[{"data":1,"prerenderedAt":621},["ShallowReactive",2],{"method-molalo2025":3},{"method":4,"reference":64,"equipment":83,"figures":193,"results":194},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":22,"limitations":25,"sensors":31,"platform":37,"estimator":44,"association":45,"timeModel":46,"deskew":47,"loopClosure":48,"globalOptimization":49,"mapRepresentation":50,"prior":51,"outputGeometry":52,"compute":53,"codeUrl":54,"codeLicense":55,"relatedVersions":56},"molalo2025","Blanco-Claraco, 2025","MOLA-LO","A flexible framework for accurate LiDAR odometry, map manipulation, and localization",2025,"recent","C05","odometry_with_local_mapping","MOLA-LO 主張以「視圖式地圖」（view-based map：帶時間戳的原始感測資料加上位姿）作為基本地圖表示，事後可依任務重新產生各種度量地圖，例如點雲、雜湊體素、佔據體素或類 NDT 地圖。建圖流程可像組合神經網路層一樣以可重用區塊設定，而不需撰寫程式。其 LiDAR 里程計在類 ICP 最佳化中緊耦合估計線速度與角速度，不需 IMU；迴圈閉合則以後處理方式進行，並可加入 GNSS 做地理參考。","An open framework centred on view-based maps from which task-specific metric maps are regenerated, with IMU-free LiDAR odometry that jointly estimates velocities inside ICP, offline loop closure, and GNSS georeferencing.","full_text_reviewed","peer_reviewed_published","background","在 Hilti 2021 的 RPG Drone Testing Arena 序列（作者表列為無人機、工業廠房、約 0.08 km）評估；作者並回報在部分狹窄、無特徵且未公開地面真值的 Hilti 2021 室內序列無法收斂。另有背包式森林建圖與 DARPA SubT 洞穴（ANYmal）案例；未見施工現場點雲幾何評估。",[20,21],"public_benchmark","underground_or_tunnel",[23,24],"Single self-adaptive configuration tested on 83 sequences over more than 250 km of automotive, handheld, airborne and quadruped data (abstract)","Enables posterior generation of maps optimized for different tasks (abstract; Sec. 3.2)",[26,27,28,29,30],"Fails to converge on some Hilti 2021 sequences without public ground truth (narrow, featureless indoor spaces) where feature extraction or multi-modality seem needed (Sec. 4.6)","Vertical (z) drift dominates error in some ANYmal SubT sequences (Sec. 4.8)","Loop closure runs as post-processing, not concurrently (Sec. 3.12)","Loop closure and georeferencing are not released as open source; the other framework components are (Sec. 8)","With a single horizontal LiDAR on the NTU VIRAL drone, ATE is about an order of magnitude worse than LIO baselines; only a two-LiDAR near and far map configuration reaches LIO-level errors (Sec. 4.5; Table 6)",[32,33,34,35,36],"3D LiDAR (16 to 128 rings)","2D LiDAR","optional wheel odometry for kinematic prediction","optional consumer-grade GNSS (loop closure and georeferencing only)","IMU not used by LO",[38,39,40,41,42,43],"vehicle (KITTI, KITTI-360, MulRan, ParisLuco, UAL campus)","handheld (Newer College)","UAV (Voxgraph, HILTI 2021 drone arena, NTU VIRAL)","legged (ANYmal C, DARPA SubT)","backpack (Almeria forests)","wheelchair and indoor robot with 2D LiDAR","ICP-like optimizer with tightly-coupled estimation of linear and angular velocity (no IMU required); self-adaptive parameters via dynamic variables","Default lidar3d-default configuration: point-to-point pairings between the sparser twice-decimated scan layer and the local map, solved by Gauss-Newton on SE(3) with a robust kernel; matching threshold and kernel scale follow an adaptive threshold inspired by KISS-ICP but driven by a proportional feedback controller on ICP quality; the 3D-NDT configuration adds point-to-plane pairings for planar voxels","discrete scan poses with linear and angular velocities estimated inside the ICP-like optimizer and used for intra-scan SE(3) interpolation (Sec. 