m":13962,"d":46,"f":13963,"v":13964,"n":23,"y":1008,"u":13965},"ruler",[],[],[13966],[6226,41,29,13967,3230],"measured box height 9.5 cm as planar ground truth",{"c":372,"m":13969,"d":20,"f":13970,"v":13972,"n":23,"y":385,"u":13973},"RWI B21",[13971],"RWI",[],[13974],[388,53,29,569,31],{"c":372,"m":13976,"d":46,"f":13977,"v":13978,"n":23,"y":1411,"u":13979},"RWI Pioneer",[13971],[],[13980],[1414,23,13981,13982,13983],"Hallway run provided by Steffen Gutmann (Fig. 9)","low-cost platform with odometry error significantly higher than the more expensive platforms used in the other experiments","Sec. 5.2, Fig. 9",{"c":18,"m":13985,"d":20,"f":13986,"v":13987,"n":23,"y":100,"u":13988},"Ryzen 7 7800x3d",[1302],[],[13989],[11792,28,29,13990,917],"CPU, desktop with 32GB RAM",{"c":372,"m":13992,"d":46,"f":13993,"v":13994,"n":23,"y":132,"u":13995},"sailboat",[],[],[13996],[2135,23,2136,13997,13998],"inland-waterway sequences with all three devices","Table 3, Fig. 2c",{"c":33,"m":14000,"d":46,"f":14001,"v":14002,"n":23,"y":1819,"u":14003},"Satellite image",[],[],[14004],[5093,41,29,14005,14006],"trajectory and building walls matched to it to judge horizontal drift","Sec. 7.4, Figs. 18-19",{"c":651,"m":14008,"d":46,"f":14009,"v":14011,"n":23,"y":1008,"u":14012},"SBG Ellipse-D GNSS-INS",[14010],"SBG",[],[14013],[1155,41,2376,14014,332],"reference poses from offline LiDAR bundle adjustment incorporating RTK-GPS",{"c":662,"m":14016,"d":46,"f":14017,"v":14019,"n":23,"y":132,"u":14020},"SBG INS",[14018],"SBG (brand as labelled in Fig. 3(a); full company name not given)",[],[14021],[657,23,658,14022,660],"inertial navigation system on the multi-sensor platform; model not reported",{"c":522,"m":14024,"d":46,"f":14025,"v":14026,"n":23,"y":289,"u":14027},"second camera mounted underneath the original uEye camera, with fisheye lens",[],[],[14028],[5332,53,29,14029,2153],"same configuration as the original camera except resolution 640 x 480 px; fisheye lens with 185 deg horizontal FoV; model not named separately",{"c":662,"m":14031,"d":46,"f":14032,"v":14033,"n":23,"y":142,"u":14034},"second IMU on the sensor base (model not stated)",[],[],[14035],[467,41,29,14036,324],"measures base motion as input for spring system identification",{"c":372,"m":14038,"d":46,"f":14039,"v":14040,"n":23,"y":346,"u":14041},"Segway mobile platform",[],[],[14042],[7586,23,600,569,10205],{"c":372,"m":14044,"d":46,"f":14045,"v":14046,"n":23,"y":100,"u":14047},"Segway robot",[],[],[14048],[599,23,600,14049,4427],"NCLT platform",{"c":372,"m":14051,"d":46,"f":14052,"v":14053,"n":23,"y":318,"u":14054},"self-assembled LiDAR-IMU sensor rig",[],[],[14055],[14056,53,29,14057,14058],"lv2020licalib","one VLP-16 and three Xsens IMUs; an attached camera was not used","Fig. 1, Sec. V",{"c":372,"m":14060,"d":46,"f":14061,"v":14062,"n":23,"y":49,"u":14063},"self-assembled small unmanned ground vehicle",[],[],[14064],[8457,53,8458,14065,14066],"sensors rigidly mounted; red-boxed sensors used for YQ","Fig. 5",{"c":944,"m":14068,"d":46,"f":14069,"v":14070,"n":23,"y":37,"u":14071},"self-contained embedded mapping system for handheld mapping",[],[],[14072],[5601,53,29,14073,14074],"Hokuyo UTM-30LX, Intel Atom Z530 board and MEMS IMU; can be carried by hand or mounted on vehicles","Sec. VI-C, Fig. 4b",{"c":372,"m":14076,"d":46,"f":14077,"v":14078,"n":23,"y":346,"u":14079},"self-developed aerial robot (quadrotor)",[],[],[14080],[1078,53,29,14081,3404],"tracks a figure-eight pattern of 1.0 m radius circles",{"c":372,"m":14083,"d":46,"f":14084,"v":14085,"n":23,"y":122,"u":14086},"self-developed handheld device",[],[],[14087],[1191,53,2307,14088,14089],"carried while cycling (TJ-1 to TJ-4) and walking (TJ-5); aggressive rotations up to 232 deg\u002Fs (TJ-6, TJ-7)","Sec. IV-A-2; Table I",{"c":372,"m":14091,"d":46,"f":14092,"v":14093,"n":23,"y":122,"u":14094},"self-rotating single-actuated UAV",[],[],[14095],[2169,53,29,14096,14097],"average yaw rate about 25 rad\u002Fs; Livox Avia facing front","Sec. 7.2; Fig. 18b",{"c":18,"m":14099,"d":20,"f":14100,"v":14101,"n":23,"y":233,"u":14102},"Server with 32 Intel Xeon E5-2690 CPUs",[98],[],[14103],[3742,28,29,14104,469],"2.90 GHz, 132 GB memory; all experiments run online with 10 Hz scans",{"c":1689,"m":14106,"d":46,"f":14107,"v":14108,"n":23,"y":122,"u":14109},"Sevensense Alphasense",[1234],[],[14110],[1462,53,29,14111,1104],"gray stereo, 30 Hz, 720 px x 540 px, diagonal FoV 165.4 deg (LSM, SMM)",{"c":522,"m":14113,"d":46,"f":14114,"v":14115,"n":23,"y":100,"u":14116},"Sevensense Alphasense Core",[1234],[],[14117],[4121,23,12282,14118,3256],"4 hardware-synchronized cameras at 30 Hz",{"c":522,"m":14120,"d":46,"f":14121,"v":14122,"n":23,"y":132,"u":14123},"Sevensense Alphasense Core Development Kit (customised), three colour fisheye cameras",[1243],[],[14124],[2662,23,791,14125,9547],"global shutter, 1440 x 1080 (1.6 MP), FoV 126 x 92.4 deg, 20 Hz, auto-exposure, about 36 deg overlap between adjacent cameras; hardware-synchronised with the IMU via FPGA",{"c":18,"m":14127,"d":20,"f":14128,"v":14130,"n":23,"y":14131,"u":14132},"SGI O2 workstation (R5000 180 MHz, 96 MB RAM)",[14129],"SGI",[],1998,[14133],[14134,28,29,14135,14136],"cignoni1998metro","R5000 180 MHz, 96 MB RAM","Sec. 3, Table 1",{"c":1776,"m":14138,"d":20,"f":14139,"v":14140,"n":23,"y":100,"u":14141},"short-range depth front-facing camera (RA1)",[],[],[14142],[2281,53,29,4332,2850],{"c":108,"m":14144,"d":46,"f":14145,"v":14146,"n":23,"y":346,"u":14147},"SICK 2D laser scanner, horizontal (model not reported)",[13794],[],[14148],[2316,53,29,14149,6541],"single planar sweep used for Hector SLAM scan matching",{"c":108,"m":14151,"d":46,"f":14152,"v":14153,"n":23,"y":346,"u":14154},"SICK 2D laser scanners, 4 vertical (model not reported)",[13794],[],[14155],[2316,53,29,14156,14157],"mounted on a frame rotating about z1; each measures -95 to 95 deg at 0.3333 deg increments; D-H kinematics in Table 2","Sec. 4.1; Sec. 4.2; Table 2",{"c":108,"m":14159,"d":46,"f":14160,"v":14161,"n":23,"y":346,"u":14162},"SICK 2D line laser scanner (four units; model not stated)",[13794],[],[14163],[13340,53,29,14164,14165],"80 m working range at 25 Hz scan speed; 200 s per 360 deg scan; 190 deg vertical line; resolution 0.1667 deg vertical and 0.072 deg horizontal","Data Acquisition and Fusion; Fig. 2",{"c":108,"m":14167,"d":20,"f":14168,"v":14169,"n":28,"y":1920,"u":14170},"SICK laser range finder",[13794],[],[14171,14173,14175],[311,53,29,14172,917],"front-mounted 7 cm above the floor; 180 deg at 1 deg spacing; effective range up to 8 m; distance error typically below 5 mm",[1923,53,29,14174,1924],"180 deg field of view",[12831,53,29,569,14176],"Experimental Results",{"c":108,"m":14178,"d":20,"f":14179,"v":14180,"n":23,"y":1411,"u":14181},"SICK laser range scanner",[13794],[],[14182],[1414,23,13981,569,14183],"Sec. 5.2, Fig. 9, note 2",{"c":108,"m":14185,"d":20,"f":14186,"v":14187,"n":23,"y":61,"u":14188},"SICK laser scanner",[13794],[],[14189],[64,53,29,14190,7034],"covering 180 degree with an angular resolution of one degree (model number not stated)",{"c":108,"m":14192,"d":46,"f":14193,"v":14194,"n":23,"y":142,"u":14195},"SICK laser scanner (model not reported)",[13794],[],[14196],[145,41,29,14197,10709],"used with a standard 2D SLAM approach to build the 2D reference map of the Intel Labs loop",{"c":108,"m":14199,"d":46,"f":14200,"v":14201,"n":23,"y":357,"u":14202},"SICK LIDAR (12 units; model not stated)",[13794],[],[14203],[1615,53,29,1616,1617],{"c":108,"m":14205,"d":20,"f":14206,"v":14207,"n":23,"y":455,"u":14208},"SICK lidar on a Schunk PowerCube via slip-ring contacts (with a digital camera)",[13794],[],[14209],[1000,53,14210,14211,14212],"Kemi mine muck piles (locations A to D)","72 000 to 418 000 points per scan, subsampled to one point per dm3 (10 000 to 22 000 points)","Sec. 9.3.2",{"c":108,"m":14214,"d":20,"f":14215,"v":14216,"n":23,"y":455,"u":14217},"SICK lidar on continuously rotating motor with slip-ring contacts (Alfred)",[13794],[],[14218],[1000,23,12723,14219,147],"omnidirectional yawing 3D scans",{"c":108,"m":14221,"d":46,"f":14222,"v":14223,"n":23,"y":455,"u":14224},"SICK lidar on pan\u002Ftilt unit (model not named for Tjorven)",[13794],[],[14225],[1000,53,14226,14227,14228],"Straight, Crossing, Kvarntorp-Loop","pitching 3D scans, 180 deg horizontal and about 100 deg vertical field of view; about 90 000 to 95 000 points per scan in the mine data","Sec. 4.1, 6.4.1, 6.4.3",{"c":108,"m":14230,"d":20,"f":14231,"v":14232,"n":952,"y":61,"u":14233},"SICK LMS",[13794],[],[14234,14235,14237,14238],[981,53,29,569,214],[7000,23,12848,14236,1416],"2D laser range finder on a pan-tilt unit; beam range limited to 10 m for this dataset; 66 3D scans, 6 million end points",[83,53,29,569,12790],[5658,53,29,14239,313],"measurement error below 5 cm; range resolution 1 cm",{"c":108,"m":14241,"d":20,"f":14242,"v":14243,"n":41,"y":71,"u":14244},"SICK LMS 200",[13794],[],[14245,14249],[1306,53,14246,14247,14248],"KVARNTORP-LOOP","2D scanner on a pan-tilt unit for pitching 3D scans; about 95,000 points per scan; scans 4 to 5 m apart","Sec. 5.1; Fig. 10",[13798,53,14250,14251,14252],"simulated Willow garage office world and asphalt-mill environment (ROS\u002FGazebo)","simulated only: the noise and mixed-measurement error models reported for this scanner (Ye and Borenstein 2002; Tuley et al. 2005) were implemented in ROS\u002FGazebo for a simulated 3D sensor with a 180 x 120 deg field of view","Sec. 6.1, 6.3",{"c":108,"m":14254,"d":20,"f":14255,"v":14256,"n":23,"y":455,"u":14257},"SICK LMS 200 (tiltable, servo-pitched)",[13794],[],[14258],[8715,53,14259,14260,332],"Kvarntorp data sets A and B (Osnabrueck Robotic 3D Scan Repository)","361 x 226 points per 3D scan covering about 180 x 116.3 deg; drive-scan-and-go; scans subsampled to 8000 points",{"c":108,"m":14262,"d":20,"f":14263,"v":14264,"n":23,"y":270,"u":14265},"SICK LMS 291 (rotating)",[13794],[],[14266],[10078,53,29,14267,14268],"2D laser on a spinning mount, one revolution every 2 s giving a hemispherical 3D view each second; spin axis pitched 65 deg from horizontal; custom driver; internal mirror and encoder wobble calibrated","Sec. 3.1, 3.2, Fig. 1",{"c":108,"m":14270,"d":20,"f":14271,"v":14272,"n":23,"y":270,"u":14273},"SICK LMS 291 (two fixed, vertical)",[13794],[],[14274],[10078,53,29,14275,14276],"mounted back to back with vertical scan planes covering 360 deg (pushbroom); 160 mm blind spot; used only for surface reconstruction, not for trajectory estimation","Sec. 3.1, 4.5, Fig. 2",{"c":108,"m":14278,"d":20,"f":14279,"v":14280,"n":23,"y":132,"u":14281},"SICK LMS 2D range finder",[13794],[],[14282,14286],[282,23,14283,14284,14285],"Freiburg building 079 (fr079)","2D LiDAR; Pioneer2 robot in text, PowerBot in Table 1","Table 1; Sec. 5.1",[282,23,14287,14288,14285],"Malaga CS faculty","2D LiDAR on a robotic wheelchair with encoders, 1.9 km",{"c":108,"m":14290,"d":20,"f":14291,"v":14292,"n":28,"y":289,"u":14294},"SICK LMS-151",[13794],[14293],"Sick LMS151",[14295,14298,14301],[9620,53,29,14296,14297],"2D LiDAR; fitted aperture half-angle 0.43 deg (Table I)","Sec. IV, V, Table I, Fig. 1",[861,53,29,14299,14300],"2D laser rotated about an axis pointing to the front of NiftiBot; 3D scans at 0.35 Hz with about 55,000 points","Sec. 3.1; Fig. 3.1; Table 3.7",[2573,23,2671,14302,14303],"2D LiDAR scanner; 44 full and partial runs","Sec. 5.1 Oxford Dataset",{"c":108,"m":14305,"d":20,"f":14306,"v":14307,"n":23,"y":318,"u":14308},"SICK LMS-151 (rotating 2D)",[13794],[],[14309],[4865,53,14310,14311,14312],"search and rescue UGV data (Gustav Knepper powerplant; Phoenix-West foundry)","rotating 2D LiDAR on UGVs; field of view not full 360 degrees","Sec. 5.9.2",{"c":108,"m":14314,"d":20,"f":14315,"v":14316,"n":23,"y":49,"u":14317},"SICK LMS-511",[13794],[],[14318],[8457,23,14319,14320,14321],"KAIST Urban (Complex Urban dataset)","2D LiDAR; not used for estimation, only assembled with the CLINS trajectory for dense reconstruction","Fig. 1; Sec. V-C",{"c":108,"m":14323,"d":20,"f":14324,"v":14325,"n":23,"y":270,"u":14326},"SICK LMS100",[13794],[],[14327],[2407,53,29,14328,530],"2D laser scanner at the front for obstacle avoidance",{"c":108,"m":14330,"d":20,"f":14331,"v":14332,"n":28,"y":455,"u":14335},"SICK LMS291",[13794],[14333,14334],"SICK LMS 291","SICK LMS-291",[14336,14338,14341],[476,53,29,14337,2191],"commercial 2D laser range finder spun about its centre scan line at 0.5 Hz; 75 Hz scan rate, 1 deg angular resolution; 150 scans per revolution; a half-revolution 'sweep' has 13,500 points; hemispherical field of view",[861,53,29,14339,14340],"two units on a custom turntable with a vertical rotating axis, 14,000 points per second each; usable range under 20 m on concrete roads","Sec. 3.4, 3.5; Fig. 3.15",[1606,952,29,14342,14343],"three units on IP-S2; TOF; outdoor range up to 80 m, typical 30 m at 10% reflectivity; 75 Hz; scan angle 90 to 180 deg; 40 kHz; range accuracy +\u002F-35 mm","Sec. 3.2, Table 3",{"c":108,"m":14345,"d":20,"f":14346,"v":14347,"n":23,"y":142,"u":14348},"SICK LMS291 (spinning)",[13794],[],[14349],[467,41,29,14350,14351],"rotated at 1 Hz about its middle scan ray, mounted at 750 mm on a pushcart, 13 500 points per second, hemispherical FoV facing behind the cart","Sec. IV; Fig. 2(b)",{"c":108,"m":14353,"d":20,"f":14354,"v":14355,"n":41,"y":61,"u":14356},"SICK PLS",[13794],[],[14357,14358],[981,53,29,569,214],[5658,53,29,14359,313],"measurement error below 20 cm; range resolution 1 cm",{"c":108,"m":14361,"d":20,"f":14362,"v":14363,"n":23,"y":554,"u":14364},"Sick range-sensor",[13794],[],[14365],[12677,23,13738,14366,127],"360 rays over a 180 deg field of view",{"c":108,"m":14368,"d":20,"f":14369,"v":14370,"n":41,"y":252,"u":14371},"SICK scanner",[13794],[],[14372,14375],[14373,53,29,14374,2191],"cole_newman2006_3dslam","standard 2D scanner oscillating at 0.6 Hz about a horizontal axis to sweep a series of elevations (custom 3D laser range finder)",[4849,53,14376,14377,14378],"simulated indoor hallway and outdoor building scenes","simulated by ray tracing, mounted on a rotating joint; Gaussian noise added (plots titled 15,000 points, 1 cm noise)","Sec. IV (p. 4), Figs. 2, 3, 5",{"c":108,"m":14380,"d":20,"f":14381,"v":14382,"n":23,"y":455,"u":14383},"SICK scanner (statically mounted, 2D)",[13794],[],[14384],[4806,23,4807,569,14385],"Sec. 8.4",{"c":108,"m":14387,"d":20,"f":14388,"v":14389,"n":23,"y":455,"u":14390},"SICK scanner mounted on a pan-tilt unit (3D)",[13794],[],[14391],[4806,23,4807,14392,14393],"3D scans used for Monte Carlo localization on aerial images; height variations of 0.5 m and above extracted","Sec. 5.2; Sec. 8.4",{"c":108,"m":14395,"d":20,"f":14396,"v":14397,"n":23,"y":132,"u":14398},"SICK TiM781S",[13794],[],[14399],[1145,952,29,14400,2717],"2D LiDAR mounted 0.16 m above ground on the Dingo",{"c":108,"m":14402,"d":20,"f":14403,"v":14404,"n":23,"y":357,"u":14405},"SICK-LMS range finder",[13794],[],[14406],[971,23,972,569,974],{"c":33,"m":14408,"d":46,"f":14409,"v":14410,"n":23,"y":71,"u":14411},"simulated 2D range finder (52 rays distributed on 360 deg)",[],[],[14412],[14413,53,29,14414,127],"censi2007covariance","52 rays distributed on 360 deg; zero-mean Gaussian range noise with 0.03 m standard deviation",{"c":108,"m":14416,"d":46,"f":14417,"v":14418,"n":23,"y":233,"u":14419},"simulated 64-channel LiDAR",[],[],[14420],[2979,23,14421,14422,2300],"KITTI-CARLA","CARLA simulator, precise ground truth and timestamps",{"c":662,"m":14424,"d":46,"f":14425,"v":14426,"n":41,"y":318,"u":14427},"simulated IMU",[],[],[14428,14432],[5254,23,14429,14430,14431],"simulated sinusoidal trajectories","100 Hz; accelerometer noise sd 0.02 m\u002Fs2; gyroscope noise sd 0.002 rad\u002Fs","Table I caption; Table III caption",[6102,53,6103,14433,3677],"simulated together with wheel odometry and ground truth odometry",{"c":522,"m":14435,"d":46,"f":14436,"v":14437,"n":23,"y":49,"u":14438},"simulated monocular camera (CARLA simulator)",[],[],[14439],[577,23,14440,14441,14442],"authors' CARLA-simulated BA datasets","four authors' datasets: car front-looking, UAV nadir, strong shadows, side-looking with motion blur","Sec. IV-B, Fig. 6",{"c":33,"m":14444,"d":46,"f":14445,"v":14446,"n":23,"y":1819,"u":14447},"simulated noiseless RGB-D sensor",[],[],[14448],[1764,23,14449,14450,3511],"synthetic ESDF benchmark","320 x 240, maximum range 5 m, 50 random poses",{"c":1776,"m":14452,"d":46,"f":14453,"v":14454,"n":23,"y":1008,"u":14455},"simulated RGB-D camera (human roaming trajectories; sensor not named)",[],[],[14456],[1323,23,14457,14458,917],"ReplicaCAD (FRL apartment, Baked_sc0_staging_01 and Baked_sc0_staging_05)","RGB-D sequences organised in the Replica dataset format; different paths for the baseline and update configurations; intrinsics not reported",{"c":6267,"m":14460,"d":46,"f":14461,"v":14462,"n":23,"y":233,"u":14463},"simulated UWB network (ranges generated from Vicon data)",[],[],[14464],[1201,53,14465,14466,671],"EuRoC MAV (simulated UWB)","4 anchors at (3,3,3), (3,-3,0.5), (-3,-3,3), (-3,3,0.5) m; 