[{"data":1,"prerenderedAt":1300},["ShallowReactive",2],{"method-mulls2021":3},{"method":4,"reference":64,"equipment":86,"figures":138,"results":139},{"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":24,"limitations":31,"sensors":38,"platform":40,"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},"mulls2021","Pan et al., 2021","MULLS","MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square",2021,"recent","C04","full_slam_with_global_correction","MULLS 不依賴掃描線或距離影像，直接把每幀點雲分類為地面、立面、屋頂、柱、梁與頂點等幾何特徵點，因而可用於不同線數與配置的 LiDAR。前端以「多度量線性最小平方」ICP 在各類別內同時最小化點到點、點到面與點到線距離，並以殘差、方向平衡與強度一致性加權；後端以子地圖為單位，透過 TEASER 全域配準與 MULLS-ICP 精修建立迴圈邊，再做階層式位姿圖最佳化。","MULLS classifies points into ground\u002Ffacade\u002Froof\u002Fpillar\u002Fbeam\u002Fvertex classes without scan-line assumptions, registers them with a weighted multi-metric linear least-squares ICP, and closes loops between submaps via TEASER plus hierarchical pose-graph optimization.","full_text_reviewed","peer_reviewed_published","main_body","MULLS 在 ISPRS MIMAP 背包資料（五層樓既有建築）中，以 TLS 點雲為參考評估地圖品質，平均最近鄰距離 6.7 cm（Sec. IV-B1）；此為既有建築而非施工中工地，且度量為雲對雲最近鄰距離，對齊方式未在所讀內容中說明。其立面、柱、梁等分類與建築構件語意相近（推論），但未做工程任務驗證。",[20,21,22,23],"public_benchmark","completed_building","independent_reference","cross_site",[25,26,27,28,29,30],"KITTI 00-10 mean 0.49% and 0.16 deg per 100 m for MULLS-LO (best ATE in Table II) and 0.65% and 0.19 deg per 100 m on the online test set 11-21 (runner-up)","MULLS-SLAM with loop closure has the best ARE, 0.13 deg per 100 m, but slightly worse ATE (Table II, Sec. IV-A)","with a single scan-to-map iteration MULLS already ranks 5th, supporting a speed and accuracy trade-off (Sec. IV-A)","MIMAP 5-floor building: mean nearest-neighbor distance 6.7 cm between the MULLS map and a TLS (written 'Rigel VZ-1000') point cloud of floors 1-2 (Sec. IV-B1, Fig. 9)","worked across seven lidar types indoors and outdoors (Table I, Fig. 10)","transformation estimation 0.2 ms per ICP iteration with about 2k source and 20k target points, 80 ms per frame in total (Table V)",[32,33,34,35,36,37],"May encounter problems in tunnels where structured features are rare (Sec. IV-C, Table III discussion)","loop-heavy sequences sometimes exceed 100 ms per frame (Sec. IV-D)","HESAI results are qualitative only (no ground truth) (Sec. IV-B2)","ground filtering needs a known initial orientation when the LiDAR is not mounted horizontally (Sec. III-B1)","vertex point-to-point correspondences reduced odometry accuracy, so vertices are used only as keypoints for global registration (Sec. IV-C, Table III)","KITTI ATE and ARE, measured within 800 m, do not reflect the global gain of loop closure (Sec. IV-A)",[39],"3D LiDAR (seven types: Velodyne HDL-64E, VLP-32C, HDL-32E; Hesai Pandar QT Lite, XT, 64, 128); no IMU required",[41,42,43],"vehicle (KITTI)","backpack (MIMAP)","not_reported (HESAI dataset platform)","multi-metric linear least-squares ICP (small-angle linearization, Gauss-Markov estimation) with residual, direction-balance and intensity weights; hierarchical inter-\u002Finner-submap pose-graph optimization (Sec. III-C, III-E)","ring\u002Frange-image independent classification into ground, facade, roof, pillar, beam and vertex points (dual-threshold ground filter + PCA); category-constrained nearest neighbors with