3.3.1; Sec. 7.1)","per-scan de-skewing by trajectory interpolation on SE(3) using the estimated velocities (Eq. 2); ablation on UAL campus data: ATE 7.97 m without vs 6.65 m with de-skewing (Sec. 7.1)","Post-processing only (not concurrent with LO): the view-based map is split into sub-maps with bounding boxes and optional GNSS georeferencing; candidates are sub-map pairs whose expected bounding-box intersection (Monte Carlo over relative poses from Dijkstra on the sub-map graph) exceeds a threshold, with no place-recognition descriptor; each candidate is verified by an ICP pipeline with separate ground and non-ground layers and a voxel-occupancy quality score","Two-level graph: key-frame factor graph in GTSAM with LO relative-pose factors and optional GNSS factors, optimized first without and then with robust kernels, re-optimized after each accepted loop closure; the sub-map graph is used only for candidate search","View-based map (key-frames with pose, velocities and raw observations) as the stored map; the LO local map is a single hashed-voxel point cloud with at most 20 points per voxel and resolution 1.5% of the estimated maximum sensor range clamped to 0.5 to 1.0 m, updated only when a decider's distance criteria are met; other layers (contiguous point clouds, VDB occupancy voxels, 3D-NDT, 2D grids) can be regenerated from the view-based map","optional GNSS; prior metric map for localization","arbitrary metric maps regenerated from view-based maps; georeferenced maps and trajectories exportable to KML (Sec. 5.2)","No GPU use is reported; per-scan times measured on an Intel i7-8700 at 3.20 GHz for MulRan (24 ms default, 39 ms 3D-NDT, 40 to 42 ms with loop closure); other tables report 23 ms (KITTI), 13.1 ms (DARPA SubT), 14.5 ms (UAL campus) and 31.6 to 69.4 ms (NTU VIRAL configurations); results are deterministic across CPUs","https:\u002F\u002Fgithub.com\u002FMOLAorg\u002Fmola","mola_lidar_odometry: GPL-3.0 (LICENSE and package.xml checked); main mola repository: per-package licenses declared in package.xml (not individually checked)",[57,61],{"relation":58,"title":59,"doi_or_url":60},"preprint","A flexible framework for accurate LiDAR odometry, map manipulation, and localization (arXiv v3)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.20465",{"relation":62,"title":63,"doi_or_url":54},"code_release","MOLAorg\u002Fmola and MOLAorg\u002Fmola_lidar_odometry",{"id":5,"kind":65,"shortName":7,"title":8,"authors":66,"year":9,"venue":68,"venueType":69,"publisher":70,"volumeIssuePages":71,"doi":72,"arxivId":73,"url":74,"firstPublicDate":75,"publicationStatus":16,"metadataStatus":76,"fulltextStatus":15,"era":10,"classicReason":77,"codeUrl":54,"cluster":11,"topics":78,"mdpi":79,"verification":80,"label":6,"fulltextRoute":81,"versionRead":82,"addedByCensus":79},"method",[67],"Jose Luis Blanco-Claraco","The International Journal of Robotics Research","journal","SAGE","44(9):1553-1599","10.1177\u002F02783649251316881","2407.20465","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1177\u002F02783649251316881","2024-07-29","metadata_verified","not_applicable",[11],false,"corrected","NTU institutional (Chrome)","Version of record, IJRR 44(9):1553-1599 (SAGE HTML, first published online 2025-02-14)",[84,92,97,101,107,112,117,121,127,133,139,144,149,154,159,163,167,172,175,181,186],{"category":85,"model":86,"canonical":87,"role":88,"dataset":89,"specs":90,"locator":91},"lidar","OS0-128","Ouster