2 UAV nodes with 2 antennae; 80 Hz; 0.05 m noise",{"c":353,"m":14468,"d":46,"f":14469,"v":14470,"n":23,"y":122,"u":14471},"simulated wheel odometry",[],[],[14472],[6102,53,6103,14473,3677],"simulated in the six sequences",{"c":108,"m":14475,"d":46,"f":14476,"v":14477,"n":23,"y":233,"u":14478},"single 3D LiDAR (model not named)",[],[],[14479],[6593,53,14480,569,14481],"MulRan KAIST and DCC; LT-ParkingLot (six sessions over three days)","Sec. III; Sec. V-A2",{"c":522,"m":14483,"d":46,"f":14484,"v":14485,"n":23,"y":289,"u":14486},"single camera of the KITTI setup",[],[],[14487],[5332,23,5847,569,4496],{"c":18,"m":14489,"d":46,"f":14490,"v":14491,"n":23,"y":100,"u":14492},"single CPU core (model not reported)",[],[],[14493],[5177,28,14494,14495,14496],"Map-free relocalization","platform for the matching-time versus accuracy plot","Fig. 2 right (VoR); Fig. 3 right (arXiv v1)",{"c":18,"m":14498,"d":46,"f":14499,"v":14500,"n":23,"y":346,"u":14501},"single CPU core at 4 GHz (processor model not reported)",[],[],[14502],[2612,28,29,14503,214],"one core, less than 1 GB RAM",{"c":18,"m":14505,"d":46,"f":14506,"v":14507,"n":23,"y":49,"u":14508},"single desktop CPU and GPU (models not reported)",[],[],[14509],[10282,28,29,14510,14511],"PyTorch; tracking and mapping run concurrently on the same GPU","Sec. 1; Table 4",{"c":108,"m":14513,"d":46,"f":14514,"v":14515,"n":23,"y":233,"u":14516},"single lidar on Spot (model and manufacturer not reported)",[],[],[14517],[1405,53,6209,14518,717],"one of the two Spot sensor configurations (the other is a Hovermap); point clouds differ in size and density from the Husky three-lidar setup",{"c":18,"m":14520,"d":20,"f":14521,"v":14522,"n":23,"y":6352,"u":14523},"single-processor computer rated at 1.6 Mflops on the 100 x 100 double-precision Linpack benchmark",[],[],[14524],[6355,28,29,14525,214],"all programs written in C",{"c":372,"m":14527,"d":46,"f":14528,"v":14529,"n":23,"y":270,"u":14530},"site utility vehicle (pickup truck) carrying a steel-frame sensor cart",[],[],[14531],[10078,53,29,14532,14533],"cart strapped to the vehicle bed with batteries, electronics and a ROS logging laptop; driven by a mine employee at 20-30 km\u002Fh (limit 30 km\u002Fh)","Sec. 3.1, Fig. 1, Fig. 3",{"c":944,"m":14535,"d":46,"f":14536,"v":14538,"n":23,"y":299,"u":14539},"SITECO ROAD SCANNER",[14537],"SITECO",[],[14540],[1606,952,29,14541,9547],"post-processed X-Y 0.020 m, Z 0.050 m, roll and pitch 0.005 deg, true heading 0.010 deg (manufacturer information); one FARO PHOTON 120, IXSEA LANDINS, 8 BASLER SCOUT cameras",{"c":662,"m":14543,"d":46,"f":14544,"v":14545,"n":23,"y":49,"u":14546},"six-axis IMU (model not stated)",[],[],[14547],[1938,23,14548,2377,1416],"UTBM (EU long-term)",{"c":18,"m":14550,"d":20,"f":14551,"v":14552,"n":23,"y":346,"u":14553},"Skylake i7-6700HQ CPU with 16GB of memory",[98],[],[14554],[5863,28,29,14555,127],"Ubuntu 16.10, frequency scaling disabled, GCC 5.4.1; SLAMBench framework",{"c":18,"m":14557,"d":46,"f":14558,"v":14559,"n":23,"y":1008,"u":14560},"SLAM computer (model not stated)",[],[],[14561],[2018,28,29,14562,14563],"Ubuntu 20.04, ROS 1 Noetic, PCL 1.10.0","Table A1",{"c":944,"m":14565,"d":20,"f":14566,"v":14568,"n":23,"y":100,"u":14569},"SLAM-K120",[14567],"Kolida",[],[14570],[9955,952,29,14571,14572],"Listed in the survey's commercial catalogue only, not tested: handheld 3D laser scanner integrating LiDAR and IMU, operates without GNSS, 360 deg rotation, 285 deg FOV; listed scenarios include pipeline survey, underground garage, cultural relics and ancient buildings, mine survey","Table 10; Sec. 4.4 (LI solutions)",{"c":944,"m":14574,"d":20,"f":14575,"v":14576,"n":23,"y":100,"u":14577},"SLAM-K120 Plus",[14567],[],[14578],[9955,952,29,14579,14580],"Listed in the survey's commercial catalogue only (sensor class L, I), not tested; no further specs","Table 10",{"c":944,"m":14582,"d":20,"f":14583,"v":14585,"n":23,"y":100,"u":14586},"SLAM100",[14584],"FEIMA ROBOTICS",[],[14587],[9955,952,29,14588,14589],"Listed in the survey's commercial catalogue only (sensor class L, V), not tested; Table 10 lists the solution as 'SLAM 100-SLAM GO'; Sec. 4.4 describes external interfaces for panoramic cameras, GPS modules, cars and UAVs","Sec. 4.4 (LV solutions); Table 10",{"c":108,"m":14591,"d":46,"f":14592,"v":14594,"n":23,"y":122,"u":14595},"Slamtec Mapper",[14593],"Slamtec",[],[14596],[8013,53,29,14597,917],"2D LiDAR for positioning and navigation (Hector map)",{"c":108,"m":14599,"d":20,"f":14600,"v":14602,"n":23,"y":132,"u":14603},"SLAMTEC RPLIDAR A2",[14601],"SLAMTEC",[],[14604],[2352,53,29,14605,14606],"2D; 360 deg angular range; angular resolution \u003C= 0.12 deg; range 30 m; accuracy +-30 mm; 10 Hz","Sec. 4.1, Table 3, Fig. 11",{"c":662,"m":14608,"d":46,"f":14609,"v":14610,"n":23,"y":299,"u":14611},"small IMU under the LiDAR (model not_reported)",[],[],[14612],[4141,952,29,14613,1212],"part of the ZEB1",{"c":662,"m":14615,"d":46,"f":14616,"v":14617,"n":23,"y":37,"u":14618},"small low-cost MEMS IMU (model not reported)",[],[],[14619],[5601,53,29,569,3256],{"c":372,"m":14621,"d":46,"f":14622,"v":14623,"n":23,"y":132,"u":14624},"Smart Transport Robot (STR)",[],[],[14625],[3836,53,29,569,530],{"c":372,"m":14627,"d":46,"f":14628,"v":14629,"n":23,"y":289,"u":14630},"SmartTer (modified Smart Fortwo)",[],[],[14631],[861,53,29,14632,14633],"6.38 m3, 730 kg, about 15 km\u002Fh","Sec. 3.4; Table 3.7",{"c":108,"m":14635,"d":46,"f":14636,"v":14637,"n":41,"y":233,"u":14638},"solid-state LiDAR (model not named)",[],[],[14639,14641,14643],[1340,23,9643,14640,127],"AVIA dataset from FastLIO2",[6518,23,2121,14642,10205],"used for Gaussian initialization and LiDAR factors",[6518,23,2145,14642,10205],{"c":108,"m":14645,"d":46,"f":14646,"v":14647,"n":23,"y":122,"u":14648},"solid-state LiDAR of ref. [26] (retina-like, incommensurable scanning; model not named)",[],[],[14649],[2962,53,14650,569,10205],"self-collected scene-1 (indoor factory) and scene-2 (outdoor park)",{"c":522,"m":14652,"d":20,"f":14653,"v":14655,"n":23,"y":1819,"u":14656},"Sony a7S II with Zeiss Loxia 21mm f\u002F2.8 lens",[14654],"Sony",[],[14657],[2091,23,2092,14658,3611],"rolling shutter; 4K (> 8 MP) video; 90 deg diagonal FOV; stabilized by a Pilotfly H2 gimbal; used for scenes marked S in Table 1",{"c":651,"m":14660,"d":46,"f":14661,"v":14662,"n":23,"y":100,"u":14663},"SPAN-CPT",[11291],[],[14664],[4092,41,4093,14665,478],"multi-frequency multi-constellation GNSS RTK with a tactical-grade IMU; post-processed ground truth",{"c":33,"m":14667,"d":46,"f":14668,"v":14669,"n":23,"y":233,"u":14670},"spherical targets",[],[],[14671],[3818,41,29,14672,863],"used to align static scans to existing mine survey data at Edgar",{"c":33,"m":14674,"d":20,"f":14675,"v":14676,"n":23,"y":100,"u":14677},"spherical targets (diameter 0.145 m) and black-and-white targets",[],[],[14678],[1986,41,29,14679,14680],"six spheres (three at each end) as GCPs for transforming Hovermap clouds; 28 black-and-white targets, four around each reference profile, for P40 georeferencing","Materials and methods; Measurement and processing of the reference dataset",{"c":33,"m":14682,"d":20,"f":14683,"v":14684,"n":23,"y":132,"u":14685},"spherical targets, diameter 0.14 m (4 GCPs)",[],[],[14686],[3843,41,29,14687,14688],"two at each tunnel end; used only for rigid transformation into the reference frame","Sec. 2; Sec. 2.5",{"c":108,"m":14690,"d":46,"f":14691,"v":14692,"n":23,"y":100,"u":14693},"spinning and solid-state LiDARs (models not named)",[],[],[14694],[192,23,1146,14695,14696],"2 spinning and 2 solid-state LiDARs; the dataset introduces FMCW LiDAR","Table 3, Sec. 8.1",{"c":108,"m":14698,"d":46,"f":14699,"v":14700,"n":41,"y":233,"u":14701},"spinning LiDAR (model not named)",[],[],[14702,14705],[1340,23,13853,14703,14704],"abstract reports over 200 Hz for 32-line spinning lidars; line count and model per dataset not stated","Abstract; Sec. V-B",[192,23,1280,14706,14707],"one spinning LiDAR on a handheld system","Table 3, Sec. 8.3",{"c":944,"m":14709,"d":46,"f":14710,"v":14711,"n":23,"y":233,"u":14712},"SpinningPack (CSIRO perception pack)",[2644],[],[14713],[668,53,669,14714,14715],"spinning VLP-16, 3DM-CV5 IMU, four RGB cameras; robot-mounted at DARPA and hand-held at QCAT","Sec. VI-A1, VI-A3; Fig. 6",{"c":372,"m":14717,"d":46,"f":14718,"v":14719,"n":28,"y":49,"u":14720},"Spot",[2254],[],[14721,14723,14725],[1405,53,6209,14722,717],"quadruped platform equipped with either a single lidar or a Hovermap",[1359,53,29,1554,14724],"Sec. III-C1; Fig. 1",[2510,53,2511,1554,1867],{"c":522,"m":14727,"d":46,"f":14728,"v":14729,"n":23,"y":1008,"u":14730},"Spot built-in cameras (RGB, five monochrome, six depth, one infrared)",[2254],[],[14731],[2272,53,29,14732,9191],"used by Spot's native mobility and obstacle avoidance",{"c":353,"m":14734,"d":46,"f":14735,"v":14736,"n":23,"y":233,"u":14737},"Spot kinematic inertial odometry (KIO) and visual inertial odometry (VIO), out of the box",[],[],[14738],[2510,23,2511,2512,332],{"c":108,"m":14740,"d":46,"f":14741,"v":14742,"n":23,"y":233,"u":14743},"Spot on-board lidar (model not reported)",[],[],[14744],[2510,53,2511,14745,332],"one lidar, extrinsically calibrated; 10 Hz",{"c":33,"m":14747,"d":46,"f":14748,"v":14749,"n":23,"y":49,"u":14750},"Spot VIO and KIO from the Boston Dynamics API",[2254],[],[14751],[1359,53,29,14752,1361],"VIO chosen for Spot because it was more accurate than KIO in the authors' tests",{"c":372,"m":14754,"d":20,"f":14755,"v":14756,"n":23,"y":100,"u":14757},"Spot1 robot",[],[],[14758],[507,23,508,14759,14760],"quadruped robot moving back and forth in a cave tunnel (Valentine Cave)","Fig. 7; Sec. V-B4",{"c":18,"m":14762,"d":20,"f":14763,"v":14764,"n":23,"y":357,"u":14765},"standard laptop (Intel Core2@2.4 GHz)",[98],[],[14766],[971,28,29,14767,1084],"2.4 GHz",{"c":18,"m":14769,"d":20,"f":14770,"v":14771,"n":23,"y":1819,"u":14772},"standard laptop (Intel i7, 2.4 GHz)",[],[],[14773],[1067,28,29,14774,1069],"Intel i7, 2.4 GHz",{"c":18,"m":14776,"d":20,"f":14777,"v":14778,"n":23,"y":71,"u":14779},"standard PC with a 2.8 GHz processor",[],[],[14780],[981,28,29,14781,14782],"2.8 GHz","Sec. VI-F",{"c":1776,"m":14784,"d":20,"f":14785,"v":14786,"n":23,"y":289,"u":14787},"standard RGB-D camera (Microsoft Kinect or ASUS Xtion Pro Live named as examples)",[],[],[14788],[5933,53,14789,14790,14791],"ElasticFusion qualitative datasets (Copy from Zhou and Koltun, Lab, Hotel, Office)","device used for the authors' hand-held qualitative datasets not stated","Sec. II footnote 1; Sec. VII-B",{"c":372,"m":14793,"d":46,"f":14794,"v":14795,"n":23,"y":142,"u":14796},"standard station wagon",[],[],[14797],[12546,23,842,14798,12579],"cameras mounted on a roof rack; computer running a real-time database",{"c":33,"m":14800,"d":46,"f":14801,"v":14802,"n":23,"y":299,"u":14803},"static checkerboard calibration pattern (dimensions not reported)",[],[],[14804],[1433,53,29,14805,14806],"known geometry; defines the world frame","Sec. III-B, Fig. 1, Fig. 6",{"c":5895,"m":14808,"d":46,"f":14809,"v":14811,"n":23,"y":49,"u":14812},"static total stations (PASCO Mobile Measurement System)",[14810],"PASCO Corporation (system developer)",[],[14813],[8484,41,29,14814,12790],"several static total stations placed in the environment; 3D LiDAR position measured to a few millimetres (translation only)",{"c":2807,"m":14816,"d":46,"f":14817,"v":14818,"n":23,"y":289,"u":14819},"statically mounted 3D laser scanner (model not reported)",[],[],[14820],[3751,41,3752,14821,14822],"acquires the ground-truth map used for GT-RMSE after manual alignment of the first point cloud","Sec. II, Sec. V",{"c":108,"m":14824,"d":46,"f":14825,"v":14826,"n":23,"y":455,"u":14827},"stationary 2D laser scanner mounted horizontally on the vehicle's bucket (model not stated)",[],[],[14828],[476,41,29,14829,898],"processed by the authors' 2D SLAM framework [1] assuming zero pitch, roll and vertical translation",{"c":1689,"m":14831,"d":46,"f":14832,"v":14833,"n":41,"y":122,"u":14834},"stereo camera (model not named)",[],[],[14835,14837],[1349,23,5484,14836,745],"used offline with RAFT optical flow to simulate RGB-D input for the NICE-SLAM baseline",[7185,23,1079,14838,14839],"depth from stereo used as input on EuRoC Machine Hall","Supp. 9.5, Table 14",{"c":1689,"m":14841,"d":46,"f":14842,"v":14843,"n":23,"y":122,"u":14844},"stereo camera (model not stated)",[],[],[14845],[2452,23,2577,14846,6265],"15 Hz; only the left camera used",{"c":1689,"m":14848,"d":46,"f":14849,"v":14850,"n":23,"y":233,"u":14851},"stereo camera and IMU of the EuRoC dataset (models not named in the paper)",[],[],[14852],[2775,23,14853,569,5221],"EuRoC (Vicon Room 1, Vicon Room 2, Machine Hall)",{"c":1689,"m":14855,"d":46,"f":14856,"v":14857,"n":23,"y":37,"u":14858},"stereo camera of the Karlsruhe dataset (model not reported)",[],[],[14859],[6365,23,12555,14860,478],"1344 x 391 pixels, 10 fps, calibrated and rectified",{"c":1689,"m":14862,"d":46,"f":14863,"v":14864,"n":23,"y":10603,"u":14865},"stereo rig with a correlation-based stereovision system (image triplets)",[],[],[14866],[10606,53,29,14867,14868],"about 6 m from the rock scene; 71505 and 51503 reconstructed points; positions differ by 30 deg and 3.75 m; data resolution about 5 cm","Sec. 6.1; Figs. 19-20; Sec. 7.2",{"c":1689,"m":14870,"d":20,"f":14871,"v":14873,"n":23,"y":100,"u":14874},"Stereolabs ZED2i",[14872],"Stereolabs",[],[14875],[4121,53,29,14876,7981],"passive stereo camera",{"c":1689,"m":14878,"d":46,"f":14879,"v":14880,"n":23,"y":1008,"u":14881},"stereoscopic-inertial camera",[],[],[14882],[3053,23,815,10835,3056],{"c":662,"m":14884,"d":20,"f":14885,"v":14886,"n":41,"y":233,"u":14887},"STIM300",[],[],[14888,14891],[1562,23,1563,14889,14890],"tactical grade; 200 Hz; bias instability about 0.3 deg\u002Fh; Allan variance at 25 C; mounted below the LiDAR; defines the body frame","Sec. III-A4; Table II",[678,23,679,14892,14893],"tactical grade, 6-axis MEMS, 200 Hz; gyro bias instability 0.3 deg\u002Fh, accelerometer 0.04 mg; defines body frame; mounted beneath LiDAR","Table 2; Sec. 3.1.5; Sec. 4",{"c":33,"m":14895,"d":20,"f":14896,"v":14897,"n":23,"y":1008,"u":14898},"STM32",[],[],[14899],[2018,53,2019,14900,2021],"synchronized timers for LiDAR and camera signals",{"c":33,"m":14902,"d":20,"f":14903,"v":14904,"n":23,"y":132,"u":14905},"STM32 microcontroller",[],[],[14906],[135,23,1913,14907,12011],"hardware synchronization of LiDAR and camera",{"c":33,"m":14909,"d":20,"f":14910,"v":14911,"n":23,"y":1008,"u":14912},"STM32 microcontroller (PPS synchronization board)",[],[],[14913],[2366,53,29,14914,14915],"synchronized timers, GPRMC over USB serial (Fig. 1 a3)","Sec. III; Fig. 1 (a3)",{"c":33,"m":14917,"d":20,"f":14918,"v":14919,"n":41,"y":233,"u":14920},"STM32 synchronized timers",[],[],[14921,14923],[3255,23,3509,14922,9756],"hardware trigger at 10 Hz for all sensors (PWM to LiDAR and camera)",[216,23,3513,14924,9760],"10 Hz hardware trigger for all sensors",{"c":33,"m":14926,"d":20,"f":14927,"v":14928,"n":23,"y":233,"u":14929},"STM32F4DISCOVERY",[],[],[14930],[752,53,29,14931,14932],"MCU platform emulating a GNSS receiver; timer RTC resolution 1\u002F76.8 us (about 13 ns); generates PPS and NMEA GPRMC messages and timestamps IMU samples","Sec. II, Sec. III",{"c":944,"m":14934,"d":46,"f":14935,"v":14937,"n":23,"y":299,"u":14938},"STREETMAPPER PORTABLE",[14936],"3D LASER MAPPING Ltd. and IGI mbH",[],[14939],[1606,952,29,14940,14941],"one RIEGL VQ-250 at 300 kHz; TERRAcontrol GNSS and FOG IMU; one 12 Mpx SLR camera; post-processed position 0.050 m, roll\u002Fpitch 0.004 deg, true heading 0.010 deg; no DMI as standard","Sec. 3.4, Table 1",{"c":1776,"m":14943,"d":46,"f":14944,"v":14945,"n":41,"y":1819,"u":14947},"Structure Sensor",[11876],[14946],"Structure sensor",[14948,14952],[1822,53,14949,14950,14951],"BundleFusion captured sequences (Apt 0 to 2, Copyroom, Office 0 to 3)","RGB-D stream at 30 Hz with 640x480 colour and depth; another colour resolution of 1296x968 mentioned for SIFT timing; footnote links structure.io and the acknowledgements thank Occipital for hardware donations, but the manufacturer is not named","Sec. 6; Sec. 7.3; Acknowledgments",[8232,23,8233,14953,14954],"commodity RGB-D sensor with design similar to Microsoft Kinect v1; depth 640 x 480 at 30 Hz, hardware-synchronized with the iPad colour camera; 16-bit depth, zLib compressed","Sec. 3.1, ref. [63], acknowledgements",{"c":1776,"m":14956,"d":46,"f":14957,"v":14958,"n":23,"y":1008,"u":14959},"structure sensors on handheld devices",[],[],[14960],[3053,23,8233,14961,14962],"2.5 million images from 1513 scans; poses derived from BundleFusion","Sec. II-C; Sec. V-F",{"c":33,"m":14964,"d":20,"f":14965,"v":14966,"n":23,"y":1421,"u":14967},"structured light 3D scanner",[],[],[14968],[7375,23,14969,569,14970],"denture point set","Sec. 6.3, footnote 6",{"c":18,"m":14972,"d":20,"f":14973,"v":14975,"n":23,"y":10603,"u":14976},"SUN 4\u002F60 workstation",[14974],"SUN (brand as written in the model name; company not otherwise