point-to-point, point-to-plane and point-to-line metrics (Sec. III-B, III-C)","discrete frame poses","optional uniform-motion correction with slerp when point-wise timestamps are available and no IMU (Sec. III-A)","submap-to-submap global registration with TEASER using neighborhood-category-context (NCC) features, refined by MULLS-ICP; edges rejected by posterior std. and overlap thresholds (Sec. III-E)","hierarchical pose graph: inter-submap then inner-submap (Sec. III-E, Fig. 6)","local map of static classified feature points cropped to a radius; periodically stored submaps (Sec. III-D, III-E)","none","point cloud map and trajectory; compared with a TLS point cloud on MIMAP (Sec. IV-B1, Fig. 9)","Intel i7-7700HQ CPU; about 0.08-0.10 s per frame on KITTI; transformation estimation about 0.2 ms per ICP iteration (Table II, Table V)","https:\u002F\u002Fgithub.com\u002FYuePanEdward\u002FMULLS","GPL-3.0 (LICENSE file)",[57,61],{"relation":58,"title":59,"doi_or_url":60},"preprint","arXiv 2102.03771 (v1 2021-02-07, v3 2021-04-27)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03771",{"relation":62,"title":63,"doi_or_url":54},"code_release","YuePanEdward\u002FMULLS",{"id":5,"kind":65,"shortName":7,"title":8,"authors":66,"year":9,"venue":72,"venueType":73,"publisher":74,"volumeIssuePages":75,"doi":76,"arxivId":77,"url":60,"firstPublicDate":78,"publicationStatus":16,"metadataStatus":79,"fulltextStatus":15,"era":10,"classicReason":80,"codeUrl":54,"cluster":11,"topics":81,"mdpi":82,"verification":83,"label":6,"fulltextRoute":84,"versionRead":85,"addedByCensus":82},"method",[67,68,69,70,71],"Yue Pan","Pengchuan Xiao","Yujie He","Zhenlei Shao","Zesong Li","2021 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 11633-11640","10.1109\u002Ficra48506.2021.9561364","2102.03771","2021-02-07","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv 2102.03771 v3 (2021-04-27); IEEE ICRA 2021 version of record not read",[87,94,100,106,110,115,120,125,127,129,131],{"category":88,"model":89,"canonical":89,"role":90,"dataset":91,"specs":92,"locator":93},"lidar","Velodyne HDL-64E","dataset sensor","KITTI odometry","written 'HDL64E' in Table I; 22 sequences, more than 43k scans","Sec. IV-A, Table I",{"category":95,"model":96,"canonical":96,"role":97,"dataset":91,"specs":98,"locator":99},"gnss","KITTI GNSS-INS ground truth (model not stated)","reference or ground truth","ground truth poses for seq. 00-10","Sec. IV-A",{"category":88,"model":101,"canonical":102,"role":90,"dataset":103,"specs":104,"locator":105},"Velodyne VLP32C","Velodyne VLP-32C","ISPRS MIMAP","on a backpack mapping system; 3 sequences, 35k frames with HDL32E, indoor","Sec. IV-B1, Table I",{"category":88,"model":107,"canonical":108,"role":90,"dataset":103,"specs":109,"locator":105},"Velodyne HDL32E","Velodyne HDL-32E","on a backpack mapping system; indoor 5-floor building",{"category":111,"model":112,"canonical":112,"role":90,"dataset":103,"specs":113,"locator":114},"platform","Backpack mapping system (model not stated)","carries VLP32C and HDL32E","Sec. IV-B1",{"category":116,"model":117,"canonical":117,"role":97,"dataset":103,"specs":118,"locator":119},"tls_scanner","Rigel VZ-1000","high accuracy TLS point cloud of floors 1 and 2 used as map ground truth","Sec. IV-B1, Fig. 