OS0-128","dataset sensor","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",{"category":85,"model":93,"canonical":94,"role":88,"dataset":95,"specs":96,"locator":91},"OS1-64","Ouster OS1-64","Newer College (Ramezani et al. 2020)","64 rings; handheld (Table 1; Sec. 4.9 text names it OS0-64, inconsistent with Table 1)",{"category":85,"model":93,"canonical":94,"role":88,"dataset":98,"specs":99,"locator":100},"MulRan","vehicle; dataset also provides consumer-grade GNSS used only in loop closure","Table 1; Sec. 4.1",{"category":102,"model":103,"canonical":103,"role":104,"dataset":98,"specs":105,"locator":106},"gnss","consumer-grade GNSS receiver","method input","used for georeferencing and loop-closure candidate search, not by LO","Sec. 4.1; Sec. 3.11; Sec. 7.5",{"category":85,"model":108,"canonical":108,"role":88,"dataset":109,"specs":110,"locator":111},"Velodyne HDL-64E","KITTI odometry","64 rings; 0.205 deg vertical angle correction applied","Table 1; Sec. 4.2",{"category":85,"model":113,"canonical":108,"role":88,"dataset":114,"specs":115,"locator":116},"HDL-64E","KITTI-360","vehicle","Table 1; Sec. 4.3",{"category":85,"model":93,"canonical":94,"role":88,"dataset":118,"specs":119,"locator":120},"Voxgraph","drone; RTK-based ground truth","Table 1; Sec. 4.7",{"category":85,"model":122,"canonical":123,"role":88,"dataset":124,"specs":125,"locator":126},"OS0-64","Ouster OS0-64","HILTI 2021","drone testing arena, industrial unit","Table 1; Sec. 4.6",{"category":85,"model":128,"canonical":129,"role":88,"dataset":130,"specs":131,"locator":132},"Velodyne HDL-32","Velodyne HDL-32E","ParisLuco","32 rings; vehicle","Table 1; Sec. 4.4",{"category":85,"model":134,"canonical":135,"role":104,"dataset":136,"specs":137,"locator":138},"OS0-32","Ouster OS0-32","Almeria forests (Aguilar et al. 2024)","backpack kit with the LiDAR as the only sensor; forests","Table 1; Sec. 5.3",{"category":85,"model":140,"canonical":140,"role":88,"dataset":141,"specs":142,"locator":143},"2 x OS1-16 (Table 1); text names two Velodyne VLP-16","NTU-VIRAL","one horizontal and one vertical LiDAR on a drone; motion capture ground truth","Table 1; Sec. 4.5",{"category":85,"model":145,"canonical":145,"role":88,"dataset":146,"specs":147,"locator":148},"Velodyne VLP-16","DARPA Subterranean final event","one per ANYmal C robot","Table 1; Sec. 4.8",{"category":150,"model":151,"canonical":151,"role":88,"dataset":146,"specs":152,"locator":153},"platform","ANYmal C legged robot","four robots, one sequence each","Sec. 4.8",{"category":155,"model":156,"canonical":156,"role":157,"dataset":146,"specs":158,"locator":153},"tls_scanner","survey-quality scanners (models not reported)","reference or ground truth","ground truth point cloud used to derive ground-truth trajectories by scan matching",{"category":85,"model":145,"canonical":145,"role":88,"dataset":160,"specs":161,"locator":162},"UAL VLP-16 campus","electric vehicle on university campus","Table 1; Sec. 4.10",{"category":102,"model":164,"canonical":164,"role":157,"dataset":160,"specs":165,"locator":166},"RTK GNSS","3D positioning used as ground truth","Sec. 4.10",{"category":85,"model":168,"canonical":168,"role":88,"dataset":169,"specs":170,"locator":171},"SICK LMS 2D range finder","Freiburg building 079 (fr079)","2D LiDAR; Pioneer2 robot in text, PowerBot in Table 1","Table 1; Sec. 5.1",{"category":85,"model":168,"canonical":168,"role":88,"dataset":173,"specs":174,"locator":171},"Malaga