stated)",[],[14977],[10606,28,29,14978,14979],"double precision LINPACK rating 1.05 Mflops; C implementation, not optimized","Sec. 5 opening; Note 2",{"c":18,"m":14981,"d":46,"f":14982,"v":14984,"n":23,"y":4627,"u":14985},"Sun Workstations",[14983],"Sun Microsystems",[],[14986],[4630,28,29,14987,3364],"under Unix; no timing reported",{"c":372,"m":14989,"d":46,"f":14990,"v":14991,"n":23,"y":49,"u":14992},"supermegabot",[],[],[14993],[11143,53,29,14994,14995],"mobile robot carrying one LiDAR, three cameras and an IMU; wheeled base visible in the Fig. 4 photo; repository github.com\u002Fethz-asl\u002Feth-supermegabot (footnote 1)","Sec. IV-A, footnote 1, Fig. 4",{"c":372,"m":14997,"d":46,"f":14998,"v":14999,"n":23,"y":299,"u":15000},"survey tripod",[],[],[15001],[9201,53,9202,15002,147],"scanner 'mounted on a survey tripod with dual-axis compensation always activated' (as written; the compensation is not attributed to a named device); 4 to 5 scan positions per survey on elevated spots on the bank or river bed",{"c":33,"m":15004,"d":46,"f":15005,"v":15006,"n":23,"y":233,"u":15007},"survey-grade 3D map (provided by DARPA or produced by the team; instrument not reported)",[],[],[15008],[2510,41,2511,15009,332],"ground-truth trajectory produced by LOCUS 1.0 scan-to-survey-map registration with manual post-processing",{"c":2807,"m":15011,"d":46,"f":15012,"v":15013,"n":23,"y":233,"u":15014},"survey-grade laser scanner (model not stated)",[],[],[15015],[668,41,2063,15016,1204],"DARPA ground-truth point cloud used at 1 cm resolution; about 100 person-hours according to DARPA",{"c":2807,"m":15018,"d":46,"f":15019,"v":15020,"n":23,"y":1008,"u":15021},"survey-grade LiDAR",[],[],[15022],[3053,41,815,15023,3056],"Supports ground truth; six-DOF ground-truth poses accurate to 3 cm",{"c":2807,"m":15025,"d":46,"f":15026,"v":15027,"n":23,"y":122,"u":15028},"survey-grade lidar scanners (models not_reported)",[],[],[15029],[1462,41,29,15030,1204],"accurate prior maps; ground truth by ICP of the robot scans to the prior map (SUB, LSM, SMM)",{"c":2807,"m":15032,"d":46,"f":15033,"v":15034,"n":23,"y":100,"u":15035},"survey-grade TLS point cloud map (scanner model not named)",[],[],[15036],[507,41,815,15037,15038],"mm-level accuracy; reference poses obtained by aligning each scan to it; also the reference model for Table XI","Sec. V-A1; Table XI caption",{"c":2807,"m":15040,"d":46,"f":15041,"v":15042,"n":23,"y":132,"u":15043},"survey-quality scanners (models not reported)",[],[],[15044],[282,41,1494,15045,1496],"ground truth point cloud used to derive ground-truth trajectories by scan matching",{"c":33,"m":15047,"d":46,"f":15048,"v":15049,"n":23,"y":233,"u":15050},"surveyed targets (63; survey instrument not stated)",[],[],[15051],[668,41,15052,15053,15054],"QCAT","scattered over indoor, outdoor, 3-storey office and mock-up tunnel areas","Sec. VI-A3, VI-D; Fig. 12",{"c":33,"m":15056,"d":46,"f":15057,"v":15058,"n":23,"y":455,"u":15059},"SwissRanger time-of-flight camera",[],[],[15060],[1000,23,15061,15062,15063],"3D-Cam (Jacobs University Bremen)","3D-Cam pair: about 65% overlap, about 25 000 points per scan","Sec. 6.4.1",{"c":18,"m":15065,"d":20,"f":15066,"v":15067,"n":23,"y":6352,"u":15068},"Symbolics 3620 Lisp Machine",[],[],[15069],[13224,28,29,569,15070],"p. 152",{"c":1776,"m":15072,"d":46,"f":15073,"v":15074,"n":23,"y":270,"u":15075},"synthetic RGB-D camera (POV-Ray ray-traced)",[],[],[15076],[15077,23,15078,15079,15080],"handa2014iclnuim","ICL-NUIM","640 x 480 images, about 90 deg field of view, K with fx 481.20, fy -480.0, cx 319.50, cy 239.50, no lens distortion or anti-aliasing, 30 Hz; optional Kinect-style depth noise (random pixel shifts sigma_s = 1\u002F2, disparity noise sigma_d = 1\u002F6, constant 35130, quantisation, lateral noise along normals) and camera-response-based RGB noise","Sec. II Eq. (1); Sec. IV; Sec. V; footnotes 3-4",{"c":1776,"m":15082,"d":46,"f":15083,"v":15084,"n":23,"y":233,"u":15085},"synthetic RGB-D depth maps (ICL-NUIM Living room, no simulated noise)",[],[],[15086],[517,23,15078,15087,15088],"depth maps converted to point clouds with ground-truth camera poses; qualitative result","Sec. 5.6.5",{"c":33,"m":15090,"d":46,"f":15091,"v":15092,"n":23,"y":289,"u":15093},"tablet fixed to a swivel chair (tablet model not stated)",[],[],[15094],[1648,53,29,15095,15096],"full rotation of the chair to measure rotation drift of ICP versus IMU tracking","Sec. 7.4; Fig. 13",{"c":651,"m":15098,"d":20,"f":15099,"v":15101,"n":23,"y":233,"u":15102},"Tallysman TW3882 antenna",[15100],"Tallysman",[],[15103],[1058,53,1059,15104,1061],"GNSS antenna of the receiver",{"c":33,"m":15106,"d":46,"f":15107,"v":15108,"n":41,"y":270,"u":15110},"tape ruler",[],[15109],"Tape ruler",[15111,15113],[2630,41,29,15112,5221],"manual ground-truth measurement for the IMU comparison tests",[5093,41,29,15114,15115],"manual ground truth for handheld tests","Sec. 7.2, Table 3",{"c":33,"m":15117,"d":46,"f":15118,"v":15119,"n":23,"y":233,"u":15120},"target board with removable targets and a flat reference surface",[],[],[15121],[3818,41,29,15122,863],"installed at Mine-A for target-level tests and simulated changes",{"c":662,"m":15124,"d":20,"f":15125,"v":15127,"n":23,"y":132,"u":15128},"TDK ICM-20948",[15126],"TDK",[],[15129,15130],[2221,23,12225,2223,1104],[2221,23,12209,2223,1104],{"c":372,"m":15132,"d":46,"f":15133,"v":15134,"n":23,"y":49,"u":15135},"team CoSTAR ground robots (not further specified)",[],[],[15136],[627,53,29,15137,15138],"explored the Arch Coal Mine about 275 m underground","Fig. 1; Sec. 1",{"c":33,"m":15140,"d":20,"f":15141,"v":15142,"n":23,"y":1008,"u":15143},"Teensy 3.6 microcontroller",[],[],[15144],[1011,53,29,15145,4453],"20 g; hardware time synchronization of cameras and IMU, timestamps sent as ROS messages",{"c":108,"m":15147,"d":46,"f":15148,"v":15150,"n":23,"y":71,"u":15151},"Terrapoint ALTMS",[15149],"Terrapoint (proprietary)",[],[15152],[1573,53,29,15153,15154],"510-class IMU, 0.75 mrad beam divergence, 2 cm ranging error, optimization-based boresight; flown at 1000 m AGL","Comparison section: fixed wing system; Table 6",{"c":2807,"m":15156,"d":46,"f":15157,"v":15158,"n":23,"y":1008,"u":15159},"terrestrial laser scanner (model not reported)",[],[],[15160],[610,41,791,15161,1416],"TLS ground truth of the Christ Church College scene",{"c":2807,"m":15163,"d":46,"f":15164,"v":15165,"n":23,"y":132,"u":15166},"terrestrial laser scanner from Zhai et al. 2024 (model not reported here)",[],[],[15167],[405,952,29,15168,15169],"1 mm specification (as stated in Sec. 4.2)","Sec. 4.2, Tables 2 to 4",{"c":2807,"m":15171,"d":46,"f":15172,"v":15173,"n":23,"y":100,"u":15174},"terrestrial laser scanner survey-grade map (model not named)",[],[],[15175],[507,41,508,15176,510],"used only for a qualitative mapping-error visualisation",{"c":2807,"m":15178,"d":46,"f":15179,"v":15180,"n":23,"y":1008,"u":15181},"terrestrial laser-scanning map (scanner model not reported)",[],[],[15182],[1155,41,791,15183,332],"each undistorted scan registered to the TLS map to compute ground truth",{"c":2807,"m":15185,"d":46,"f":15186,"v":15187,"n":23,"y":122,"u":15188},"terrestrial scanner (model not stated in the paper)",[],[],[15189],[807,41,815,15190,332],"Provides the near ground truth mesh",{"c":882,"m":15192,"d":20,"f":15193,"v":15195,"n":23,"y":100,"u":15196},"Texas Instruments IWR6843AOP-EVM",[15194],"Texas Instruments",[],[15197],[1263,53,12278,15198,1351],"10 Hz; radar velocities for a velocity factor",{"c":5895,"m":15200,"d":46,"f":15201,"v":15202,"n":23,"y":299,"u":15203},"theodolite (model not stated)",[],[],[15204],[302,41,6139,15205,15206],"tracked the ground-truth poses of the platform with millimetric precision","Sec. 5.2.1",{"c":5895,"m":15208,"d":46,"f":15209,"v":15210,"n":23,"y":122,"u":15211},"theodolite used to survey control points (model not reported)",[],[],[15212],[1279,41,1280,15213,15214],"many TLS scans georeferenced to control points determined with a very precise theodolite","Ground-truth scans",{"c":4378,"m":15216,"d":20,"f":15217,"v":15218,"n":23,"y":100,"u":15219},"thermal camera on 3-axis gimbal (RA2)",[],[],[15220],[2281,53,29,569,4333],{"c":522,"m":15222,"d":46,"f":15223,"v":15225,"n":23,"y":152,"u":15226},"THETA V",[15224],"RICOH Co., Ltd.",[],[15227],[2936,53,29,15228,1416],"consumer equirectangular (360-degree) camera; 10.0 fps; 15000 frames outdoor, 1430 frames indoor",{"c":18,"m":15230,"d":20,"f":15231,"v":15232,"n":23,"y":1008,"u":15233},"ThinkPad laptop, 12th Gen Intel Core i7-12700Hx20 (as written), 16 GB RAM",[],[],[15234],[1011,28,29,15235,15236],"used for the ray-tracing ablation timings","Sec. 5.5.2",{"c":18,"m":15238,"d":20,"f":15239,"v":15240,"n":23,"y":132,"u":15241},"ThinkPad P17",[],[],[15242],[225,28,226,15243,15244],"Intel i7, 32 GB RAM","Table 8",{"c":522,"m":15246,"d":46,"f":15247,"v":15248,"n":23,"y":132,"u":15249},"three global-shutter cameras (model not named)",[],[],[15250],[790,23,791,15251,2002],"images used at 540 x 720",{"c":33,"m":15253,"d":46,"f":15254,"v":15255,"n":23,"y":152,"u":15256},"three orthogonal laser distance measurement sensors (sensor head at end-effector)",[],[],[15257],[6800,53,29,15258,15259],"used for high-accuracy localization against model planes, six end-effector poses in the experiment","Sec. III-B, Sec. IV-A, Fig. 3",{"c":33,"m":15261,"d":46,"f":15262,"v":15263,"n":23,"y":233,"u":15264},"three reflective plates on an entrance gate",[],[],[15265],[1405,53,29,15266,324],"fiducial markers with known 3D coordinates for initial pose calibration",{"c":662,"m":15268,"d":20,"f":15269,"v":15271,"n":23,"y":100,"u":15272},"Ti-10",[15270],"Xsens",[],[15273],[4092,53,4093,2377,478],{"c":33,"m":15275,"d":46,"f":15276,"v":15277,"n":23,"y":152,"u":15278},"tilt and turn targets",[],[],[15279],[1811,41,1812,15280,15281],"located by both the laser scanner and the motion capture system to align the TLS cloud to the motion-capture frame","Sec. IV-C; Fig. 12c",{"c":108,"m":15283,"d":20,"f":15284,"v":15285,"n":23,"y":455,"u":15286},"tiltable SICK laser scanner (Kurt3D)",[13794],[],[15287],[1000,53,15288,15289,15290],"Mission-4, Mission-4-1","pitching scans with field of view similar to Tjorven; about 70 000 to 75 000 points per scan","Sec. 4.3, 6.4.3, 8.2.1",{"c":108,"m":15292,"d":46,"f":15293,"v":15294,"n":23,"y":37,"u":15295},"tilting laser scanner (model not named)",[],[],[15296],[13039,23,29,15297,15298],"source of the raw point cloud in the statistical outlier removal example (Fig. 7); the paper does not say which robot carried it","Fig. 7 caption",{"c":662,"m":15300,"d":46,"f":15301,"v":15302,"n":23,"y":318,"u":15303},"time-synchronized IMU (model not reported)",[],[],[15304],[3237,53,29,15305,5829],"feeds ROVIO visual-inertial odometry",{"c":944,"m":15307,"d":46,"f":15308,"v":15310,"n":23,"y":318,"u":15311},"TIMMS Indoor Mapping",[15309],"Trimble",[],[15312],[951,952,29,15313,15314],"trolley integrating a TLS (Trimble TX-5 ±2 mm; FARO Focus X-130 and X-330 ±0.3 mm; S-70-A, S-150-A and S-350-A ±1 mm; 97,600 pts\u002Fs) and an IMU; 49.5 kg; 4 h","Sec. 2.3.3; Tables 1-3, 6; Fig. 8b",{"c":18,"m":15316,"d":46,"f":15317,"v":15318,"n":23,"y":318,"u":15319},"Titan X Pascal",[702],[],[15320],[15321,28,29,15322,15323],"d3vo2020","single GPU used to train DepthNet and PoseNet; VO runtime not reported","Supp. A",{"c":372,"m":15325,"d":46,"f":15326,"v":15327,"n":23,"y":71,"u":15328},"Tjorven (authors' mobile robot)",[],[],[15329],[1306,53,14246,15330,15331],"driven manually, stationary during scans; also carries a digital camera, sonar array, omnidirectional camera and differential GPS, not used for registration","Sec. 5.1; Fig. 10 caption",{"c":353,"m":15333,"d":20,"f":15334,"v":15335,"n":23,"y":71,"u":15336},"Tjorven 2D odometry",[],[],[15337],[1306,53,14246,15338,4867],"pose error up to about 1.5 m and 0.2 rad between scans; reset after scans 11, 16 and 66",{"c":2807,"m":15340,"d":46,"f":15341,"v":15342,"n":23,"y":132,"u":15343},"TLS (instrument not named)",[],[],[15344],[369,41,791,15345,15346],"highly accurate TLS point cloud map; every undistorted scan registered to it to obtain ground-truth poses; also the accuracy reference","Sec. IV-A; Fig. 6",{"c":2807,"m":15348,"d":46,"f":15349,"v":15350,"n":23,"y":100,"u":15351},"TLS (model not named)",[],[],[15352],[192,41,1280,15353,15354],"ground-truth source listed for ConSLAM","Table 3, Sec. 8.2.1",{"c":2807,"m":15356,"d":46,"f":15357,"v":15358,"n":23,"y":132,"u":15359},"TLS (model not stated in this paper)",[],[],[15360],[1136,41,15361,15362,15363],"Oxford Spires (Radcliffe01; T-RO only)","LiDAR-TLS map registration giving 1 to 2 cm ground-truth trajectories","T-RO Sec. III-A",{"c":2807,"m":15365,"d":46,"f":15366,"v":15367,"n":23,"y":100,"u":15368},"TLS (Terrestrial Laser Scanner), model not reported",[],[],[15369],[15370,41,15371,15372,15373],"zhao2024deskew","UoM indoor data","described as highly precise; 10 RANSAC planes manually selected from the TLS cloud for plane fitting","Sec. 3 Evaluation metrics, Sec. 4 Data, Fig. 4 to 5",{"c":651,"m":15375,"d":20,"f":15376,"v":15378,"n":23,"y":299,"u":15379},"TOPCON dual-frequency 40-channel GNSS receiver",[15377],"TOPCON",[],[15380],[1606,952,29,15381,1667],"position and clock drift updated at 20 Hz",{"c":944,"m":15383,"d":20,"f":15384,"v":15385,"n":23,"y":299,"u":15386},"TOPCON IP-S2",[15377],[],[15387],[1606,952,29,15388,4258],"post-processed X\u002FY 0.015 m, Z 0.025 m, roll and pitch 0.020 deg, true heading 0.040 deg (Ag58 IMU); three SICK LMS 291, LADYBUG3",{"c":944,"m":15390,"d":20,"f":15391,"v":15393,"n":23,"y":233,"u":15394},"Topcon IP-S3 HD1",[15392],"Topcon",[],[15395],[4224,952,29,15396,5838],"vehicle-mounted, 2015, outdoor; 360° FoV camera; Velodyne HDL-32E LiDAR, 100 m; IMU and GPS; 0.1 cm road surface accuracy (1 sigma, manufacturer)",{"c":5895,"m":15398,"d":46,"f":15399,"v":15400,"n":23,"y":132,"u":15401},"Total Station",[],[],[15402],[216,41,5988,10685,157],{"c":5895,"m":15404,"d":46,"f":15405,"v":15406,"n":41,"y":122,"u":15407},"total station (model not reported)",[],[],[15408,15411],[1396,41,29,15409,15410],"Explorer autocalibration: pose of the first robot relative to DARPA fiducial markers; CTU-CRAS-Norlab UAV reference frames may be given by a total station","Sec. III-E-2, III-F-3",[201,41,5524,15412,1204],"ground-truth trajectory for some Hilti sequences",{"c":5895,"m":15414,"d":46,"f":15415,"v":15416,"n":28,"y":71,"u":15417},"total station (model not stated)",[],[],[15418,15420,15424],[915,41,29,15419,917],"two instrument locations, check points coordinated by intersection; mean point precision +\u002F-0.5 mm horizontal and +\u002F-0.1 mm height",[1306,53,15421,15422,15423],"TUNNEL","tripod-mounted; measured three marked points on the scanner from a fixed position; not accurate enough for ground truth, used as initial estimate","Sec. 5.1; Fig. 6",[292,41,15425,15426,478],"ETH Challenging Laser Registration data sets","ground-truth poses of all point clouds tracked with millimetre precision",{"c":5895,"m":15428,"d":46,"f":15429,"v":15430,"n":23,"y":49,"u":15431},"Total Station (TS) with a tracking prism on top of the sensor suite (model not_reported)",[],[],[15432],[3692,41,29,15433,1084],"trajectory ground truth for Dark-Room, Long-Corridor, White-Wall and Constrained-Environment",{"c":5895,"m":15435,"d":46,"f":15436,"v":15437,"n":23,"y":100,"u":15438},"total station tracking system (model not named)",[],[],[15439],[507,41,10692,15440,4640],"reference trajectories for some sequences (others from a motion capture system)",{"c":372,"m":15442,"d":46,"f":15443,"v":15444,"n":23,"y":299,"u":15445},"tracked search-and-rescue robot with flippers (name not stated)",[],[],[15446],[302,53,29,15447,12587],"tracks and flippers; track-encoder motion highly unreliable",{"c":33,"m":15449,"d":46,"f":15450,"v":15451,"n":23,"y":100,"u":15452},"Tracker",[],[],[15453],[192,41,184,15454,15354],"ground-truth source listed for NTU VIRAL; tracking systems described in Sec. 8.2.1",{"c":372,"m":15456,"d":46,"f":15457,"v":15458,"n":23,"y":100,"u":15459},"Trailblazer",[12728],[],[15460],[9592,23,9593,15461,15462],"700 kg construction robot prototype (Fig. 1a: drilling robot prototype), tracked; strong vibrations when turning, motion mostly constrained to a plane, large extrinsics between sensors; LiDAR, one stereo camera and IMU on a steel plate; moved on a pallet with a hand pallet jack for extrinsic calibration","Sec. III, Sec. III-C, Sec. III-D, Fig. 1a, Fig. 2",{"c":651,"m":15464,"d":20,"f":15465,"v":15467,"n":23,"y":299,"u":15468},"TRIMBLE BD960",[15466],"TRIMBLE",[],[15469],[1606,952,29,15470,530],"L1\u002FL2 GNSS receiver board; GLONASS or RTK; OMNISTAR High Precision corrections",{"c":2807,"m":15472,"d":20,"f":15473,"v":15474,"n":23,"y":11274,"u":15475},"TRIMBLE GS200",[15309],[],[15476],[11277,53,29,15477,764],"ground-based laser scanner; building-site test with 4 daily scans of about 200,000 points from almost the same position and orientation",{"c":2807,"m":15479,"d":20,"f":15480,"v":15481,"n":23,"y":357,"u":15482},"Trimble GX 3D (identified via ref. [44] cited for the scanner used in this research)",[15309],[],[15483],[15484,53,29,15485,15486],"bosche2010asbuiltdims","about 12 mm accuracy at 100 m; maximum horizontal and vertical resolution about 60 microrad (about one point per 3 mm at 50 m); scans 1-4 at 582 microrad and scan 5 at 300 microrad (Table 1)","Sec. 1.1.2, ref. [44], Table 1",{"c":944,"m":15488,"d":20,"f":15489,"v":15490,"n":23,"y":233,"u":15491},"Trimble Indoor",[15309],[],[15492],[4224,952,29,15493,4226],"trolley, 2015, indoor; 360° FoV camera; Trimble TX-5 or FARO Focus X-130, X-330, S-70-A, S-150-A, S-350-A; IMU yes, GPS no; 1 cm relative accuracy with FARO Focus X-130 (manufacturer)",{"c":944,"m":15495,"d":20,"f":15496,"v":15497,"n":23,"y":233,"u":15498},"Trimble MX50",[15309],[],[15499],[4224,952,29,15500,15501],"vehicle-mounted, 2021, outdoor; camera covering 90% of a full sphere; 2 MX50 laser scanners, 80 m; IMU and GPS; 0.2 cm (laser scanner, manufacturer)","Table 4; Table 2; Sec. 4.1",{"c":944,"m":15503,"d":20,"f":15504,"v":15505,"n":23,"y":233,"u":15506},"Trimble MX7",[15309],[],[15507],[4224,952,29,15508,5838],"vehicle-mounted, outdoor; release year not given; 360° FoV camera; no LiDAR; IMU and GPS; accuracy not given",{"c":944,"m":15510,"d":20,"f":15511,"v":15512,"n":23,"y":299,"u":15513},"TRIMBLE MX8",[15466],[],[15514],[1606,952,29,15515,15516],"post-processed X-Y 0.020 m, Z 0.050 m, roll\u002Fpitch 0.015 deg in Sec. 3.3 text (0.020 deg in Table 1), true heading 0.020 deg; two RIEGL VQ-250; up to six 5 Mpx cameras plus one surface camera","Sec. 3.3, Table 1",{"c":944,"m":15518,"d":20,"f":15519,"v":15520,"n":23,"y":233,"u":15521},"Trimble MX9",[15309],[],[15522],[4224,952,29,15523,15501],"vehicle-mounted, 2018, outdoor; 1 spherical, 2 side-looking and 1 backward or downward camera; MX9 laser scanner up to 420 m; IMU and GPS; 0.5 cm (laser scanner, manufacturer)",{"c":651,"m":15525,"d":20,"f":15526,"v":15528,"n":23,"y":132,"u":15529},"Trimble R12i",[15527],"Trimble Inc., United States",[],[15530],[1990,53,29,15531,3832],"RTK std 8 mm horizontal, 15 mm vertical (manufacturer); 10 mm and 40 mm in practice with CZEPOS permanent stations; mounted on the scanning backpack for RTK_1 and RTK_2",{"c":5895,"m":15533,"d":20,"f":15534,"v":15535,"n":23,"y":100,"u":15536},"Trimble SX10",[15309],[],[15537],[2101,41,29,15538,15539],"set up at five locations to scan the tripod-mounted BLK2GO and targets (stationary test); EDM and angular observations for 3D coordinates of circuit targets (drift test)","Sec. 3.2; Sec. 3.3",{"c":2807,"m":15541,"d":20,"f":15542,"v":15543,"n":41,"y":100,"u":15544},"Trimble X7",[15309],[],[15545,15548],[9592,41,9593,15546,15547],"per-site TLS for sparse GCP ground truth and pre-survey of the 6x6 test grid; field auto registration; 92, 84 and 90% of registered scans within 3 mm registration uncertainty (Sites 1-3)","Fig. 1c, Sec. III-D, Sec. IV-D, Fig. 5b",[3843,952,29,15549,4206],"1550 nm, FoV 360 x 282 deg, 0.5 Mpts\u002Fs, 0.6-80 m, 2 mm, 21 arcsec; 30 stations every ~4 m merged by ICP (cloud-to-cloud error 4.8 mm)",{"c":2807,"m":15551,"d":20,"f":15552,"v":15553,"n":41,"y":132,"u":15554},"Trimble X9",[15309],[],[15555,15557],[3836,41,29,15556,530],"reference point cloud with registration errors minimised",[3840,41,29,15558,917],"reference point clouds",{"c":1689,"m":15560,"d":46,"f":15561,"v":15562,"n":23,"y":10603,"u":15563},"trinocular stereo system",[],[],[15564],[10606,53,29,15565,15566],"mounted on the authors' mobile vehicle; 3-D curves reconstructed with the curve-based trinocular stereo algorithm of Robert and Faugeras (1991); in the chair test the chair was about 3 m away and the two positions differed by about 4 deg and 100 mm (36 curves and 588 points, 48 curves and 763 points); camera models and optics not reported","Sec. 5.3; Fig. 17",{"c":372,"m":15568,"d":46,"f":15569,"v":15570,"n":23,"y":132,"u":15571},"tripod",[],[],[15572],[4195,23,4196,15573,15574],"static tripod-mounted stations chosen by a trained civil engineer for maximum coverage with minimum stations","Methods, Data acquisition",{"c":33,"m":15576,"d":46,"f":15577,"v":15578,"n":23,"y":357,"u":15579},"tripod on a linear translation stage slotted onto a floor-fixed measuring rule (model not reported)",[],[],[15580],[12941,41,29,15581,15582],"stereo rig moved repeatedly to predetermined locations in 15 cm steps from 10 m to 1 m from the wall; sequences taken with and without fiducials on the wall","Sec. 4 (Experimental Results); Fig. 11(a)",{"c":372,"m":15584,"d":46,"f":15585,"v":15586,"n":23,"y":100,"u":15587},"trolley (pushed cart)",[],[],[15588],[4756,53,9801,15589,9803],"sensors strapped down on the trolley",{"c":372,"m":15591,"d":46,"f":15592,"v":15593,"n":23,"y":132,"u":15594},"trolley-based dual-MLS integrated platform",[],[],[15595],[2071,53,29,15596,15597],"custom design carrying both MLS systems and a 360° prism","Sec. 1 contributions, 4.1; Fig. 6",{"c":372,"m":15599,"d":46,"f":15600,"v":15601,"n":23,"y":142,"u":15602},"truck (model not reported)",[],[],[15603],[9060,23,9061,15604,3807],"vehicle carrying the laser range-finder",{"c":5895,"m":15606,"d":20,"f":15607,"v":15608,"n":23,"y":318,"u":15609},"TS16",[9159],[],[15610],[688,41,15611,15612,7969],"ANYmal (Fire Service College)","laser tracking system following a reflective prism with millimetre accuracy; ANYmal experiment",{"c":522,"m":15614,"d":46,"f":15615,"v":15616,"n":23,"y":346,"u":15617},"TUM monoVO camera (model not reported in this paper)",[],[],[15618],[349,23,15619,15620,15621],"TUM monoVO","50 photometrically calibrated sequences (response, vignetting, exposure times), 105 minutes, about 190,000 frames; exposure varied from 0.018 to 10.5 ms in an indoor-outdoor sequence","Sec. 2.1.2, Fig. 3; Sec. 4 datasets; Fig. 11",{"c":1776,"m":15623,"d":46,"f":15624,"v":15625,"n":23,"y":289,"u":15626},"TUM RGB-D benchmark camera (model not stated)",[],[],[15627],[2595,23,991,15628,15629],"hand-held indoor sequences; images used as monocular input","Sec. VIII, VIII-B",{"c":1776,"m":15631,"d":46,"f":15632,"v":15633,"n":23,"y":270,"u":15634},"TUM RGB-D benchmark sensor (model not stated)",[],[],[15635],[5194,23,991,15636,147],"images used as monocular input; the first depth map used only for bootstrapping and initial scale",{"c":1776,"m":15638,"d":46,"f":15639,"v":15640,"n":23,"y":1819,"u":15641},"TUM RGB-D sensor (model not stated)",[],[],[15642],[1783,23,991,15643,15644],"640x480 at 30 Hz; freiburg2 depth maps with about 4% scale bias","Sec. IV-C, Table IV",{"c":522,"m":15646,"d":46,"f":15647,"v":15648,"n":23,"y":346,"u":15649},"TUM-Mono camera (model not reported)",[],[],[15650],[3979,23,15651,15652,332],"TUM-Mono","50 indoor and outdoor sequences with photometric calibration and start equal to end point",{"c":1689,"m":15654,"d":46,"f":15655,"v":15656,"n":23,"y":49,"u":15657},"TUM-VI hand-held fisheye stereo-inertial rig, cameras (model not stated)",[],[],[15658],[3658,23,15659,15660,5221],"TUM-VI","fisheye stereo; CLAHE equalisation applied; 1500 ORB points per image (monocular-inertial) or 1000 (stereo-inertial)",{"c":944,"m":15662,"d":46,"f":15663,"v":15664,"n":23,"y":233,"u":15665},"TUM-VI handheld visual-inertial device (model not reported in this paper)",[],[],[15666],[742,23,15659,15667,469],"large-scale indoor and outdoor handheld sequences, including sliding down a tube",{"c":662,"m":15669,"d":46,"f":15670,"v":15671,"n":23,"y":49,"u":15672},"TUM-VI rig IMU (model not stated)",[],[],[15673],[3658,23,15659,15674,5221],"part of the hand-held stereo-inertial rig",{"c":33,"m":15676,"d":46,"f":15677,"v":15678,"n":23,"y":37,"u":15679},"turntable",[],[],[15680],[2821,41,29,15681,917],"tabletop scene rotated through a full rotation in about 19 s (560 frames) with the Kinect fixed, equivalent to a precise circular sensor path",{"c":372,"m":15683,"d":46,"f":15684,"v":15685,"n":23,"y":299,"u":15686},"turntable (model not reported)",[],[],[15687],[7393,23,15688,15689,228],"Flowerpot and Teapot (Nguyen et al.)","objects rotated in front of a stationary Kinect",{"c":372,"m":15691,"d":46,"f":15692,"v":15693,"n":23,"y":152,"u":15694},"TurtleBot",[],[],[15695],[1674,53,29,15696,15697],"data collection platform for the geo-tagged environmental data collection example","Sec. 6.2, Fig. 14",{"c":108,"m":15699,"d":20,"f":15700,"v":15701,"n":23,"y":100,"u":15702},"two 16-channel Ouster LiDARs (horizontal and vertical OS1-16)",[180],[],[15703],[905,23,184,15704,907],"10 Hz with per-point relative timestamps",{"c":108,"m":15706,"d":20,"f":15707,"v":15708,"n":23,"y":122,"u":15709},"two 16-line Velodyne LiDARs (inclined)",[221],[],[15710],[1191,23,1192,15711,15712],"large tilt angles","Sec. IV-A-2; Sec. IV-B-4",{"c":18,"m":15714,"d":20,"f":15715,"v":15716,"n":23,"y":132,"u":15717},"Two data-collection PCs (Intel i7)",[],[],[15718],[678,23,679,15719,530],"each 1 TB SSD, 64 GB DDR4, Intel i7, Ubuntu with real-time kernel patch, ROS; synchronized via NTP (recording computers, not runtime evaluation)",{"c":1689,"m":15721,"d":46,"f":15722,"v":15723,"n":23,"y":289,"u":15724},"two embedded WVGA monochrome cameras (model not stated)",[],[],[15725],[1074,53,29,15726,15727],"11 cm baseline, 20 Hz in the datasets (hardware up to 60 Hz), rigidly mounted on an aluminium frame with the IMU","Sec. VII-A1; Fig. 11",{"c":108,"m":15729,"d":46,"f":15730,"v":15731,"n":23,"y":299,"u":15732},"two fixed sweeping laser scanners (model not reported)",[],[],[15733],[7000,23,15734,15735,1416],"New College (Epoch C)","sweeping to the left and right side of the robot; 14 million end points",{"c":18,"m":15737,"d":20,"f":15738,"v":15739,"n":23,"y":122,"u":15740},"two Intel Xeon E5-2620V3",[98],[],[15741],[1191,28,29,15742,15743],"2.4 GHz, 32 GB RAM workstation","Sec. IV-A-1",{"c":1689,"m":15745,"d":46,"f":15746,"v":15747,"n":23,"y":152,"u":15748},"two megapixel cameras (model not reported)",[],[],[15749],[155,53,29,15750,15751],"stereo pair on the custom platform","Sec. VIII-A; Fig. 1(a)",{"c":33,"m":15753,"d":20,"f":15754,"v":15755,"n":23,"y":71,"u":15756},"two orthogonal inclinometers (built into the Faro 880)",[4107],[],[15757],[915,53,29,15758,15759],"manufacturer accuracy +\u002F-0.01 deg; residual RMS 0.034 deg (calibration 9) and 0.058 deg (calibration 10)","Sec. 3.1, 4.5",{"c":1689,"m":15761,"d":46,"f":15762,"v":15763,"n":23,"y":152,"u":15764},"two synchronized global-shutter colour cameras (front-facing stereo pair) on a custom multi-camera rig similar to Gohl et al. [22]",[],[],[15765],[1795,23,1796,15766,15767],"one camera supplies the RGB image of the RGB-D frames; both usable for stereo SLAM; raw Bayer images debayered, flat-field corrected, no white balancing","Sec. 5; Supp. Sec. 3.1, 4.1, Fig. 2",{"c":1689,"m":15769,"d":46,"f":15770,"v":15771,"n":23,"y":152,"u":15772},"two synchronized global-shutter infrared cameras (stereo pair below the colour cameras) on the same rig",[],[],[15773],[1795,23,1796,15774,15775],"active stereo depth by PatchMatch stereo with ZNCC cost and 11x11 window, reprojected to the colour camera; colour and depth recorded at exactly the same time","Sec. 5; Supp. Sec. 3.1, 4.6",{"c":1689,"m":15777,"d":46,"f":15778,"v":15779,"n":23,"y":152,"u":15780},"two synchronized monochrome PointGrey cameras",[12947],[],[15781],[2555,23,842,15782,917],"rectified 1241x376 images, baseline 0.54 m, 10 Hz, car roof",{"c":522,"m":15784,"d":46,"f":15785,"v":15786,"n":23,"y":132,"u":15787},"two synchronized pinhole cameras (left used)",[],[],[15788],[216,23,212,15789,157],"triggered at 10 Hz; 752x480 grayscale",{"c":372,"m":15791,"d":46,"f":15792,"v":15794,"n":23,"y":233,"u":15795},"two-wheeled Segway",[15793],"Segway",[],[15796],[2979,23,600,15797,12168],"abrupt rotations about the LiDAR axis",{"c":651,"m":15799,"d":20,"f":15800,"v":15801,"n":23,"y":233,"u":15802},"u-blox ZED-F9P",[4021],[],[15803],[1058,53,1059,15804,15805],"low-cost multi-band, multi-constellation receiver; raw measurements at 10 Hz","Sec. VIII-B; Table IV",{"c":651,"m":15807,"d":20,"f":15808,"v":15809,"n":23,"y":233,"u":15810},"u-blox ZED-F9P internal RTK engine with RTCM from a nearby base station",[4021],[],[15811],[1058,41,1059,15812,15805],"RTK solution at 10 Hz; about 1 cm accuracy in open areas; fails indoors and under heavy blockage",{"c":372,"m":15814,"d":46,"f":15815,"v":15816,"n":41,"y":233,"u":15817},"UAV",[],[],[15818,15821],[5386,53,29,15819,15820],"carries the Livox Avia facing the ground over a mountain, 3490 m trajectory; outdoor aerial experiment helped by Ambit-Geospatial (Acknowledgment)","Sec. IV-C2; Table V; Acknowledgment",[3255,23,212,15822,167],"aerial platform of NTU-VIRAL (fast UAV motion noted)",{"c":372,"m":15824,"d":46,"f":15825,"v":15826,"n":28,"y":152,"u":15827},"UAV (model not reported)",[],[],[15828,15830,15833],[7639,53,29,15829,745],"flown indoors (V1); held by hand and quickly shaken in V2-01; carrying mode in V3 outdoor random walk not stated in the TIE text",[431,53,29,15831,15832],"flight height 30 m; 9 min operation; SfM point cloud of 5,160,845 points over 88 x 46 x 20 m, 760.8 points\u002Fm2","Sec. 4.1; Table 2; Sec. 5",[1101,23,1137,15834,15835],"3 to 12 m\u002Fs, altitude 80 to 130 m; 9 sequences, 32.6 km","Sec. IV-B2; Table I",{"c":372,"m":15837,"d":46,"f":15838,"v":15839,"n":23,"y":100,"u":15840},"UAV (not further specified)",[],[],[15841],[5070,23,184,569,1204],{"c":522,"m":15843,"d":46,"f":15844,"v":15845,"n":23,"y":152,"u":15846},"UAV camera with 1\u002F2.3-inch CMOS sensor (model not reported)",[],[],[15847],[431,53,29,15848,15849],"80% image overlap; GSD 1 cm","Table 2; Sec. 5",{"c":651,"m":15851,"d":46,"f":15852,"v":15853,"n":23,"y":233,"u":15854},"UAV onboard GPS\u002FIMU navigation (model not reported)",[],[],[15855],[378,41,29,15856,2777],"used only for UAV navigation, not by FAST-LIO2; trajectories compared visually, GPS trajectories not available for quantitative evaluation",{"c":33,"m":15858,"d":46,"f":15859,"v":15860,"n":23,"y":346,"u":15861},"UAV photogrammetry DSM (nadir and oblique images; platform and camera not reported)",[],[],[15862],[2795,41,29,15863,15864],"check-point total RMSE 0.029 m (Valperga) and 0.023 m (Rocca)","Tables 10, 14",{"c":18,"m":15866,"d":46,"f":15867,"v":15868,"n":23,"y":299,"u":15869},"Ubuntu netbook (model not_reported)",[],[],[15870],[4141,952,29,15871,1212],"tethered to the ZEB1 for data storage and real-time processing, with a battery pack",{"c":18,"m":15873,"d":20,"f":15874,"v":15876,"n":23,"y":1008,"u":15877},"Ubuntu server with AMD EPYC 7763 CPU and NVIDIA A100 GPU",[15875],"AMD; NVIDIA",[],[15878],[2018,28,29,15879,15880],"used for FL-PointNet++ training (PyTorch 1.12.1, CUDA 11.3)","Sec. 2.3.1; Table A1",{"c":522,"m":15882,"d":20,"f":15883,"v":15884,"n":23,"y":100,"u":15885},"uEye 1221 LE",[],[],[15886],[4121,23,184,15887,5457],"two cameras on a UAV",{"c":522,"m":15889,"d":46,"f":15890,"v":15891,"n":23,"y":49,"u":15892},"uEye camera with a wide-angle fisheye lens",[],[],[15893],[3692,53,29,15894,1084],"monocular",{"c":522,"m":15896,"d":46,"f":15897,"v":15898,"n":41,"y":289,"u":15899},"uEye monochrome camera",[],[],[15900,15903],[5332,53,29,15901,15902],"60 Hz, 752 x 480 px, wide-angle lens with 76 deg horizontal FoV","Sec. VII, Sec. VII-A, Fig. 8",[3860,53,29,15904,6165],"60 Hz frame rate, 752 x 480 pixels, 76 deg horizontal field of view",{"c":522,"m":15906,"d":20,"f":15907,"v":15908,"n":23,"y":346,"u":15909},"uEye UI-1220SE",[],[],[15910],[2905,53,29,15911,15912],"monochrome, 752 x 480 px, 76 deg horizontal FoV, 50 Hz","Sec. 10.1, Fig. 16",{"c":372,"m":15914,"d":46,"f":15915,"v":15916,"n":41,"y":233,"u":15917},"UGV",[],[],[15918,15921],[2135,23,2136,15919,15920],"off-road and some metro-tunnel sequences; aluminium-profile rack carrying all three devices","Table 3, Fig. 2d, Sec. 4.4.2",[5386,23,15922,15923,469],"SSL_SLAM dataset [33]","moving quite stably and slowly (sensor not restated in this paper)",{"c":372,"m":15925,"d":46,"f":15926,"v":15927,"n":41,"y":318,"u":15928},"UGV (model not reported)",[],[],[15929,15932],[4865,53,14310,15930,15931],"three UGVs in the powerplant, two in the foundry","Sec. 5.9.2; Table 2",[6382,53,29,15933,1204],"carries the Livox Mid-40 in the indoor corridor test",{"c":372,"m":15935,"d":46,"f":15936,"v":15937,"n":23,"y":100,"u":15938},"UGV (Unmanned Ground Vehicle), model not reported",[],[],[15939],[15370,23,15371,15940,15941],"average linear speed 0.33 m\u002Fs, average turn rate 14.85 deg\u002Fs; frame 63 captured at 0.33 m\u002Fs and 10.23 deg\u002Fs","Sec. 4 Data (UoM indoor data)",{"c":372,"m":15943,"d":20,"f":15944,"v":15945,"n":23,"y":152,"u":15946},"UGV from Asadi et al. 2018d (model not restated)",[],[],[15947],[3311,53,29,15948,15949],"moved along a path while recording video","Sec. Experimental Setup and Results (Initial Setup)",{"c":372,"m":15951,"d":46,"f":15952,"v":15953,"n":23,"y":122,"u":15954},"UGV in comparative scenario (model not reported)",[],[],[15955],[8013,952,29,15956,3230],"used with RTAB-Map, no motion integration; other configurations stated as identical",{"c":353,"m":15958,"d":46,"f":15959,"v":15960,"n":23,"y":318,"u":15961},"UGV motor encoders (model not reported)",[],[],[15962],[4865,53,14310,15963,14312],"multiple motor encoders",{"c":353,"m":15965,"d":46,"f":15966,"v":15967,"n":23,"y":318,"u":15968},"UGV wheel encoders fused with an IMU by EKF (models not named)",[],[],[15969],[2248,53,29,15970,147],"used instead of ZED visual odometry for RTAB-Map mapping",{"c":372,"m":15972,"d":20,"f":15973,"v":15974,"n":23,"y":49,"u":15975},"UGV1, UAV1 and UAV2 (Team Explorer robots)",[],[],[15976],[3692,53,29,15977,4103],"multi-robot mapping in the DARPA SubT Urban Alpha Course",{"c":372,"m":15979,"d":46,"f":15980,"v":15981,"n":23,"y":100,"u":15982},"UGV1, UGV2, UGV3 (wheeled robots; models not reported)",[],[],[15983],[1371,23,1372,15984,12952],"max speed 2 m\u002Fs in Urban, Tunnel, Cave, Nuclear sequences",{"c":944,"m":15986,"d":46,"f":15987,"v":15988,"n":23,"y":318,"u":15989},"UltraCAM Panther",[],[],[15990],[951,952,29,15991,15992],"backpack with low-cost 3D LiDAR; 17 kg; 300,000-600,000 pts\u002Fs; relative accuracy 3 cm; >1 h; 26 CCD cameras, 172 Mp, 1.4 x 1.4 µm pixels, 360° x 180°","Sec. 2.2.2; Tables 1-6; Fig. 5a",{"c":662,"m":15994,"d":20,"f":15995,"v":15997,"n":23,"y":152,"u":15998},"UM7",[15996],"Redshift Labs",[],[15999],[3036,53,29,16000,12767],"accelerometer, gyroscope, magnetometer, up to 255 Hz, 11 g; only angular velocities used in the EKF",{"c":33,"m":16002,"d":46,"f":16003,"v":16004,"n":23,"y":71,"u":16005},"Uncalibrated mid-resolution aerial image",[],[],[16006],[547,41,29,16007,16008],"distance ratios between reference points A-D compared with the point model","Sec. 5.4, Fig. 15, Table II",{"c":522,"m":16010,"d":46,"f":16011,"v":16013,"n":23,"y":71,"u":16014},"Unibrain Fire-i",[16012],"Unibrain",[],[16015],[6853,53,29,16016,398],"video camera with 2.1 mm wide-angle lens; 640x480 YUV411 frames at 30 Hz, converted to 8-bit greyscale",{"c":372,"m":16018,"d":20,"f":16019,"v":16021,"n":23,"y":233,"u":16022},"Unitree A1",[16020],"Unitree Robotics",[],[16023],[781,53,29,16024,8378],"quadruped robot dog, 620 x 300 x 340 mm, three motors per leg; moved at 0.3-0.4 m\u002Fs with roll and pitch posture changes; teleoperated by keyboard via SSH over a 5G mobile router",{"c":372,"m":16026,"d":20,"f":16027,"v":16029,"n":23,"y":132,"u":16030},"Unitree A1 ('IDOG' Intelligent DOcumentation Gadget)",[16028],"Unitree",[],[16031],[1296,53,29,16032,16033],"quadruped carrying lidar, IMU, camera, external computer and extra battery; manually controlled in the office test","Sec. 4.5; Fig. 7; Table 4",{"c":353,"m":16035,"d":20,"f":16036,"v":16037,"n":23,"y":132,"u":16038},"Unitree A1 built-in joint encoders and contact sensors",[16028],[],[16039],[678,23,679,16040,16041],"12 joint motor encoders and four contact sensors per leg (as written; located at the hip, thigh, calf and foot), 50 Hz; built-in IMU 50 Hz; out-of-the-box kinematic-inertial odometry 50 Hz; motor torque, velocity, position, temperature logged","Table 2; Sec. 3.2.1",{"c":372,"m":16043,"d":20,"f":16044,"v":16045,"n":23,"y":132,"u":16046},"Unitree A1 quadruped",[16028],[],[16047],[678,23,679,16048,16049],"suite on dorsal side, Ethernet to kinematic sensors; about 1.5 m\u002Fs","Sec. 3.2.1; Sec. 5.1.2",{"c":372,"m":16051,"d":46,"f":16052,"v":16053,"n":23,"y":132,"u":16054},"Unitree Aliengo",[16020],[],[16055],[1904,53,29,16056,16057],"65 cm x 31 cm x 60 cm, 21 kg; forward velocity fixed at 0.2 m\u002Fs","Sec. 3.1, Table 2",{"c":372,"m":16059,"d":20,"f":16060,"v":16061,"n":23,"y":132,"u":16062},"Unitree Go 1 Edu",[16020],[],[16063],[2352,53,29,16064,16065],"quadruped; onboard one Raspberry Pi, two Jetson Nano, one Jetson NX on a wired network; high-level manufacturer API used for motion control","Sec. 3.1.2, Sec. 4.1, Fig. 11",{"c":372,"m":16067,"d":20,"f":16068,"v":16069,"n":23,"y":122,"u":16070},"Unitree Go1",[16028],[],[16071],[8013,53,29,16072,16073],"legged robot, 3 motors per leg, 12 DOF; controlled via unitree_legged_sdk over UDP; rolls body -15 deg and +30 deg at each scan position","Sec. 3.1.1, 3.1.2, 4.1",{"c":372,"m":16075,"d":20,"f":16076,"v":16077,"n":23,"y":1008,"u":16078},"Unitree Go1 quadruped",[16028],[],[16079],[1155,23,12026,16080,332],"indoor sequences, one parking lot and one running sequence",{"c":33,"m":16082,"d":46,"f":16083,"v":16085,"n":23,"y":318,"u":16086},"Unity-based photo-realistic simulator provided by MIT Lincoln Lab",[16084],"MIT Lincoln Laboratory",[],[16087],[2953,23,16088,16089,16090],"photo-realistic simulator (MIT Lincoln Lab)","ROS sensor streams with ground-truth geometry, semantics, depth and poses; 720x480 dense depth images","Sec. III-C; Sec. III-D; Acknowledgments",{"c":522,"m":16092,"d":46,"f":16093,"v":16094,"n":23,"y":1008,"u":16095},"upward high-resolution RGB camera with low FOV (UGV and USV; model not stated)",[],[],[16096],[1011,53,29,16097,147],"captures undersides of infrastructure",{"c":1776,"m":16099,"d":46,"f":16100,"v":16101,"n":23,"y":100,"u":16102},"upward- and downward-facing depth cameras (model not reported)",[],[],[16103],[1396,53,29,16104,16105],"CTU-CRAS-Norlab UAV; integrated into the dense map to cover LIDAR blind spots, not used for localization","Sec. III-E-2, Fig. 5",{"c":372,"m":16107,"d":46,"f":16108,"v":16109,"n":23,"y":385,"u":16110},"Urban Robot",[12779],[],[16111],[388,53,29,16112,16113],"tracked skid-steering robot for indoor and outdoor exploration with extremely poor odometry","Fig. 1 caption; Sec. 3.3",{"c":372,"m":16115,"d":46,"f":16116,"v":16117,"n":23,"y":37,"u":16118},"USV platform (model not reported)",[],[],[16119],[5601,53,29,16120,16121],"carried the Hector UGV mapping system on Claytor Lake, Virginia","Sec. VI-B, Fig. 4c",{"c":372,"m":16123,"d":46,"f":16124,"v":16125,"n":41,"y":1819,"u":16127},"Utility vehicle",[],[16126],"utility vehicle",[16128,16131],[5093,53,29,16129,16130],"sidewalks and off-road terrain; 2-3 m\u002Fs on 1.0 km campus run","Sec. 7.4, Fig. 17a",[2905,53,29,16132,16133],"off-road driving; both sensor suites attached","Fig. 16d",{"c":108,"m":16135,"d":20,"f":16136,"v":16137,"n":23,"y":152,"u":16138},"UTM30",[],[],[16139],[2555,23,13022,16140,3230],"2D long-range lidar, 30 m range, 40 Hz; also filtered to 5.6 m to emulate a short-range lidar such as a URG04LX",{"c":944,"m":16142,"d":46,"f":16143,"v":16144,"n":23,"y":49,"u":16145},"UVigo Backpack",[],[],[16146],[9220,23,9211,16147,16148],"UVigo: 14.9 M points, 1.0 cm spacing, trajectory, moderate clutter; non-commercial experimental backpack lidar with slightly lower accuracy; median point-model distance 4.9 cm","Table 1; Sec. 5.4",{"c":944,"m":16150,"d":46,"f":16151,"v":16153,"n":23,"y":1819,"u":16154},"UVigo Backpack (prototype backpack-based mobile mapping system)",[16152],"University of Vigo prototype (Filgueira et al., 2016)",[],[16155],[9210,23,9211,16156,9213],"max range 100 m; 300 x 10^3 points\u002Fs; 0.1 to 0.4 deg horizontal, 2.0 deg vertical; FOV 30 x 360 deg; relative accuracy 3 cm; absolute accuracy not given (Table 2); UVigo: 14.9 x 10^6 points, 0.010 m spacing, timestamped trajectory (Table 1)",{"c":372,"m":16158,"d":46,"f":16159,"v":16160,"n":23,"y":1421,"u":16161},"vacuum cleaner pushed on a trolley",[],[],[16162],[2638,53,29,16163,13389],"Revo LDS data captured by pushing the vacuum cleaner around on a trolley",{"c":18,"m":16165,"d":20,"f":16166,"v":16168,"n":41,"y":4627,"u":16169},"VAX 11\u002F780",[16167],"Digital Equipment Corporation",[],[16170,16174],[16171,28,29,16172,16173],"arun1987svd","programs written in C; IMSL subroutines LSVDF and EIGRS","Sec. VII, Table I",[4630,28,29,16175,3364],"under VMS; 100 s to 30 min per model",{"c":662,"m":16177,"d":46,"f":16178,"v":16179,"n":23,"y":132,"u":16180},"VBR IMU (model not reported in this paper)",[],[],[16181],[3944,23,16182,16183,5457],"VBR (Vision Benchmark in Rome)","IMU measurements; the driving sequences Campus* and Ciampino* contain episodes of missing IMU measurements",{"c":108,"m":16185,"d":46,"f":16186,"v":16187,"n":23,"y":132,"u":16188},"VBR LiDAR (model not reported)",[],[],[16189],[3944,23,16182,16190,5457],"far plane limited to 30 m",{"c":1689,"m":16192,"d":46,"f":16193,"v":16194,"n":23,"y":132,"u":16195},"VBR stereo camera (model not reported; high resolution)",[],[],[16196],[3944,23,16182,16197,5457],"with IMU, LiDAR and ground-truth poses",{"c":662,"m":16199,"d":20,"f":16200,"v":16202,"n":23,"y":49,"u":16203},"Vector Nav 100",[16201],"VectorNav",[],[16204],[1359,53,16205,16206,16207],"DARPA SubT Husky datasets (CoSTAR)","recorded at 50 Hz in the Urban datasets and 100 Hz in the Tunnel dataset","Sec. III dataset description",{"c":662,"m":16209,"d":46,"f":16210,"v":16211,"n":23,"y":132,"u":16212},"VectorNav IMU (model not stated)",[16201],[],[16213],[5348,23,7967,569,7969],{"c":662,"m":16215,"d":20,"f":16216,"v":16217,"n":23,"y":346,"u":16218},"VectorNav VN-100 Rugged",[16201],[],[16219],[4101,53,4884,1272,469],{"c":662,"m":16221,"d":20,"f":16222,"v":16223,"n":23,"y":132,"u":16224},"VectorNav VN-200",[16201],[],[16225],[2221,23,12217,16226,1104],"ATV and handheld",{"c":662,"m":16228,"d":20,"f":16229,"v":16230,"n":8521,"y":318,"u":16233},"VectorNav VN100",[16201],[16231,16232],"VectorNav VN-100","VectorNav-VN100",[16234,16236,16238,16239,16241,16243,16244,16247,16248],[715,53,29,16235,7796],"rigidly mounted below the LiDAR base on both field platforms; gyroscope used for optional rotational prior",[715,23,16237,569,717],"DARPA SubT Urban Circuit Alpha Course",[1263,53,12278,1272,1351],[5070,23,184,16240,1204],"9-axis IMU",[4412,53,29,16242,1157],"inertial measurements at 200 Hz",[4121,23,184,569,5457],[3460,23,184,16245,16246],"rugged IMU, configured at 400 Hz, effective ~385 Hz; also outputs internal EKF orientation fused with magnetometer; body frame origin","Sec. 2.1, Table 2",[1219,53,1220,569,745],[1191,23,184,569,1193],{"c":372,"m":16250,"d":46,"f":16251,"v":16252,"n":41,"y":122,"u":16253},"vehicle",[],[],[16254,16257],[2135,23,2136,16255,16256],"urban-tunnel and bridge sequences, device alpha only","Table 3, Sec. 4.4.2",[5280,53,6600,16258,16259],"up to about 50 km\u002Fh, U-turns and a 400 m tunnel","Sec. III-A; Sec. III-D",{"c":372,"m":16261,"d":46,"f":16262,"v":16263,"n":23,"y":233,"u":16264},"vehicle (MulRan data collection car, model not stated)",[],[],[16265],[668,23,4281,16266,4283],"urban driving in Daejeon, sequence about 5 km",{"c":353,"m":16268,"d":46,"f":16269,"v":16270,"n":23,"y":554,"u":16271},"vehicle odometry (sensor not stated)",[],[],[16272],[3371,23,3372,569,167],{"c":108,"m":16274,"d":46,"f":16275,"v":16276,"n":41,"y":132,"u":16277},"Velodyne (model not named)",[221],[],[16278,16280],[3427,23,16279,569,2207],"S3E",[1966,23,16281,569,1104],"KITTI; WildPlaces",{"c":108,"m":16283,"d":46,"f":16284,"v":16285,"n":23,"y":132,"u":16286},"Velodyne 64-beam LiDAR (model not stated)",[221],[],[16287],[1588,23,12563,16288,1084],"motion-distorted raw point clouds",{"c":108,"m":16290,"d":20,"f":16291,"v":16292,"n":23,"y":152,"u":16293},"Velodyne 64E",[221],[],[16294],[2555,23,842,16295,917],"roof-mounted, synchronized with stereo at 10 Hz; downsampled with a 50 cm voxel filter for S2S and S2M",{"c":108,"m":16297,"d":20,"f":16298,"v":16299,"n":23,"y":132,"u":16300},"Velodyne Alpha-Prime 128-beam",[221],[],[16301],[1588,23,1589,16302,16303],"128-beam lidar on the Boreas data collection platform","Fig. 14; Sec. V-D",{"c":662,"m":16305,"d":20,"f":16306,"v":16307,"n":41,"y":152,"u":16309},"Velodyne HDL-32 built-in IMU",[221],[16308],"Velodyne HDL 32 built-in IMU",[16310,16311],[2559,23,2560,569,2562],[2567,53,2568,569,16312],"Sec. 5 Own Datasets",{"c":108,"m":16314,"d":20,"f":16315,"v":16316,"n":16326,"y":270,"u":16327},"Velodyne HDL-32E",[221],[16317,16318,16319,16320,16321,16322,16323,16324,16325],"HDL-32 E (as written)","HDL-32E","Velodyne HDL 32","Velodyne HDL 32E","Velodyne HDL-32","Velodyne HDL-32E (two, left and right of the radar)","Velodyne HDL-32e","Velodyne HDL32","Velodyne HDL32E",30,[16328,16329,16332,16335,16337,16339,16342,16345,16347,16348,16350,16353,16356,16359,16362,16364,16367,16371,16374,16376,16378,16379,16381,16383,16386,16388,16391,16395,16398,16401,16403,16406,16409],[6316,53,29,569,1072],[282,23,5008,16330,16331],"32 rings; vehicle","Table 1; Sec. 4.4",[7474,23,16333,12476,16334],"NCLT; ULHK","Sec. III-B-1; Sec. III-B-4",[2979,23,5008,16336,2300],"mounted vertically on the authors' vehicle; 4 km, 12751 scans in central Paris",[2979,23,600,16338,12168],"mounted on a two-wheeled Segway",[2612,53,29,16340,16341],"32 laser beams, spinning at 10 Hz (100 ms per scan); mounted vertically on a car roof (Paris, 12951 scans) and tilted 60 degrees in pitch (Lille, 1500 scans)","Sec. VI; Sec. VI-A",[2528,952,29,16343,16344],"range residuals of its first 16 lasers after static calibration (from Glennie et al. 2013); overall RMSE 22.7 mm","Sec. 4.2, Fig. 5",[2169,23,16346,12324,3364],"utbm and ulhk",[5280,23,2577,12515,10036],[7586,23,600,16349,10205],"32-ray, attached to a Segway mobile platform",[5118,23,6790,16351,16352],"32-ray; scans concatenated into one cloud","Sec. VI-A3; Table III",[1957,53,29,16354,16355],"360 deg range data at 10 Hz; angular velocity output used for UKF pose prediction","Sec. System overview; Sec. Sensor localization",[7253,53,16357,16358,469],"authors' real HDL-32e sequences","about 15,000 points per frame; eight sequences of about 120 m",[9620,53,29,16360,16361],"3D LiDAR; fitted aperture half-angle 0.085 deg (Table I); mounted on the robot for the 3D tunnel map","Sec. IV, V, Table I, Fig. 9",[2452,23,2577,16363,6265],"32-beam 3D LiDAR, 10 Hz",[2559,23,2560,16365,16366],"about four times the data of the VLP-16; roof of a car","Sec. VII-E-2; Table VII",[192,23,16368,16369,16370],"Ford AV","four units","Sec. 8.1",[1938,23,16372,16373,1416],"UTBM (EU long-term), UrbanLoco, UrbanNav","spinning LiDAR of the public data sets",[3444,23,600,16375,1104],"mechanical spinning 32-line; FoV 360.0 x 41.3 deg; 695,000 points\u002Fs; USD 8,800",[201,23,11303,16377,1204],"car driving on urban streets",[4092,53,4093,185,478],[2173,23,2577,16380,2174],"robotcar urban dataset",[2567,53,2568,16382,16312],"multi-beam LiDAR with built-in IMU; per-point timestamps from datasheet",[1976,23,1977,16384,16385],"on a backpack mapping system; indoor 5-floor building","Sec. IV-B1, Table I",[1730,53,29,16387,127],"roughly 70,000 points per 360 deg scan at 11 Hz; maximum range 80 m",[861,53,29,16389,16390],"Artor: about 50,000 points per scan at 5 Hz (half nominal rate); watercraft: about 45,000 points at 1.6 Hz","Sec. 3.1.5, 3.3; Table 3.7",[16392,53,29,16393,16394],"aloam_software","32 scan lines (scan_line 32); launch configuration only, no example data","launch\u002Faloam_velodyne_HDL_32.launch",[378,23,16396,16397,9835],"UTBM robocar dataset (utbm)","two units at 10 Hz on a human-driven robocar (max 50 km\u002Fh); only the left LiDAR used",[378,23,16399,16400,9835],"UrbanLoco HK (ulhk)","10 Hz, human-driven vehicle",[378,23,600,16402,9835],"10 Hz, UGV",[5093,53,29,16404,16405],"single-axis scanner with 32 beams, 10 Hz by default; mounted high on vehicle roof","Sec. 7.4, Fig. 17",[2905,53,29,16407,16408],"360 deg horizontal and 40 deg vertical FoV, 0.7 million points per second at 5 Hz spinning rate","Sec. 10.1, Fig. 16a",[15370,23,15371,16410,15941],"up to 72,000 points per cloud; average 67,573 points over 1187 clouds; mounted on a moving UGV",{"c":108,"m":16412,"d":20,"f":16413,"v":16415,"n":16423,"y":357,"u":16424},"Velodyne HDL-64E",[16414,221],"VELODYNE",[16416,16417,16418,16419,16420,16421,16422],"HDL-64E","VELODYNE HDL-64E S2","Velodyne HDL 64","Velodyne HDL-64","Velodyne HDL-64E (simulated)","Velodyne HDL-64E S2","Velodyne HDL64",38,[16425,16428,16431,16433,16436,16439,16441,16444,16447,16450,16451,16454,16457,16459,16462,16463,16466,16469,16471,16475,16478,16479,16481,16484,16486,16488,16490,16493,16496,16499,16502,16503,16507,16509,16511,16512,16515,16517,16519,16521,16523,16525],[7578,23,578,16426,16427],"provided KITTI point clouds recorded at 10 Hz; parameters assume vertical FoV fup 3.0 deg and fdown 25 deg, vertex map 900 x 64 (Table I)","Sec. IV, Table I",[282,23,578,16429,16430],"64 rings; 0.205 deg vertical angle correction applied","Table 1; Sec. 4.2",[282,23,16432,16250,4453],"KITTI-360",[4572,23,16434,16435,478],"KITTI (odometry benchmark and raw road drives)","point clouds recorded at 10 Hz (provided by KITTI)",[924,23,578,16437,16438],"not_reported (the paper gives only processing settings: 64 x 900 range-image input and a 75 m cutoff for overlap ground truth)","Sec. IV; Sec. III-C",[4511,23,578,16440,639],"KITTI recording platform; only the Velodyne data are used",[2979,23,16442,16443,2300],"KITTI \u002F KITTI-raw \u002F KITTI-360","64-beam, mounted on a car; KITTI-raw and KITTI-360 scans not motion-corrected, timestamps estimated from azimuth; 0.205 deg intrinsic angle correction applied",[2612,23,578,16445,16446],"vertical, on the roof of a car, 64 beams, 10 Hz; scans provided de-skewed","Sec. VI; Sec. VI-B",[114,23,16448,16449,332],"KITTI (SemanticKITTI labels and poses)","64-channel; about 0.1 million points per