CS faculty","2D LiDAR on a robotic wheelchair with encoders, 1.9 km",{"category":176,"model":177,"canonical":177,"role":104,"dataset":178,"specs":179,"locator":180},"wheel_or_leg_odometry","wheel encoders","fr079 and Malaga CS faculty","incremental odometry used by kinematic state prediction in the 2D configuration","Sec. 3.8; Sec. 5.1",{"category":182,"model":183,"canonical":183,"role":157,"dataset":141,"specs":184,"locator":185},"other","motion capture system","accurate ground truth for NTU VIRAL","Sec. 4.5",{"category":187,"model":188,"canonical":188,"role":189,"dataset":190,"specs":191,"locator":192},"compute","Intel i7-8700 at 3.20 GHz","compute for runtime",null,"desktop CPU used for MulRan per-scan timings","Sec. 4.1",[],{"totalRows":195,"groupCount":196,"groups":197,"others":592},92,9,[198,367,446,546],{"slug":199,"group":200,"sourceId":5,"sourceLabel":6,"table":201,"selfRows":202,"metrics":203,"seqs":209,"entrants":241,"cells":252,"outcomes":360,"locators":362,"hardware":363,"wordings":364,"notes":365},"molalo2025-table-9","molalo2025:Table 9","Table 9",42,[204],{"label":205,"unit":206,"statistic":207,"alignment":208},"absolute translational error (RMSE, evo_ape -a)","m","RMSE","SE3",[210,214,216,219,221,223,225,227,229,231,233,235,237,239],{"dataset":211,"sequence":212,"environment":213},"Newer College (2020, sequences 01 and 02)","01","college campus, handheld (outdoor quads, underground passages, stairs)",{"dataset":211,"sequence":215,"environment":213},"02",{"dataset":217,"sequence":218,"environment":213},"Newer College extension (2021)","Cloister",{"dataset":217,"sequence":220,"environment":213},"MathsEasy",{"dataset":217,"sequence":222,"environment":213},"MathsMedium",{"dataset":217,"sequence":224,"environment":213},"MathsHard",{"dataset":217,"sequence":226,"environment":213},"Park",{"dataset":217,"sequence":228,"environment":213},"QuadEasy",{"dataset":217,"sequence":230,"environment":213},"QuadMedium",{"dataset":217,"sequence":232,"environment":213},"QuadHard",{"dataset":217,"sequence":234,"environment":213},"Undergr.Easy",{"dataset":217,"sequence":236,"environment":213},"Undergr.Medium",{"dataset":217,"sequence":238,"environment":213},"Undergr.Hard",{"dataset":217,"sequence":240,"environment":213},"Stairs",[242,246,248,250],{"name":243,"methodId":244,"linkable":245,"proposed":79,"self":79},"KISS-ICP","kissicp2023",true,{"name":247,"methodId":5,"linkable":245,"proposed":245,"self":245},"MOLA-LO (ours)",{"name":249,"methodId":5,"linkable":245,"proposed":79,"self":245},"MOLA-LO (always updates local map)",{"name":251,"methodId":5,"linkable":245,"proposed":245,"self":245},"MOLA-LO + LC (ours)",[253,257,260,263,266,269,272,275,278,281,283,286,289,292,295,297,299,300,302,303,305,307,309,310,311,312,313,315,316,318,320,322,323,325,327,329,330,332,333,334,336,338,340,342,344,345,346,348,349,350,351,352,354,355,356,358],[254,254,254,255,256,254,256,256,254],0,0.61,-1,[254,254,258,259,256,254,256,256,254],1,1.78,[254,254,261,262,256,254,256,256,254],2,0.95,[254,254,264,265,256,254,256,256,254],3,0.07,[254,254,267,268,256,254,256,256,254],4,0.12,[254,254,270,271,254,254,256,256,254],5,29.6,[254,254,273,274,256,254,256,256,254],6,1.54,[254,254,276,277,256,254,256,256,254],7,0.1,[254,254,279,280,256,254,256,256,254],8,0.19,[254,254,196,282,256,254,256,256,254],0.65,[254,254,284,285,256,254,256,256,254],10,0.26,[254,254,287,288,256,254,256