scan, 30 deg vertical FoV (as compared in Sec. IV-A)",[6422,23,842,569,745],[12546,23,842,16452,16453],"10 Hz, 64 laser beams, range 100 m, more than one million points per second; egomotion compensated with GPS\u002FIMU","Sec. 1, Sec. 2.1",[6489,53,29,16455,16456],"64 lasers; 360 deg by 26.8 deg FOV; range 50 m (10% reflectivity) and 120 m (80%); 1.5 cm range precision (1 sigma); 0.09 deg azimuth resolution; 1,333,333 measurements per second; 905 nm, 5 ns pulse, 2.0 mrad beam divergence; head up to 900 rpm (Table 6 lists 20 mm range accuracy)","Introduction; Table 1; Table 6",[7586,23,842,16458,793],"64-ray, located in the centre of the car",[5118,23,578,16460,16461],"64-ray, full HFOV","Sec. VI-A1; Table III",[192,23,842,12324,16370],[6523,23,578,16464,16465],"64 laser beams, 10 Hz, about 1.3 million points\u002Fsecond; encoded as 64 x 1800 matrices cropped to 1792","Sec. 4 implementation details; Sec. 4.5",[6523,23,6524,16467,16468],"horizontally scanning 3D lidar mounted on top of a vehicle; rotates at 10 Hz","Sec. 4.1; Sec. 4.5",[7159,23,578,16470,332],"used in both KITTI and Ford Campus",[7159,23,16472,16473,16474],"Ford Campus Vision and Lidar Dataset","Ford sensor setting gives sparser projection images than KITTI","Sec. IV-A; Sec. IV-C",[1938,952,10596,16476,16477],"high-end spinning LiDAR mounted on the same platform for comparison","Sec. 5.3.1; Fig. 7",[5783,23,842,569,2162],[3444,23,842,16480,1104],"mechanical spinning 64-line; FoV 360.0 x 26.8 deg; 1,333,312 points\u002Fs; USD 75,000",[4039,53,4040,16482,16483],"64 laser diodes over about 26 deg pitch, continuous 360 deg rotation at about 10 Hz; each turn arranged as an 870 x 64 range image; relatively high measurement noise","Sec. I; Sec. II-A; Sec. II-D",[2567,23,578,16485,2805],"scans pre-compensated in the KITTI odometry release",[1615,53,29,16487,1617],"one unit on MIT's DARPA Urban Challenge vehicle, synchronized with this algorithm; deployment context, not evaluated in the paper",[1976,23,578,16489,12411],"written 'HDL64E' in Table I; 22 sequences, more than 43k scans",[507,23,578,16491,16492],"64-beam, car-mounted; reference poses from GNSS-INS","Sec. V-A1; Table I",[1606,952,29,16494,16495],"64 lasers in two blocks; 360 deg horizontal x 26.8 deg vertical FOV; 5 to 15 Hz; more than 1.3 million points\u002Fs; 905 nm; distance accuracy below 2 cm (1 sigma) for 10% target at 50 m or 80% target at 120 m; used in TOPCON IP-S2 HD","Sec. 2.2, Sec. 3.2",[16392,23,832,16497,16498],"64 scan lines (scan_line 64), minimum_range 5 m in launch file","README Sec. 4; launch\u002Faloam_velodyne_HDL_64.launch",[7490,23,16500,16501,469],"Mai City","simulated 64-beam LiDAR in CARLA",[7490,23,578,7411,745],[2680,23,16504,16505,16506],"KITTI odometry (sequence 00)","360 deg horizontal FOV, 48 more channels than the VLP-16, vertical FOV 26.9 deg; downsampled to the VLP-16 range image (75% of points omitted) for real time on the Jetson","Sec. II; Sec. IV-D",[517,23,6822,16508,6824],"car roof; Columbia Park sequence; qualitative result",[1183,23,842,16510,745],"on the KITTI car",[1850,23,832,569,469],[7256,23,16513,16514,469],"KITTI Vision Benchmark (odometry)","on-board KITTI sensor; point clouds provided already de-skewed",[5386,23,578,16516,8004],"mechanical, 10 Hz, repetitive scan, FoV 360°×32°; KITTI in-frame motion pre-compensated; 0.22° vertical angle correction applied as in IMLS-SLAM",[4348,23,578,16518,4496],"KITTI LiDAR",[5093,23,578,16520,8619],"logged at 10 Hz",[9955,23,842,16522,12952],"KITTI row of Table 7 (car platform, urban and rural); no other specs",[905,23,578,16524,332],"KITTI points are motion-corrected, per-point time discarded",[5523,23,832,16526,469],"KITTI odometry sequences 00-10, 23,201 scans",{"c":108,"m":16528,"d":46,"f":16529,"v":16530,"n":23,"y":289,"u":16531},"Velodyne laser scanner (KITTI ground truth, model not stated)",[221],[],[16532],[2595,41,578,16533,2597],"used with GPS for the ground truth",{"c":108,"m":16535,"d":20,"f":16536,"v":16537,"n":23,"y":1008,"u":16538},"Velodyne LiDAR",[221],[],[16539],[3053,23,842,16540,3056],"Vehicle-mounted; with stereo cameras, GPS and inertial sensors",{"c":108,"m":16542,"d":46,"f":16543,"v":16544,"n":23,"y":270,"u":16545},"Velodyne lidar (360 deg, model not named)",[221],[],[16546],[2630,23,5847,16520,16547],"Sec. VII-C; Fig. 13",{"c":108,"m":16549,"d":46,"f":16550,"v":16551,"n":23,"y":49,"u":16552},"Velodyne LiDAR (KITTI; model not specified in the paper)",[221],[],[16553],[11700,23,578,16554,917],"XYZ and reflectance provided; only XYZ used; transformed to the left camera frame",{"c":108,"m":16556,"d":46,"f":16557,"v":16558,"n":23,"y":100,"u":16559},"Velodyne LiDAR (model not named)",[221],[],[16560],[3805,23,600,185,441],{"c":108,"m":16562,"d":46,"f":16563,"v":16564,"n":23,"y":1008,"u":16565},"Velodyne LiDAR (model not_reported)",[221],[],[16566],[2537,23,2767,569,917],{"c":108,"m":16568,"d":20,"f":16569,"v":16570,"n":23,"y":289,"u":16571},"Velodyne lidar of the KITTI setup",[221],[],[16572],[5332,23,5847,569,4496],{"c":108,"m":16574,"d":46,"f":16575,"v":16576,"n":23,"y":233,"u":16577},"Velodyne lidars (three per Husky; model not reported)",[221],[],[16578],[1405,53,6209,16579,6211],"merged after extrinsic calibration and adaptive voxelization",{"c":108,"m":16581,"d":20,"f":16582,"v":16583,"n":23,"y":318,"u":16584},"Velodyne PuckLITE",[221],[],[16585],[4412,53,29,16586,332],"point clouds at 10 Hz",{"c":108,"m":16588,"d":20,"f":16589,"v":16590,"n":23,"y":455,"u":16591},"Velodyne range finder",[221],[],[16592],[4849,53,4850,16593,16594],"roof-mounted on an instrumented car; scans cover a range of 70 to 100 m from the sensor","Sec. IV (pp. 4 and 8), Fig. 4",{"c":108,"m":16596,"d":20,"f":16597,"v":16605,"n":16624,"y":1421,"u":16625},"Velodyne VLP-16",[221,16598,16599,16600,16601,16602,16603,16604],"Velodyne (San Jose, CA, USA)","Velodyne (as named)","Velodyne (inferred from the model name; the paper does not name the maker)","Velodyne (per ref. [74])","Velodyne (sensor specification simulated)","Velodyne LiDAR (Morgan Hill, CA)","Velodyne Lidar (per ref. [5])",[16606,16607,16608,16609,16610,16611,16612,16613,16614,16615,16616,16617,16618,16619,16620,16621,16622,16623],"VLP-16","VLP-16 (introduction says VLP-16E)","VLP16","Velodyne PUCK (VLP-16)","Velodyne Puck Lite","Velodyne Puck VLP16","Velodyne VLP-16 (simulated in Gazebo)","Velodyne VLP-16 (simulated)","Velodyne VLP-16 (two units)","Velodyne VLP-16 (upward-mounted, UGV and USV)","Velodyne VLP-16 (virtual)","Velodyne VLP-16 PUCK (two, tilted left and right)","Velodyne VLP-16 Puck","Velodyne VLP-16 Puck Lite","Velodyne VLP16","Velodyne VLP16 (16 beams)","Velodyne VLP16 (Velodyne puck)","Velodyne VLP16 Lite (also written 'Velodyne Puck Lite')",76,[16626,16628,16631,16634,16637,16639,16641,16644,16646,16649,16650,16653,16655,16657,16660,16662,16666,16669,16671,16673,16676,16678,16680,16682,16685,16687,16690,16692,16694,16696,16700,16703,16705,16708,16710,16713,16716,16718,16721,16724,16727,16730,16732,16734,16737,16741,16744,16746,16749,16751,16753,16756,16758,16762,16765,16769,16772,16774,16777,16779,16780,16782,16784,16787,16790,16793,16796,16798,16801,16803,16804,16806,16809,16812,16814,16817,16820,16822,16825,16829,16832,16833,16835,16838,16840,16841,16843],[9143,53,29,16627,167],"used for all datasets; ESDF resolution derived from LiDAR resolution: 0.18 m vertical, 0.03 m horizontal",[9143,23,16629,16630,167],"TIERS LiDARs dataset","TIERS multi-modal LiDAR dataset recorded by a moving platform; sequences T6-T8 small room, T10-T11 larger hallway",[282,23,1494,16632,16633],"one per ANYmal C robot","Table 1; Sec. 4.8",[282,23,13862,16635,16636],"electric vehicle on university campus","Table 1; Sec. 4.10",[688,53,1454,16638,2946],"mounted on top of ANYmal for localization and global mapping; used by AICP",[3036,53,29,16640,4480],"two units (vertical for overhead mapping, horizontal for localization); 16 beams; vertical lidar 830 g with 20 deg FOV, horizontal lidar 590 g with 30 deg FOV (as reported); scans at 10 Hz",[1011,53,29,16642,16643],"590 g; vertical FOV 30 deg; 300,000 points per second; 100 m range; own time-stamping synchronized by PPS and NMEA","Table 1; Sec. 4.1-4.3",[1011,53,29,569,16645],"Sec. 4.2, Fig. 10 caption",[715,53,29,16647,16648],"360 deg LiDAR, 10 Hz; with protective guards on Spot","Fig. 1B; Sec. II-B; Sec. III-C",[715,23,16237,569,717],[4511,53,8773,16651,16652],"two lidars on the backpack; only the horizontal one used; 10 Hz","Sec. IV; Sec. IV-D",[7474,23,2426,569,16654],"Sec. III-B-5",[2135,23,2136,16656,1374],"16 scan lines, range 120 m, vertical FOV 30 deg, horizontal FOV 360 deg, 10 Hz; device alpha",[1948,53,3553,16658,16659],"about 300,000 range measurements per second, 16 rings, 30 deg vertical field of view, 100 m maximum range, up to 1200 rpm; a scan line is one data packet of 24 firing sequences of 1.33 ms","Sec. IV; Sec. V-A",[1948,23,1949,16661,1072],"one mounted horizontally and one vertically on a backpack; calibration provided with the data and refined in the graph",[114,23,16663,16664,16665],"Semi-indoor (self-collected)","sparse 16-channel LiDAR in a highly structured semi-indoor environment","Sec. IV-A, Fig. 5",[4290,53,29,16667,16668],"not_reported (low vertical resolution noted as a cause of poor scan-to-scan estimates in narrow tunnels)","Sec. 4; Sec. 3.1",[1396,53,29,16670,3816],"in the CatPack, mounted 45 deg off horizontal and spun about the pack's vertical axis, giving an effective 120 deg vertical field of view",[3818,53,29,16672,1667],"16 channels, 2.0 deg vertical resolution, 30 deg vertical FOV, +-0.03 m accuracy, range up to 100 m (used in both MLS)",[752,53,29,16674,16675],"hardware GNSS sync interface (PPS plus NMEA GPRMC); true packet period 1.328 ms and 1 us timestamp resolution per documentation; default 10 rotations per second","Sec. II, Sec. IV, Fig. 5",[6800,53,29,16677,332],"3D LiDAR for model-based ICP localization and obstacle updates",[5348,23,7967,16679,7969],"3D LiDAR; no specifications given",[5348,23,5349,16681,7928],"16 channels; range up to 100 m; accuracy ±3 cm; FoV 360° x 30°; angular resolution 0.1-0.4° horizontal, 2.0° vertical; 5-20 Hz; about 300,000 points\u002Fs single return, 600,000 dual return",[2528,53,29,16683,16684],"three units tested; 16 lasers aimed in 2 deg steps over 30 deg (-15 to +15 deg); 360 deg by 30 deg FOV; range up to 100 m; 3 cm range accuracy; 0.09 deg horizontal encoder resolution; 5 to 20 Hz rotation; 300 kHz; 8 W; 830 g; 7.2 cm by 10.3 cm; Class 1, 903 nm; IP-67","Sec. 1-3, Table 1, Sec. 4.1",[1145,23,1146,16686,332],"excluded because of self-occlusion by surrounding sensors",[1263,53,1445,16688,16689],"10 Hz; horizontal FOV reduced to 180 degrees in exp. 1, full 360 degrees in exp. 2; noise model sigma_p = 1 cm from datasheet","Sec. IV-B, IV-C, Fig. 2",[3821,53,29,16691,917],"rotating puck inside Hovermap, 360 deg field of view, 905 nm wavelength",[2169,23,16693,12324,3364],"liosam (LIO-SAM dataset)",[5280,23,2577,10035,16695],"Table I; Sec. III-C",[7586,23,16697,16698,16699],"Complex Urban LiDAR","two tilted units without 360 deg surround view; clouds merged into one scan","Sec. IV-A3",[4121,53,29,16701,16702],"LiDAR model used to generate simulated point clouds; observation range limited to 15 m in a 40 m wide environment","Sec. VI-A; Fig. 8",[2452,53,10666,16704,332],"16-beam 3D LiDAR, 10 Hz",[13470,53,29,16706,16707],"simulated as 16 channels (plus or minus 15 deg), 10 Hz, 240k points per second; FOV reduced to plus or minus 45 deg for the handheld rig; read with the snark driver","Sec. III-C, IV-A, IV-B, Fig. 8",[5254,53,29,16709,1072],"3D lidar",[2559,53,7914,16711,16712],"16 channels (plus or minus 15 deg), 10 Hz, 300k points per second, noise plus or minus 3 cm","Sec. VII; Fig. 1",[192,952,29,16714,16715],"mechanical LiDAR with a vertical channel-based repetitive scanning pattern","Fig. 2a",[5783,23,16717,569,2162],"NAVER LABS localization dataset",[6382,23,16719,16720,3256],"LeGO-LOAM VLP-16 sample data","sample data offered by LeGO-LOAM on GitHub",[14056,53,29,16722,16723],"simulated to match the real sensor: 10 Hz scans, 360 deg horizontal and +\u002F-15 deg vertical field of view; hardware synchronized with the IMUs","Sec. V, V-A, Fig. 1",[8457,53,8458,16725,16726],"3D LiDAR on the self-assembled vehicle","Sec. V-B; Fig. 5",[8457,23,14319,16728,16729],"3D LiDAR mounted on the vehicle at a tilt of about 45 degrees","Sec. V-B; Fig. 1 caption",[1155,23,12026,16731,1538],"on a Unitree Go1 quadruped",[1536,53,29,16733,332],"mounted upright for the training missions and upside down for the test mission; range image H = 16, W = 720, about 32,000 points per scan",[1549,53,29,16735,16736],"16 beams, 30 deg vertical field of view; its simulated model generated all training scans in Gazebo","Sec. V-A2, VI-B, VI-C",[1476,53,16738,16739,16740],"ANYmal parkour (authors)","LiDAR on ANYmal in the parkour experiment; its accumulated cloud meshed with HF offline poses","Sec. VI-C2; Fig. 18",[52,53,16742,16743,12411],"R-LOAM simulated datasets 1, 3, 5","16 scan lines, up to 30,000 points per scan, 10 Hz, zero-mean Gaussian noise sigma 0.03",[3742,53,29,16745,332],"10 Hz update rate; continuously rotated about the roll axis within +\u002F-40 deg",[1359,53,16205,16747,16748],"two units on Husky, one flat and one pitched forward 30 deg; scans recorded at 10 Hz; about 0.1 s per scan","Sec. III dataset description; footnote 1",[1359,53,29,16750,1361],"one unit on Spot",[2922,53,29,16752,332],"16-line LiDAR",[2493,53,29,16754,16755],"multi-beam; hand-held, legged-robot and wheeled-robot payloads","VoR Sec. VII-A; Fig. 9b-d",[2221,23,2426,16757,1104],"ATV, trail road",[16392,23,16759,16760,16761],"NSH indoor outdoor (example rosbag linked in README)","16 scan lines (scan_line 16), minimum_range 0.3 m in launch file","README Sec. 3; launch\u002Faloam_velodyne_VLP_16.launch",[2391,53,29,16763,16764],"fixed on top of a car with the IMU (port test)","Sec. IV-B1; Fig. 1",[668,53,16766,16767,16768],"DARPA SubT Final Event (Team CSIRO Data61); QCAT (SpinningPack)","measurement rate 20 Hz; mounted at an inclined angle on a servomotor spinning about the sensor z axis at 0.5 Hz, giving 120 deg vertical FoV (SpinningPack)","Sec. VI-A1, VI-A3; Table I",[668,53,4367,16770,16771],"fixed, vertical FoV 30 deg (FlatPack)","Sec. VI-A3; Table I",[2510,53,2511,16773,10028],"three per Husky, extrinsically calibrated: one flat, one pitched forward 30 deg, one pitched backward 30 deg; 10 Hz (two used where marked in Table I)",[2680,53,29,16775,16776],"16 channels; range up to 100 m, accuracy +\u002F-3 cm; vertical FOV 30 deg (+\u002F-15 deg), 2 deg vertical resolution; 360 deg horizontal FOV, 0.1 to 0.4 deg horizontal resolution; scan rate set to 10 Hz (0.2 deg); projected to a 1800 x 16 range image","Sec. II; Sec. III-B",[2758,53,29,16778,13311],"10 Hz rotation rate in the runtime discussion",[2785,53,29,569,898],[1296,53,29,16781,7950],"16 channels, 100 m, up to +-3 cm, vertical FoV 30 deg (2.0 deg resolution), 360 deg horizontal, 5-20 Hz; recorded at 10 Hz",[1296,23,1280,16783,1374],"handheld ConSLAM rig",[3761,952,29,16785,16786],"300,000 pts\u002Fs, 100 m, 30 mm at 100 m; actuated by a servomotor on a 3D printed handheld frame; ROS, PCL and GTSAM; no IMU","Sec. 2.3; Table 1; Fig. 2a",[1279,23,1280,16788,16789],"16 beams, about 10 Hz, up to about 30 000 points per message, range limited to 60 m in the driver","Sensors and devices; Intrinsic calibrations; Data collection system",[1553,53,29,16791,16792],"16-beam (sparse) LiDAR on ANYmal-C; used in all field experiments","Sec. VII-A, VII-G3",[1520,53,29,16794,16795],"partially blocked by the robot payload in the forest run (Sec. VI-A)","Sec. V, V-A-3, V-B, VI-A",[4780,53,13756,16797,917],"simulated 3D LiDAR on the SUMMIT XL in Gazebo",[1183,53,29,16799,16800],"on the warehouse AGV; intensity rescaled to [0, 1]","Sec. III-A; Sec. IV-A",[1850,23,12839,16802,12841],"Gazebo sensor model",[1850,53,29,569,1969],[1462,53,29,16805,1104],"10 Hz, resolution 16 px x 1824 px",[2423,23,2426,16807,16808],"multi-line spinning LiDAR; images 480 x 300; used for the tracking evaluation","Sec. 4.1; Supp. 7.4",[3133,23,16810,569,16811],"LINS dataset (seaport)","Sec. IV-D; Fig. 7",[378,23,10445,16813,9835],"16 lines, 10 Hz",[4789,53,29,16815,16816],"16 lines, 10 Hz, mounted above the IMU in the handheld sensor pair","Sec. VII-A; Fig. 4a",[8932,53,29,16818,16819],"single 3D LiDAR, no IMU or odometry used; operated at 10 Hz","Sec. 4.1, Fig. 5, Sec. 4.3",[4348,53,4349,16821,4351],"16-beam LiDAR at 10 Hz",[2905,53,29,16823,16824],"360 deg horizontal and 30 deg vertical FoV, 0.3 million points per second at 5 Hz spinning rate","Sec. 10.1, Fig. 16b",[125,23,16826,16827,16828],"Semi-indoor (authors' custom)","one sparse 16-channel LiDAR; dynamic objects moving close to static structure","Sec. I, Fig. 7",[2429,23,16830,16831,917],"BIM-robot simulation benchmark","simulated sensor on a mobile robot in the Gazebo BIM-robot simulator",[3692,53,29,569,3694],[1371,23,1372,16834,3908],"10 Hz, 360 x 30 deg FOV; synchronized with CPU clock by PPS",[6069,53,6070,16836,16837],"on the NavVis M6; the only data used by the evaluated algorithms; scan period 100 ms","Sec. VIII-A; Sec. VIII-B",[1720,952,1721,16839,332],"LiDAR on the robot used by the FastLIO-SLAM baseline and for the sequence-3 reference",[2080,53,2081,569,2083],[4730,53,29,16842,4732],"10 Hz (Table IV)",[3843,952,29,16844,1212],"16 channels, standard deviation of distance measurement 3 cm; sensor inside the GeoSLAM ZEB Horizon RT",{"c":108,"m":16846,"d":20,"f":16847,"v":16848,"n":2256,"y":49,"u":16853},"Velodyne VLP-32C",[221],[16849,16850,16851,16852],"VLP-32C (two sensors)","VLP-32C (two units)","Velodyne VLP-32","Velodyne VLP32C",[16854,16856,16859,16861,16863],[1904,53,29,569,16855],"Sec. 3.1, Fig. 2",[114,23,16857,16858,332],"Argoverse 2 big city","urban driving data with various dynamic objects",[1976,23,1977,16860,16385],"on a backpack mapping system; 3 sequences, 35k frames with HDL32E, indoor",[4756,23,4757,16862,478],"360 x 40 deg FOV, 10 Hz",[125,23,16864,16865,13463],"Argoverse 2.0 big city","car-mounted, big-city streets",{"c":108,"m":16867,"d":20,"f":16868,"v":16869,"n":23,"y":100,"u":16871},"Velodyne-32",[221],[16870],"Velodyne-32 (as written)",[16872],[5070,23,4757,16873,12438],"about 57,600 points per scan",{"c":108,"m":16875,"d":20,"f":16876,"v":16877,"n":23,"y":346,"u":16878},"Velodyne-64 LiDAR",[221],[],[16879],[2573,23,16880,16881,16882],"in-house U.S., R.A., B.D.","mounted on a car; five runs per region","Sec. 5.1 In-house Datasets",{"c":33,"m":16884,"d":46,"f":16885,"v":16886,"n":23,"y":49,"u":16887},"VersaVIS camera trigger board",[],[],[16888],[11143,53,29,16889,332],"time-synchronizes host, cameras and IMU; LiDAR-host offset assumed negligible",{"c":1689,"m":16891,"d":46,"f":16892,"v":16893,"n":23,"y":346,"u":16894},"VI sensor (EuRoC)",[],[],[16895],[4101,23,1054,16896,332],"synchronised 20 Hz stereo images and 200 Hz IMU",{"c":522,"m":16898,"d":46,"f":16899,"v":16900,"n":23,"y":233,"u":16901},"VI sensor with monocular camera and IMU (model not reported)",[],[],[16902],[3947,23,4940,16903,745],"time-synchronized",{"c":1689,"m":16905,"d":46,"f":16906,"v":16907,"n":23,"y":233,"u":16908},"VI sensor with stereo camera and IMU (model not reported)",[],[],[16909],[3947,23,4487,16910,469],"time-synchronized; only left-camera images used",{"c":1689,"m":16912,"d":46,"f":16913,"v":16914,"n":28,"y":1819,"u":16916},"VI-Sensor",[],[16915],"VI Sensor",[16917,16920,16922],[1063,53,29,16918,16919],"hand-held; IMU at 400 Hz; stereo images used by the stereo VIO; camera intrinsics and IMU-camera extrinsics estimated online","Sec. VII-A2",[3618,23,1079,16921,9283],"stereo images and inertial data; mounted on a micro aerial vehicle; 11 sequences, 19 min in three indoor environments",[1078,53,16923,16924,7539],"HKUST campus dataset","hand-held; 25 Hz images and 200 Hz IMU; 5.62 km HKUST campus loop, 1 h 34 min",{"c":522,"m":16912,"d":46,"f":16926,"v":16927,"n":23,"y":1819,"u":16928},[],[],[16929],[1067,53,29,16930,1069],"forward-looking; two embedded WVGA monochrome cameras, only the left camera used; 20 Hz",{"c":1689,"m":16932,"d":20,"f":16933,"v":16934,"n":23,"y":233,"u":16935},"VI-Sensor (Aptina MT9V034 camera sensors; left camera used)",[],[],[16936],[1058,53,1059,16937,15805],"global shutter, 752 x 480, horizontal FOV 98 deg, vertical FOV 73 deg, 20 Hz; camera and IMU synchronised by the VI-Sensor and triggered by the GNSS PPS",{"c":1689,"m":16939,"d":20,"f":16940,"v":16941,"n":23,"y":289,"u":16942},"VI-Sensor (two global-shutter wide-VGA 1\u002F3 inch imagers)",[],[],[16943],[1050,53,29,16944,332],"fronto-parallel stereo, lenses with 120 deg diagonal field of view, factory calibrated pinhole plus radial-tangential model, hardware time-synchronised to the IMU with mid-exposure triggering, 20 Hz; only one camera used",{"c":1689,"m":16946,"d":46,"f":16947,"v":16948,"n":23,"y":318,"u":16949},"VI-sensor (visual-inertial sensor of Nikolic et al. [36])",[],[],[16950],[3237,53,7886,16951,1069],"images used for DBoW2 loop closures and odometry in the indoor dataset",{"c":944,"m":16953,"d":20,"f":16954,"v":16955,"n":23,"y":233,"u":16956},"Viametris BMS3D-HD",[2178],[],[16957],[4224,952,29,16958,4689],"wearable, 2019, indoor and outdoor; FLIR Ladybug5+; 16-beam + 32-beam LiDAR; IMU and GPS; 2 cm relative accuracy (manufacturer)",{"c":944,"m":16960,"d":46,"f":16961,"v":16962,"n":23,"y":299,"u":16963},"Viametris i-MMS",[2178],[],[16964],[4141,952,29,16965,16966],"trolley; three Hokuyo line scanners on a sliding mount plus Ladybug spherical camera; online 2D SLAM after Garcia-Favrot and Parent (2009)","Sec. 2.1; Fig. 1",{"c":944,"m":16968,"d":20,"f":16969,"v":16970,"n":41,"y":1819,"u":16971},"Viametris iMS3D",[2178],[],[16972,16974],[9210,23,9211,16973,9213],"max range 80 m; 86 x 10^3 points\u002Fs; angular resolution 0.25 deg horizontal and 0.25 deg vertical; angular FOV 360 x 360 deg; relative accuracy 30 mm; absolute accuracy \u003C 1 cm (Table 2); TUB1: 33.6 x 10^6 points, mean spacing 0.005 m, timestamped trajectory, low clutter (Table 1)",[9220,23,9211,16975,691],"TUB1: 33.6 M points, mean spacing 0.5 cm, trajectory, low clutter, single storey; median point-model distance 3.4 cm",{"c":944,"m":16977,"d":20,"f":16978,"v":16979,"n":23,"y":318,"u":16980},"Viametris push-cart (footnote link: viametris.com\u002Fims3d)",[2178],[],[16981],[4124,23,16982,16983,16984],"Fire brigade #2 (SIMs3D)","push-cart; scanner precision e \u003C 0.04 m; 5.6 M points; could not scan the stairs","Table 1, Sec. 7.1",{"c":944,"m":16986,"d":20,"f":16987,"v":16988,"n":23,"y":233,"u":16989},"Viametris vMS3D",[2178],[],[16990],[4224,952,29,16991,5838],"vehicle-mounted, 2016, outdoor; FLIR Ladybug5+; Velodyne VLP-16 + Velodyne HDL-32E; IMU and GPS; 2-3 cm relative accuracy (manufacturer)",{"c":33,"m":16993,"d":46,"f":16994,"v":16995,"n":2256,"y":299,"u":16997},"Vicon",[16993],[16996],"VICON",[16998,17001,17004,17006,17007],[688,41,16999,17000,7969],"Valkyrie, HyQ","100 Hz, millimetre-accurate; Valkyrie and HyQ indoor experiments",[7393,41,15688,17002,17003],"motion capture ground truth for Kinect poses","Sec. 7, Fig. 5",[3211,41,1079,17005,147],"Vicon trajectory; the estimated trajectory is aligned to it to place the estimated cloud in the frame of the EuRoC 3D structure-scan ground truth",[1078,41,1079,9274,1081],[1083,41,1079,9274,1084],{"c":33,"m":17009,"d":46,"f":17010,"v":17011,"n":23,"y":1819,"u":17012},"Vicon fused with IMU",[16993],[],[17013],[1764,41,9483,17014,214],"pose information for EuRoC",{"c":33,"m":17016,"d":46,"f":17017,"v":17018,"n":23,"y":122,"u":17019},"Vicon motion capture",[16993],[],[17020],[1462,41,29,17021,17022],"200 Hz, used as velocity ground truth","Sec. VI-E, Sec. VII-D, Fig. 18",{"c":33,"m":17024,"d":46,"f":17025,"v":17026,"n":23,"y":233,"u":17027},"VICON motion capture (EuRoC ground truth)",[],[],[17028],[1201,41,14465,17029,671],"position and orientation ground truth of the EuRoC V1 and V2 sequences; also used to generate the simulated UWB ranges",{"c":33,"m":17031,"d":46,"f":17032,"v":17033,"n":1047,"y":270,"u":17035},"Vicon motion capture system",[16993],[17034],"VICON motion capture system",[17036,17038,17041,17044,17047,17049,17051,17054,17056],[9055,41,9056,17037,3518],"ground truth for robot trajectory and landmark positions",[1053,41,1054,17039,17040],"6D pose of a reflective-marker frame at 100 Hz (footnote links a Vicon Bonita camera page)","Table 1, Sec. 2, footnote 4",[10062,41,29,17042,17043],"tracked the sensor head and landmarks; 200 Hz timestamps used for rolling-shutter evaluation","Fig. 4 caption; Sec. 6.3.2; Fig. 13 caption",[7639,41,29,17045,17046],"covers the start and end area of V2-01","Sec. IV-C1; Fig. 6",[1657,41,29,17048,15206],"sub-millimetre accuracy, above 200 Hz; used as ground truth and for closed-loop control",[14056,41,29,17050,1072],"ten sequences of about 15 s used for trajectory ATE",[3460,41,184,17052,17053],"used to calibrate and verify extrinsics of flexibly mounted sensors with the drone at rest","Sec. 5, Sec. 5.3",[1764,41,9480,17055,214],"pose source for the cow dataset",[1850,41,29,17057,17058],"indoor room ground truth","Sec. IV-C3",{"c":33,"m":17060,"d":20,"f":17061,"v":17062,"n":23,"y":142,"u":17063},"Vicon motion capture system (14 cameras)",[16993],[],[17064],[467,41,29,17065,745],"millimeter position precision within about 2 x 2 m; about 1 deg tag orientation precision; 10 x 8 m room",{"c":33,"m":17067,"d":46,"f":17068,"v":17070,"n":23,"y":318,"u":17071},"VICON motion capture system (model not reported)",[17069],"Vicon (as named)",[],[17072],[4730,41,17073,569,1204],"Vicon Room sequences",{"c":33,"m":17075,"d":46,"f":17076,"v":17077,"n":23,"y":152,"u":17078},"Vicon motion capturing system",[16993],[],[17079],[1795,41,1796,17080,17081],"tracks passive markers on the rig; outliers removed manually and trajectory smoothed with an SE(3) cubic B-spline (20 knots per second); camera-Vicon calibration ATE RMSE required to be at most 1 mm","Sec. 5; Supp. Sec. 3, 4.4, 4.5, 4.7",{"c":33,"m":17083,"d":46,"f":17084,"v":17085,"n":23,"y":289,"u":17086},"Vicon motion tracking system",[16993],[],[17087],[1074,41,29,17088,17089],"6D ground truth at 200 Hz (Vicon Loops)","Sec. VII-A3, VII-B1; Table II",{"c":33,"m":17091,"d":46,"f":17092,"v":17093,"n":41,"y":299,"u":17094},"Vicon system",[],[],[17095,17097],[1067,41,29,17096,1069],"mounted in the room; hand-eye calibration to the camera by least squares",[302,41,29,17098,398],"ground-truth poses for the Kinect tracker (27 paths)",{"c":33,"m":17100,"d":46,"f":17101,"v":17102,"n":23,"y":49,"u":17103},"VICON system (motion capture)",[16993],[],[17104],[5237,41,29,17105,1157],"ground truth in a 4 m x 4 m room",{"c":33,"m":17107,"d":46,"f":17108,"v":17109,"n":23,"y":122,"u":17110},"Vicon Tracker (five Vicon markers on the sensor suite)",[16993],[],[17111],[2169,41,4316,17112,17113],"recorded at 300 Hz for bandwidth analysis","Sec. 5.2; Sec. 5.4",{"c":33,"m":17115,"d":20,"f":17116,"v":17117,"n":23,"y":132,"u":17118},"Vicon Vero 2.2",[16993],[],[17119],[2135,41,2136,17120,2691],"motion capture, max frame rate 330 Hz, accuracy 1 mm; 6-DoF ground truth on flat ground",{"c":108,"m":17122,"d":20,"f":17123,"v":17124,"n":23,"y":49,"u":17125},"virtual 320-beam LiDAR sensor model (Mai City ground truth)",[],[],[17126],[8131,41,16500,17127,8718],"samples the CAD model with 320 beams to give a 62.5-million-point ground-truth cloud of the observable surfaces",{"c":108,"m":17129,"d":20,"f":17130,"v":17131,"n":23,"y":49,"u":17132},"virtual 64-beam LiDAR sensor model (Mai City scans)",[],[],[17133],[8131,23,16500,17134,332],"synthetic scans of a CAD urban scene; a 64-beam sensor is implied by the statement that the ground-truth sampling used 320 beams instead of 64 (Sec. IV-A)",{"c":33,"m":17136,"d":46,"f":17137,"v":17138,"n":23,"y":152,"u":17139},"virtual cameras rendered in Blender",[],[],[17140],[1174,23,17141,17142,17143],"photo-realistic synthetic corridor dataset","sensor 32 mm x 24 mm; FOV 60, 90 or 120 deg; 320 x 240 to 1280 x 960; 30 FPS","Sec. 4.1.1; Table 1",{"c":108,"m":17145,"d":20,"f":17146,"v":17147,"n":23,"y":132,"u":17148},"virtual Velodyne HDL-64 LiDAR",[221],[],[17149],[5523,23,16500,17150,12375],"synthetic urban-like scans (Mai City)",{"c":1689,"m":17152,"d":46,"f":17153,"v":17154,"n":23,"y":346,"u":17155},"Visual-Inertial (VI-)sensor [28]",[],[],[17156],[3264,53,29,17157,294],"tightly synchronized stereo images from a pair of global shutter cameras; used for camera tracking at 10 Hz",{"c":33,"m":17159,"d":46,"f":17160,"v":17161,"n":23,"y":1421,"u":17162},"visual-inertial sensor unit (VI-Sensor, Nikolic et al. 2014)",[],[],[17163],[1053,23,1054,17164,17165],"stereo cameras and IMU hardware time-synchronized so that mid-exposure aligns with IMU measurements; calibrated with Kalibr","Sec. 2, Sec. 5.1",{"c":522,"m":17167,"d":20,"f":17168,"v":17169,"n":23,"y":299,"u":17170},"VMX-250-CS6",[13545],[],[17171],[1606,952,29,17172,1608],"up to six cameras; typical configuration four 5 Mpx cameras at up to 4 fps",{"c":662,"m":17174,"d":20,"f":17175,"v":17176,"n":28,"y":233,"u":17178},"VN100",[16201],[17177],"VN-100",[17179,17181,17182,17184],[1510,23,184,17180,1104],"adopted IMU",[1510,23,9911,17180,1104],[164,53,184,17183,167],"385 Hz",[1201,53,2519,17185,12126],"IMU on the flight platform (VoR footnote)",{"c":522,"m":17187,"d":46,"f":17188,"v":17189,"n":23,"y":152,"u":17190},"webcam (monocular; model not reported)",[],[],[17191],[3311,53,29,17192,15949],"1,920 x 1,080 video at 30 fps; fixed focal length; intrinsics from MATLAB calibration; keyframes downsampled to 640 x 360 for real time",{"c":353,"m":17194,"d":46,"f":17195,"v":17196,"n":23,"y":152,"u":17197},"wheel encoder (model not stated)",[],[],[17198],[6316,53,29,17199,1072],"mixed with the IMU for odometry",{"c":353,"m":17201,"d":46,"f":17202,"v":17203,"n":23,"y":49,"u":17204},"wheel encoder odometry",[],[],[17205],[3106,53,29,17206,127],"odometry factors in the moving-horizon graph",{"c":353,"m":17208,"d":46,"f":17209,"v":17210,"n":28,"y":152,"u":17211},"wheel encoders",[],[],[17212,17216,17219],[282,53,17213,17214,17215],"fr079 and Malaga CS faculty","incremental odometry used by kinematic state prediction in the 2D configuration","Sec. 3.8; Sec. 5.1",[6800,53,29,17217,17218],"differential drive kinematic model; low confidence while wheels turn","Sec. III-A2, Sec. IV-A",[236,53,29,17220,332],"one encoder per wheel monitoring rotation speed, 30 Hz",{"c":353,"m":17222,"d":46,"f":17223,"v":17224,"n":23,"y":100,"u":17225},"wheel encoders (not used)",[],[],[17226],[599,23,600,569,4427],{"c":353,"m":17228,"d":46,"f":17229,"v":17230,"n":23,"y":455,"u":17231},"wheel encoders (robot odometry)",[],[],[17232],[1000,53,17233,17234,17235],"Kvarntorp-Loop, Mission-4","initial pose errors up to about 1.5 m and 0.2 rad per step (Kvarntorp-Loop), up to 1.4 rad (Mission-4 Scan 33)","Sec. 6.4.3",{"c":353,"m":17237,"d":46,"f":17238,"v":17239,"n":23,"y":152,"u":17240},"wheel encoders and IMU fused by EKF (WheelIMU)",[],[],[17241],[2555,23,13022,17242,3230],"odometry already recorded in the ROS bags",{"c":353,"m":17244,"d":46,"f":17245,"v":17246,"n":23,"y":346,"u":17247},"wheel encoders on four wheels",[],[],[17248],[2316,53,29,569,917],{"c":353,"m":17250,"d":46,"f":17251,"v":17252,"n":23,"y":49,"u":17253},"wheel odometry (encoders, model not stated)",[],[],[17254],[627,952,29,17255,17256],"fused with the IMU in an EKF for the Wheel-Inertial baseline and as HeRO fallback","Sec. 3.1; Sec. 3.2",{"c":353,"m":17258,"d":46,"f":17259,"v":17260,"n":23,"y":270,"u":17261},"wheel odometry (robot-oriented longitudinal and rotational speed)",[],[],[17262],[9055,23,9056,4933,9058],{"c":353,"m":17264,"d":46,"f":17265,"v":17266,"n":23,"y":132,"u":17267},"wheel odometry (source not detailed)",[],[],[17268],[2447,23,2448,17269,11008],"mentioned as fused with IMU for pose estimation",{"c":353,"m":17271,"d":46,"f":17272,"v":17273,"n":23,"y":49,"u":17274},"wheel-inertial odometry (sensor models not reported)",[],[],[17275],[4290,41,29,17276,17277],"reference for lidar slip in a carpeted office corridor at low speed; wheel slippage stated to be negligible","Sec. 3.2, Fig. 6",{"c":353,"m":17279,"d":46,"f":17280,"v":17281,"n":23,"y":49,"u":17282},"wheel-inertial odometry (WIO) of the Husky",[],[],[17283],[1359,53,16205,17284,16207],"recorded at 50 Hz in the Urban datasets",{"c":372,"m":17286,"d":46,"f":17287,"v":17288,"n":23,"y":233,"u":17289},"wheeled ground robot (model not stated)",[],[],[17290],[2493,53,29,17291,17292],"dataset 7 min, 38 x 49 m, industrial area","VoR Sec. VII-A; Fig. 11b",{"c":353,"m":17294,"d":46,"f":17295,"v":17296,"n":23,"y":233,"u":17297},"wheeled inertial odometry (WIO) on Husky",[],[],[17298],[2510,53,2511,17299,332],"recorded at 50 Hz",{"c":372,"m":17301,"d":46,"f":17302,"v":17303,"n":23,"y":100,"u":17304},"wheeled mobile robot",[],[],[17305],[567,23,4757,17306,1538],"university campus in Shanghai",{"c":372,"m":17308,"d":46,"f":17309,"v":17310,"n":23,"y":142,"u":17311},"wheeled pushcart",[],[],[17312],[467,53,29,17313,14351],"carries the spinning SICK reference and power and logging for the first-generation handheld",{"c":372,"m":17315,"d":46,"f":17316,"v":17317,"n":23,"y":132,"u":17318},"wheeled robot",[],[],[17319],[2423,23,2426,17320,917],"traverses a botanic garden",{"c":522,"m":17322,"d":46,"f":17323,"v":17324,"n":23,"y":1819,"u":17325},"wide fisheye camera (model not reported)",[],[],[17326],[3618,53,3619,17327,3621],"same circle trajectory flown again with a fisheye lens",{"c":522,"m":17329,"d":46,"f":17330,"v":17331,"n":23,"y":346,"u":17332},"wide-angle camera (model not_reported)",[],[],[17333],[2905,53,29,17334,2907],"640 x 512 px, used for motion estimation (Contour)",{"c":522,"m":17336,"d":46,"f":17337,"v":17338,"n":23,"y":152,"u":17339},"wide-angle greyscale camera on the rig (model not reported)",[],[],[17340],[3089,23,3054,17341,898],"video used with IMU data by the in-house SLAM system for 6 DoF poses",{"c":33,"m":17343,"d":46,"f":17344,"v":17345,"n":23,"y":346,"u":17346},"Wifi