,256,254],11,0.45,[254,254,290,291,254,254,256,256,254],12,7.98,[254,254,293,294,254,254,256,256,254],13,3407,[258,254,254,296,256,254,256,256,254],0.68,[258,254,258,298,256,254,256,256,254],0.54,[258,254,261,268,256,254,256,256,254],[258,254,264,301,256,254,256,256,254],0.06,[258,254,267,268,256,254,256,256,254],[258,254,270,304,256,254,256,256,254],0.11,[258,254,273,306,256,254,256,256,254],0.9,[258,254,276,308,256,254,256,256,254],0.08,[258,254,279,308,256,254,256,256,254],[258,254,196,268,256,254,256,256,254],[258,254,284,265,256,254,256,256,254],[258,254,287,308,256,254,256,256,254],[258,254,290,314,256,254,256,256,254],0.09,[258,254,293,304,256,254,256,256,254],[261,254,254,317,254,254,256,256,254],5.11,[261,254,258,319,254,254,256,256,254],84.37,[261,254,261,321,256,254,256,256,254],0.39,[261,254,264,314,256,254,256,256,254],[261,254,267,324,254,254,256,256,254],4.93,[261,254,270,326,256,254,256,256,254],0.78,[261,254,273,328,256,254,256,256,254],1.38,[261,254,276,308,256,254,256,256,254],[261,254,279,331,256,254,256,256,254],2.99,[261,254,196,277,256,254,256,256,254],[261,254,284,308,256,254,256,256,254],[261,254,287,335,256,254,256,256,254],0.46,[261,254,290,337,254,254,256,256,254],8.06,[261,254,293,339,256,254,256,256,254],0.34,[264,254,254,341,256,254,256,256,254],0.31,[264,254,258,343,256,254,256,256,254],0.4,[264,254,261,280,256,254,256,256,254],[264,254,264,314,256,254,256,256,254],[264,254,267,347,256,254,256,256,254],0.13,[264,254,270,341,256,254,256,256,254],[264,254,273,341,256,254,256,256,254],[264,254,276,314,256,254,256,256,254],[264,254,279,268,256,254,256,256,254],[264,254,196,353,256,254,256,256,254],0.16,[264,254,284,347,256,254,256,256,254],[264,254,287,347,256,254,256,256,254],[264,254,290,357,256,254,256,256,254],0.25,[264,254,293,359,256,254,256,256,254],0.42,[361],"diverged (value printed in parentheses)",[201],[],[],[366],"Handheld Newer College sequences; ATE RMSE from evo_ape -a (Umeyama alignment); x(value) marks divergence; no method uses the IMU; same default configuration for all datasets",{"slug":368,"group":369,"sourceId":5,"sourceLabel":6,"table":370,"selfRows":371,"metrics":372,"seqs":374,"entrants":392,"cells":396,"outcomes":440,"locators":441,"hardware":442,"wordings":443,"notes":444},"molalo2025-table-4","molalo2025:Table 4","Table 4",16,[373],{"label":205,"unit":206,"statistic":207,"alignment":208},[375,378,380,382,384,386,388,390],{"dataset":114,"sequence":376,"environment":377},"00","suburban driving",{"dataset":114,"sequence":379,"environment":377},"03",{"dataset":114,"sequence":381,"environment":377},"04",{"dataset":114,"sequence":383,"environment":377},"05",{"dataset":114,"sequence":385,"environment":377},"06",{"dataset":114,"sequence":387,"environment":377},"07",{"dataset":114,"sequence":389,"environment":377},"09",{"dataset":114,"sequence":391,"environment":377},"10",[393,394,395],{"name":243,"methodId":244,"linkable":245,"proposed":79,"self":79},{"name":247,"methodId":5,"linkable":245,"proposed":245,"self":245},{"name":251,"methodId":5,"linkable":245,"proposed":245,"self":245},[397,399,401,403,405,407,409,411,413,415,417,419,421,423,425,427,429,430,431,432,434,436,437,439],[254,254,254,398,256,254,256,256,254],5.5,[254,254,258,400,256,254,256,256,254],0.62,[254,254,261,402,256,254,256,256,254],5.85,[254,254,264,404,256,254,256,256,254],3.05,[254,254,267,406,256,254,256,256,254],10.45,[254,254,270,408,256,254,256,256,254],4.22,[254,254,273,410,256,254,256,256,254],12.3,[254,254,276,412,256,254,256,256,254],5.9,[258,254,254,414,256,254,256,256,254],2.81,[258,254,258,416,256,254,256,256,254],0.72,[258,254,261,418,256,254,256,256,254],4.89,[258,254,264,420,256,254,256,256,254],2.39,[258,254,267,422,256,254,256,256,254],6.37,[258,254,270,424,256,254,256,256,254],8.22,[258,254,273,426,256,254,256,256,254],10.78,[258,254,276,428,256,254,256,256,254],1.75,[261,254,254,416,256,254,256,256,254],[261,254,258,416,256,254,256,256,254],[261,254,261,418,256,254,256,256,254],[261,254,264,433,256,254,256,256,254],1.02,[261,254,267,435,256,254,256,256,254],4.03,[261,254,270,424,256,254,256,256,254],[261,254,273,438,256,254,256,256,254],4.67,[261,254,276,428,256,254,256,256,254],[],[370],[],[],[445],"KITTI-360 ATE RMSE (evo_ape -a); sequences 03, 07 and 10 have no loop closures",{"slug":447,"group":448,"sourceId":5,"sourceLabel":6,"table":449,"selfRows":290,"metrics":450,"seqs":461,"entrants":469,"cells":491,"outcomes":537,"locators":538,"hardware":539,"wordings":541,"notes":542},"molalo2025-table-3","molalo2025:Table 3","Table 3",[451,456,458],{"label":452,"unit":453,"statistic":454,"alignment":455},"Avr. 00-10 RTE (%)","%","mean","none",{"label":457,"unit":453,"statistic":454,"alignment":455},"Avr. 11-21 RTE (%)",{"label":459,"unit":460,"statistic":454,"alignment":77},"Avr. time per frame","ms",[462,465,467],{"dataset":109,"sequence":463,"environment":464},"Avr. 00-10","road and urban driving",{"dataset":109,"sequence":466,"environment":464},"Avr. 11-21",{"dataset":109,"sequence":468,"environment":464},"KITTI 00-10",[470,472,473,475,477,479,481,483,486,489],{"name":471,"methodId":190,"linkable":79,"proposed":79,"self":79},"IMLS-SLAM",{"name":243,"methodId":244,"linkable":245,"proposed":79,"self":79},{"name":474,"methodId":190,"linkable":79,"proposed":79,"self":79},"SiMpLE (offline)",{"name":476,"methodId":190,"linkable":79,"proposed":79,"self":79},"SiMpLE (online)",{"name":478,"methodId":5,"linkable":245,"proposed":245,"self":245},"MOLA-LO (default) (ours)",{"name":480,"methodId":5,"linkable":245,"proposed":245,"self":245},"MOLA-LO (3D-NDT) (ours)",{"name":482,"methodId":5,"linkable":245,"proposed":79,"self":245},"MOLA-LO (Horn's) (ours)",{"name":484,"methodId":485,"linkable":245,"proposed":79,"self":79},"MULLS (LC)","mulls2021",{"name":487,"methodId":488,"linkable":245,"proposed":79,"self":79},"CT-ICP2 (LC)","cticp2022",{"name":490,"methodId":5,"linkable":245,"proposed":245,"self":245},"MOLA-LO (default) + LC (ours)",[492,494,495,496,497,498,500,501,503,505,506,508,509,510,511,512,513,514,515,516,518,520,521,523,525,527,529,531,533,535],[254,254,254,493,256,254,256,256,254],0.55,[258,254,254,493,256,254,256,256,254],[261,254,254,493,256,254,256,256,254],[264,254,254,400,256,254,256,256,254],[267,254,254,493,256,254,256,256,254],[270,254,254,499,256,254,256,256,254],0.58,[273,254,254,282,256,254,256,256,254],[276,254,254,502,256,254,256,256,254],0.52,[279,254,254,504,256,254,256,256,254],0.53,[196,254,254,499,256,254,256,256,254],[254,258,258,507,256,254,256,256,258],0.69,[258,258,258,255,256,254,256,256,258],[261,258,258,400,256,254,256,256,258],[264,258,258,190,254,254,256,256,258],[267,258,258,400,256,254,256,256,258],[270,258,258,190,254,254,256,256,258],[273,258,258,190,254,254,256,256,258],[276,258,258,282,256,254,256,256,258],[279,258,258,499,256,254,256,256,258],[196,258,258,517,256,254,256,256,258],0.66,[254,261,261,519,256,254,254,256,261],1000,[258,261,261,293,256,254,254,256,261],[261,261,261,522,256,254,254,256,261],545,[264,261,261,524,256,254,254,256,261],126,[267,261,261,526,256,254,254,256,261],23,[270,261,261,528,256,254,254,256,261],32,[273,261,261,530,256,254,254,256,261],45,[276,261,261,532,256,254,254,256,261],100,[279,261,261,534,256,254,254,256,261],60,[196,261,261,536,256,254,254,256,261],31,[77],[449],[540],"not