enabled router",[],[],[17347],[2739,53,29,17348,1924],"mounted on the Husky; connects the four Jetson boards to one network",{"c":18,"m":17350,"d":20,"f":17351,"v":17352,"n":23,"y":1008,"u":17353},"Windows laptop with Intel i7-13700H CPU and NVIDIA RTX 3050 GPU",[3317],[],[17354],[2018,28,29,17355,17356],"used for centerline estimation and sampling","Sec. 3.2; Table A1",{"c":18,"m":17358,"d":20,"f":17359,"v":17360,"n":23,"y":152,"u":17361},"workstation with an Intel i7-7700",[98],[],[17362],[5455,28,29,17363,214],"all mapping on CPU",{"c":18,"m":17365,"d":20,"f":17366,"v":17367,"n":23,"y":289,"u":17368},"workstation with Intel Core i7-3770 3.5GHz CPU and 16GB of RAM",[98],[],[17369],[17370,28,29,17371,17372],"choi2015robustrecon","all pipeline steps; registration timings single-threaded","Table 1 caption; supplementary Table 1",{"c":18,"m":17374,"d":20,"f":17375,"v":17376,"n":23,"y":1819,"u":17377},"workstation with Intel Xeon E5 @ 2.4GHz",[98],[],[17378],[17379,28,29,17380,3364],"psmslam2017","multi-threaded CPU implementation built on the open-source DVO SLAM; GPU acceleration is mentioned only as a possible improvement (Sec. 6)",{"c":18,"m":17382,"d":20,"f":17383,"v":17384,"n":23,"y":346,"u":17385},"workstation with Intel(R) Core(TM) i7-3770 CPU at 3.40GHz and GeForce GTX 1070 GPU",[3260],[],[17386],[5219,28,29,17387,4496],"Ubuntu 16.04; all compared methods run on it",{"c":651,"m":17389,"d":20,"f":17390,"v":17392,"n":23,"y":122,"u":17393},"X1-5H",[17391],"Bynav",[],[17394],[1720,41,1721,17395,332],"GNSS\u002FINS reference in open-sky areas; about 3 cm with RTK, 25 cm after a 10 s RTK outage",{"c":18,"m":17397,"d":20,"f":17398,"v":17399,"n":23,"y":49,"u":17400},"Xeon Platinum 8259CL CPU at 2.50GHz (server)",[],[],[17401],[6413,28,29,17402,17403],"server CPU; 12 threads allocated per algorithm; used for the 3DMatch scan-matching tests","Sec. XI-E",{"c":944,"m":17405,"d":46,"f":17406,"v":17408,"n":23,"y":100,"u":17409},"XGrids hand-held LiDAR mapping device (model not reported)",[17407],"XGrids",[],[17410],[1363,53,17411,17412,17413],"XGrid-Outdoor, XGrid-Parking","commercial hand-held LiDAR mapping device; session poses retrieved from XGrids proprietary software","Abstract; Sec. IV; Sec. IV-B",{"c":522,"m":17415,"d":20,"f":17416,"v":17418,"n":23,"y":152,"u":17419},"Ximea XiQ MQ013xG-E2",[17417],"Ximea",[],[17420],[3036,53,29,17421,4480],"USB 3.0, 1280 x 1024, up to 60 fps, 26 g; mounted vertically; triggered at 1 Hz",{"c":662,"m":17423,"d":46,"f":17424,"v":17426,"n":28,"y":299,"u":17428},"Xsens IMU (model not reported)",[15270,17425],"Xsens (as named)",[17427],"Xsens IMU (model not_reported)",[17429,17432,17433],[2604,23,17430,2377,17431],"Cheddar Gorge dataset","Sec. 8.2",[3692,53,29,569,1084],[4730,53,29,17434,4732],"400 Hz (Table IV)",{"c":662,"m":17436,"d":20,"f":17437,"v":17438,"n":2256,"y":270,"u":17439},"Xsens MTi-10",[15270],[],[17440,17441,17444,17446,17448],[2452,23,2577,2219,6265],[1938,23,17442,17443,1416],"UrbanLoco, UrbanNav","nine-axis, 100 Hz",[378,23,16399,17445,9835],"9-axis, 100 Hz",[2630,53,29,17447,5221],"optional; orientation from a Kalman filter on gyro and accelerometer used to preprocess the point cloud",[5093,53,29,17449,275],"orientation from gyros and accelerometers fused in a Kalman filter; used to pre-process the point cloud",{"c":662,"m":17451,"d":20,"f":17452,"v":17453,"n":2212,"y":152,"u":17456},"Xsens MTi-100",[15270],[17454,17455],"MTi-100","Xsens MTi-100 series (three units)",[17457,17459,17462,17464,17465,17467],[688,53,1454,17458,691],"400 Hz; initial bias 0.2 deg\u002Fs and 5 mg; bias stability 10 deg\u002Fh and 15 mg",[6800,53,29,17460,17461],"process constraint in the MHE and latency compensation","Sec. IV-A, Sec. III-A3",[14056,53,29,17463,16723],"simulated to match the real sensors at 400 Hz",[1155,53,29,5790,4103],[1462,53,29,17466,1104],"400 Hz; initial bias 0.2 deg\u002Fs and 5 mg; bias stability 10 deg\u002Fh and 15 mg (ANYmal B300: SMR, FSC, SUB)",[4789,53,29,2219,2153],{"c":662,"m":17469,"d":20,"f":17470,"v":17471,"n":23,"y":346,"u":17472},"Xsens MTi-20",[15270],[],[17473],[2905,53,29,17474,2907],"on Contour",{"c":662,"m":17476,"d":20,"f":17477,"v":17478,"n":23,"y":233,"u":17480},"Xsens MTi-28",[15270],[17479],"Xsens MTi-28A53G25",[17481],[378,23,16396,8163,9835],{"c":662,"m":17483,"d":20,"f":17484,"v":17485,"n":28,"y":346,"u":17488},"Xsens MTi-3",[15270],[17486,17487],"MTi3 Xsens IMU","Xsens MTi3",[17489,17492,17493],[13470,53,29,17490,17491],"low-cost IMU; simulated at 100 Hz; read with the ROS Xsens driver","Sec. IV-A, IV-B, V, Fig. 8",[5254,53,29,569,1072],[2559,53,7914,17494,17495],"100 Hz; noise 0.02 m\u002Fs2 and 0.097 deg\u002Fs; low cost","Sec. VII; Sec. VII-E",{"c":662,"m":17497,"d":20,"f":17498,"v":17499,"n":2256,"y":346,"u":17503},"Xsens MTi-30",[15270],[17500,17501,17502],"Xsens MTi-30 AHRS","Xsens-MTI-30","Xsens-MTI-30 (as written)",[17504,17505,17508,17511,17512],[6316,53,29,569,1072],[1011,53,29,17506,17507],"72 g; 400 Hz; xyz acceleration, xyz angular velocity, absolute heading, east-north-up orientation; sync output read by the microcontroller","Table 1; Sec. 4.1, 4.3",[2135,23,2136,17509,17510],"100 Hz; gyro noise density 0.03 deg\u002Fs\u002Fsqrt(Hz); accel noise density 60 ug\u002Fsqrt(Hz); mag RMS noise 0.5 mGauss; shared by all three devices","Table 2, Sec. 3.1",[2173,23,2577,2219,2174],[2905,53,29,1272,15912],{"c":662,"m":17514,"d":20,"f":17515,"v":17516,"n":2212,"y":152,"u":17519},"Xsens MTi-300",[15270],[17517,17518],"MTi-300","Xsens MTi-300 AHRS IMU",[17520,17521,17522,17523,17524,17525],[2452,53,10666,2219,332],[225,53,226,2377,228],[8457,23,14319,569,9145],[1155,23,1146,2377,332],[2221,23,1146,2549,1104],[2080,53,2081,569,2083],{"c":662,"m":17527,"d":20,"f":17528,"v":17529,"n":41,"y":122,"u":17530},"Xsens MTi-610",[15270],[],[17531,17533],[1296,23,1280,17532,1298],"handheld ConSLAM rig; sequence 1 IMU data faulty",[1279,23,1280,17534,17535],"9-axis (gyroscope, accelerometer, magnetometer), recorded at 400 Hz","Sensors and devices; Data collection system",{"c":662,"m":17537,"d":20,"f":17538,"v":17539,"n":952,"y":49,"u":17541},"Xsens MTi-670",[15270],[17540],"XSens MTi-670",[17542,17545,17548,17550],[1938,53,29,17543,17544],"gyroscope and accelerometer readings, e.g. 200 Hz; Livox Horizon + Xsens suite cost about 1700 EUR (Q1 2020)","Sec. 2; Sec. 5.3",[9592,23,9593,17546,17547],"triggered via the PTP-to-trigger board; noise densities and random walks estimated by Allan variance from an about 11.5 h standstill recording at 200 Hz with LiDAR and robot fans running","Sec. I, Sec. III, Sec. III-A",[378,23,9833,17549,9835],"6-axis, 200 Hz",[216,23,3780,1272,157],{"c":662,"m":17552,"d":20,"f":17553,"v":17554,"n":23,"y":132,"u":17556},"Xsens MTi-680",[15270],[17555],"Xsens Mti-680G",[17557],[2221,23,2426,17558,1104],"ATV",{"c":662,"m":17560,"d":20,"f":17561,"v":17562,"n":952,"y":299,"u":17565},"Xsens MTI-G",[15270],[17563,17564],"X-sens MTI-G","Xsens MTi-G",[17566,17567,17568,17570],[4865,53,14310,569,14312],[2604,53,29,2377,1788],[339,53,29,17569,257],"inertial measurements at 100 Hz",[861,53,29,17571,530],"GPS-aided IMU; fused with odometry in a Kalman filter for pre-alignment",{"c":662,"m":17573,"d":20,"f":17574,"v":17575,"n":23,"y":49,"u":17576},"Xsens MTi-G-700",[15270],[],[17577],[1938,952,10596,17578,17579],"six-axis, 150 Hz, synchronized with the HDL-64E","Sec. 5.3.1",{"c":662,"m":17581,"d":20,"f":17582,"v":17583,"n":41,"y":318,"u":17585},"Xsens MTi-G-710",[15270],[17584],"Xsens MTiG-710",[17586,17588],[2391,53,29,17587,16764],"used as a 6-axis IMU; fixed on top of a car (port test)",[3133,23,16810,569,16811],{"c":662,"m":17590,"d":20,"f":17591,"v":17592,"n":23,"y":233,"u":17593},"Xsens-300",[15270],[],[17594],[4511,23,8773,17595,478],"on the backpack; use by the method not stated",{"c":662,"m":17597,"d":20,"f":17598,"v":17599,"n":23,"y":49,"u":17600},"Xsens-300 IMU (as written in Sec. V-B)",[15270],[],[17601],[8457,53,8458,569,1072],{"c":1776,"m":17603,"d":46,"f":17604,"v":17605,"n":23,"y":270,"u":17606},"Xtion Pro Live",[],[],[17607],[3063,53,3064,17608,17609],"RGB and depth at 30 Hz, 640 x 480, 58 deg horizontal FoV","Sec. III; Fig. 2(a)",{"c":2807,"m":17611,"d":20,"f":17612,"v":17613,"n":23,"y":318,"u":17614},"Z + F Imager 5006i",[6477],[],[17615],[771,23,4138,569,4139],{"c":2807,"m":17617,"d":20,"f":17618,"v":17619,"n":23,"y":318,"u":17620},"Z + F IMAGER 5010C",[6477],[],[17621],[771,23,17622,17623,17624],"WHU-TLS (subway station)","integrates a camera and laser scanner","Table 2; Sec. 4.1",{"c":944,"m":17626,"d":20,"f":17627,"v":17628,"n":23,"y":132,"u":17629},"Z+F FlexScan 22",[6477],[],[17630],[2071,53,29,17631,17632],"uses a high-precision Z+F Imager 5016 TLS scanner for acquisition; larger and heavier, allows customised post-processing","Sec. 1, 4.1, 5.1",{"c":2807,"m":17634,"d":20,"f":17635,"v":17636,"n":23,"y":122,"u":17637},"Z+F Imager 5016",[6477],[],[17638],[1252,41,1253,17639,17640],"range up to 360 m, up to 1 million points\u002Fs, FoV 360 x 320 deg, angular accuracy 14.4 arcsec, linearity error \u003C= 1 mm + 10 ppm\u002Fm, sub-mm ranging noise, integrated HDR camera; registration with reflective targets, plane-to-plane matching and block adjustment; 91% (Sheldonian) and 95% (construction site) of scans within 3 mm position uncertainty","Sec. V-A; Figs. 5-6",{"c":662,"m":17642,"d":46,"f":17643,"v":17644,"n":23,"y":346,"u":17645},"ZEB inertial measurement unit (model not reported)",[],[],[17646],[2795,53,29,17647,6150],"triaxial gyros, accelerometers and three-axis magnetometers",{"c":944,"m":17649,"d":46,"f":17650,"v":17651,"n":23,"y":1819,"u":17652},"ZEB REVO (written 'Zeb-Revo' in text)",[],[],[17653],[9210,23,9211,17654,9213],"max range 30 m; 43 x 10^3 points\u002Fs; 0.625 deg horizontal, 1.8 deg vertical; FOV 270 x 360 deg; relative accuracy 2 to 3 cm; absolute accuracy 3 to 30 cm (Table 2); TUB2: 21.6 x 10^6 points, 0.008 m spacing, trajectory, two floors (Table 1)",{"c":944,"m":17656,"d":46,"f":17657,"v":17658,"n":28,"y":318,"u":17661},"Zeb Revo RT",[4651],[17659,17660],"ZEB-REVO RT","ZEB-REVO-RT",[17662,17665,17668],[9220,23,9211,17663,17664],"Grainger Museum: 28.9 M points, 2.9 cm spacing, trajectory, high clutter, non-Manhattan with curved walls; median point-model distance 2.8 cm","Table 1; Sec. 5.6",[951,952,29,17666,17667],"handheld; 2D LiDAR; Zeb-Revo with touchscreen for real-time view; 1 kg; 43,200 pts\u002Fs; relative accuracy 2-3 cm; 1.5 h; GoPro Session camera","Sec. 2.1.1; Tables 1-4, 6; Fig. 1c",[4161,952,29,17669,9547],"handheld; 850 g without battery; 43,200 points\u002Fs; FOV 270 deg H, 360 deg V (Table 1; text says 270 deg in both planes); indoor range 0.6-30 m, outdoor 15-20 m; resolution 0.625 deg H, 1.8 deg V; relative accuracy 2-3 cm; absolute position accuracy 3-30 cm; 2D infrared laser profilometer with rotating head plus IMU, no GNSS, no camera",{"c":944,"m":17671,"d":46,"f":17672,"v":17673,"n":23,"y":318,"u":17674},"ZEB-HORIZON",[4651],[],[17675],[951,952,29,17676,17677],"handheld; mapping sensor classed as 2D LiDAR; weight N\u002FA; 3,000,000 pts\u002Fs; relative accuracy 1-3 cm; operating time 3.5 h; GoPro Session camera (datasheet values)","Sec. 2.1.1; Tables 1-4, 6; Fig. 1a",{"c":944,"m":17679,"d":46,"f":17680,"v":17681,"n":952,"y":346,"u":17684},"ZEB-REVO",[4651],[17682,17683],"Zeb Revo","ZebRevo",[17685,17687,17690,17693],[9220,23,9211,17686,691],"TUB2: 21.6 M points, 0.8 cm spacing, trajectory, low clutter, multi-storey; median point-model distance 2.8 cm",[4124,23,17688,17689,691],"TU Delft (SIMs3D)","handheld; scanner precision e \u003C 0.04 m; 3.2 M points",[951,952,29,17691,17692],"handheld; rotating 2D LiDAR; 1 kg; 43,200 pts\u002Fs; relative accuracy 2-3 cm; 4 h; GoPro Session camera","Sec. 2.1.1; Tables 1-4, 6; Fig. 1b",[2795,952,29,17694,17695],"automatically rotating head; 905 nm Class 1; 100 Hz, 100 lines\u002Fs; scan speed x2.5; maximum range 15-30 m; about 43,200 points\u002Fs; 270 deg HFOV, 100 deg VFOV; about 2 kg","Table 1, Fig. 1",{"c":944,"m":17697,"d":20,"f":17698,"v":17700,"n":2256,"y":299,"u":17702},"ZEB1",[17699,4651],"3D Laser Mapping (developed by CSIRO)",[17701],"Zeb1",[17703,17705,17708,17711,17714],[9220,23,9211,17704,691],"UoM: 13.9 M points, 0.7 cm spacing, no trajectory, moderate clutter; median point-model distance 3.3 cm",[4669,53,29,17706,17707],"spring-mounted 2D laser, data logger and IMU; typical range 15-20 m; 43,200 points\u002Fs; noise +-30 mm; horizontal FOV 270 deg; swept vertical FOV about 120 deg; post-processing on cloud servers","Sec. 2, Fig. 1",[4124,23,17709,17710,691],"Fire brigade #1 (SIMs3D)","handheld; scanner precision e \u003C 0.06 m; 7.4 M points",[2795,952,29,17712,17713],"spring-mounted sensor head; 905 nm Class 1 laser; 40 Hz, 40 lines\u002Fs; maximum range 15-30 m; about 43,200 points\u002Fs; declared 3D accuracy +-0.1%; about 1.5 kg head plus data logger","Table 1; Sec. ZEB system operational behaviour",[4141,952,29,17715,17716],"handheld post and spring with Hokuyo line scanner and IMU; gently oscillated by the operator; online 6-DoF SLAM (Bosse et al. 2012), open loop","Sec. 2.2; Fig. 2",{"c":944,"m":17718,"d":20,"f":17719,"v":17720,"n":23,"y":1819,"u":17721},"ZEB1 (written 'Zeb-1' in text)",[],[],[17722],[9210,23,9211,17723,9213],"max range 30 m; 43 x 10^3 points\u002Fs; 0.25 deg horizontal, 3.5 deg vertical; FOV 270 x 150 deg; relative accuracy 2 to 3 cm; absolute accuracy 3 to 40 cm (Table 2); UoM: 13.9 x 10^6 points, 0.007 m spacing, no trajectory (Table 1)",{"c":944,"m":17725,"d":20,"f":17726,"v":17727,"n":23,"y":152,"u":17728},"Zebedee (Zeb1) spring-mounted hand-held scanner",[],[],[17729],[1174,53,29,17730,917],"point cloud of the corridor used to model the BIM manually (prior map source); reported absolute accuracy 3 to 40 cm",{"c":944,"m":17732,"d":46,"f":17733,"v":17735,"n":23,"y":142,"u":17736},"Zebedee handheld, first generation",[17734],"authors' own design",[],[17737],[467,53,29,17738,17739],"laser and IMU in a 150 g 3D-printed housing on a single spring (50 to 150 mm, 5 to 20 g); total mass well under 0.5 kg; tethered to a pushcart for power and logging in early tests","Sec. II; Sec. IV; Fig. 2(a), 2(b)",{"c":944,"m":17741,"d":46,"f":17742,"v":17743,"n":23,"y":142,"u":17744},"Zebedee handheld, second generation",[17734],[],[17745],[467,53,29,17746,17747],"MicroStrain 3DM-GX3 IMU mounted on the back of the laser and a spring with slightly different physical characteristics; design developed with assistance from CMD Product Design and Innovation; used for the stairwell oscillation-stoppage experiment","Sec. IV-D; Fig. 2(d); Acknowledgment",{"c":1689,"m":17749,"d":20,"f":17750,"v":17751,"n":23,"y":318,"u":17752},"ZED",[],[],[17753],[2248,53,29,17754,17755],"forward-looking on a pan-tilt unit; RGB left and right images with per-pixel depth via ZED SDK and ZED ROS wrapper","Sec. 3.1, 4.1, 5.1.1",{"c":1689,"m":17757,"d":20,"f":17758,"v":17759,"n":23,"y":100,"u":17760},"ZED 2",[],[],[17761],[7209,53,29,17762,17763],"hand-held stereo camera used to collect outdoor unbounded scenes","Sec. 4.1; Sec. 4.2 On Stereo; Fig. 9",{"c":662,"m":17765,"d":20,"f":17766,"v":17767,"n":23,"y":100,"u":17768},"ZED 2i camera built-in IMU (written 'built-in Next-Gen IMU')",[],[],[17769],[4756,53,9801,17770,9803],"IMU with gyroscope, accelerometer, barometer and magnetometer; 500 Hz; synchronized with the LiDAR by IEEE 1588-2008",{"c":651,"m":17772,"d":20,"f":17773,"v":17774,"n":23,"y":233,"u":17775},"ZED-F9P",[],[],[17776],[1562,23,1563,17777,17778],"RTK-GPS at 10 Hz; 4 concurrent GNSS; L1\u002FL2\u002FL5 RTK; mounted on top of the LiDAR; PPS drives FPGA synchronization","Sec. III-A5; Table II",{"c":651,"m":17780,"d":20,"f":17781,"v":17782,"n":23,"y":132,"u":17783},"ZED-F9P GNSS sensor",[],[],[17784],[3944,23,1059,17785,3280],"raw measurements and RTK solutions provided by the GVINS dataset; SPP computed with RTKLIB",[17787,17789,17791,17793,17795,17797,17799,17801,17803,17805,17807,17809,17811,17813,17815,17817,17819],{"value":108,"label":17788},"LiDAR（光達）",{"value":662,"label":17790},"慣性量測單元（IMU）",{"value":522,"label":17792},"相機",{"value":1689,"label":17794},"立體相機",{"value":1776,"label":17796},"RGB-D 相機",{"value":3173,"label":17798},"事件相機（event camera）",{"value":4378,"label":17800},"熱像儀",{"value":882,"label":17802},"雷達（radar）",{"value":651,"label":17804},"GNSS 接收器",{"value":6267,"label":17806},"超寬頻定位（UWB）",{"value":353,"label":17808},"輪式或腿式里程計",{"value":2807,"label":17810},"地面雷射掃描儀（TLS）",{"value":944,"label":17812},"行動掃描設備",{"value":5895,"label":17814},"全測站（total station）",{"value":372,"label":17816},"載具與平台",{"value":18,"label":17818},"運算硬體",{"value":33,"label":17820},"其他設備",[17822,17825,17828,17831,17834],{"value":17823,"label":17824},"method input","方法輸入",{"value":17826,"label":17827},"dataset sensor","資料集感測器",{"value":17829,"label":17830},"reference or ground truth","參考或真值量測",{"value":17832,"label":17833},"compute for runtime","執行運算平台",{"value":17835,"label":17836},"compared 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