stated for this table (Sec. 4.1 reports an Intel i7-8700 at 3.20 GHz for MulRan timings); baseline times taken from original publications may use other hardware",[],[543,544,545],"KITTI odometry average RTE over training sequences 00 to 10; IMLS-SLAM, MULLS and CT-ICP2 values from their publications; 0.205 deg vertical correction applied to KITTI scans for KISS-ICP, SiMpLE and MOLA-LO","KITTI odometry average RTE over evaluation sequences 11 to 21 from the public leaderboard; N\u002FA where not submitted","KITTI average time per frame (IMLS-SLAM printed as about 1000 ms)",{"slug":547,"group":548,"sourceId":5,"sourceLabel":6,"table":549,"selfRows":273,"metrics":550,"seqs":553,"entrants":558,"cells":566,"outcomes":585,"locators":586,"hardware":587,"wordings":588,"notes":589},"molalo2025-table-10","molalo2025:Table 10","Table 10",[551,552],{"label":205,"unit":206,"statistic":207,"alignment":208},{"label":459,"unit":460,"statistic":454,"alignment":77},[554],{"dataset":555,"sequence":556,"environment":557},"UAL VLP-16 campus dataset","UAL campus","university campus driving",[559,561,563,564,565],{"name":560,"methodId":244,"linkable":245,"proposed":79,"self":79},"KISS-ICP (w\u002Fo deskew)",{"name":562,"methodId":5,"linkable":245,"proposed":79,"self":245},"MOLA-LO (w\u002Fo deskew) (ours)",{"name":243,"methodId":244,"linkable":245,"proposed":79,"self":79},{"name":247,"methodId":5,"linkable":245,"proposed":245,"self":245},{"name":251,"methodId":5,"linkable":245,"proposed":245,"self":245},[567,569,571,573,575,576,577,579,581,583],[254,254,254,568,256,254,256,256,254],13.21,[258,254,254,570,256,254,256,256,254],7.97,[261,254,254,572,256,254,256,256,254],10.06,[264,254,254,574,256,254,256,256,254],6.65,[267,254,254,258,256,254,256,256,254],[254,258,254,290,256,254,254,256,258],[258,258,254,578,256,254,254,256,258],14.7,[261,258,254,580,256,254,254,256,258],11.2,[264,258,254,582,256,254,254,256,258],14.5,[267,258,254,584,256,254,254,256,258],21.3,[],[549],[540],[],[590,591],"UAL campus, electric vehicle with VLP-16, RTK GNSS ground truth; with and without scan deskewing","UAL campus average time per frame",[593,598,604,610,615],{"group":594,"slug":595,"sourceLabel":6,"table":596,"selfRows":273,"datasets":597},"molalo2025:Table 5","molalo2025-table-5","Table 5",[130],{"group":599,"slug":600,"sourceLabel":6,"table":601,"selfRows":270,"datasets":602},"molalo2025:Table 8","molalo2025-table-8","Table 8",[603],"DARPA Subterranean final event (Team CERBERUS)",{"group":605,"slug":606,"sourceLabel":6,"table":607,"selfRows":261,"datasets":608},"molalo2025:Table 7","molalo2025-table-7","Table 7",[609],"Voxgraph dataset",{"group":611,"slug":612,"sourceLabel":6,"table":613,"selfRows":261,"datasets":614},"molalo2025:Text Sec. 6","molalo2025-text-sec-6","Text Sec. 6",[98],{"group":616,"slug":617,"sourceLabel":6,"table":618,"selfRows":258,"datasets":619},"molalo2025:Text Sec. 4.6","molalo2025-text-sec-4-6","Text Sec. 4.6",[620],"HILTI 2021 SLAM challenge",1790510658993]