9",{"category":88,"model":121,"canonical":121,"role":90,"dataset":122,"specs":123,"locator":124},"Hesai Pandar128","HESAI","mechanical LiDAR","Sec. IV-B2, Table I",{"category":88,"model":126,"canonical":126,"role":90,"dataset":122,"specs":123,"locator":124},"Hesai Pandar64",{"category":88,"model":128,"canonical":128,"role":90,"dataset":122,"specs":123,"locator":124},"Hesai PandarXT",{"category":88,"model":130,"canonical":130,"role":90,"dataset":122,"specs":123,"locator":124},"Hesai PandarQT Lite",{"category":132,"model":133,"canonical":133,"role":134,"dataset":135,"specs":136,"locator":137},"compute","Intel Core i7-7700HQ","compute for runtime",null,"2.80 GHz; all experiments","Sec. 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V-A-1)",{"dataset":501,"sequence":168,"environment":502},{"dataset":501,"sequence":171,"environment":502},{"dataset":501,"sequence":173,"environment":502},{"dataset":501,"sequence":175,"environment":502},{"dataset":501,"sequence":177,"environment":502},{"dataset":501,"sequence":179,"environment":502},{"dataset":501,"sequence":183,"environment":502},{"dataset":501,"sequence":185,"environment":502},{"dataset":501,"sequence":483,"environment":502},{"dataset":501,"sequence":486,"environment":502},{"dataset":514,"sequence":515,"environment":516},"KITTI-CARLA","Town01","simulation (CARLA 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(ours)",[546,547,549,550,552,553,555,556,558,559,560,562,563,565,566,567,569,570,572,573,575,576,578,579,580,582,583,584,586,588,589,591,592,594,596,598,600,602,603,605,606,607,609,611,613,614,616,617,619,620,621,622,623,624,625,626,628,629,630,632,635,637,639,641,643,645,647,650,651,653,655,657,658,660,662,663,665,666,668,670,671,673,674,676,678,679,680,682,683,685,687,688,689,690,691,692,693,694,695,696,698,700,702,704,706,709,711,713,715,718,719,720,721,723,725,726,727,729,730,732,734,736,738,740,742,744,746,747,748,750,751,752,754,755,756,758,759,761,764,766,768,769,771,773],[232,232,232,270,234,232,234,234,232],[232,236,236,548,234,232,232,234,232],1250,[236,232,232,270,234,232,234,234,232],[236,236,236,551,234,232,232,234,232],80,[239,232,232,281,234,232,234,234,232],[239,236,236,554,234,232,232,234,232],175,[242,232,232,281,234,232,234,234,232],[242,236,236,557,234,232,236,234,232],60,[232,232,239,264,234,232,234,234,236],[232,232,242,243,234,232,234,234,236],[232,232,245,561,234,232,234,234,236],0.76,[232,232,248,303,234,232,234,234,236],[232,232,251,564,234,232,234,234,236],0.48,[232,232,254,379,234,232,234,234,236],[232,232,257,343,234,232,234,234,236],[232,232,260,568,234,232,234,234,236],0.97,[232,232,263,357,234,232,234,234,236],[232,232,266,571,234,232,234,234,236],0.75,[232,232,269,246,234,232,234,234,236],[232,236,274,574,234,232,232,234,236],1070,[236,232,239,237,234,232,234,234,236],[236,232,242,577,234,232,234,234,236],3.12,[236,232,245,373,234,232,234,234,236],[236,232,248,249,234,232,234,234,236],[236,232,251,581,234,232,234,234,236],1.93,[236,232,254,340,234,232,234,234,236],[236,232,257,297,234,232,234,234,236],[236,232,260,585,234,232,234,234,236],1.28,[236,232,263,587,234,232,234,234,236],1.49,[236,232,266,246,234,232,234,234,236],[236,232,269,590,234,232,234,234,236],1.41,[236,236,274,551,234,232,232,234,236],[239,232,239,593,234,232,234,234,236],2.2,[239,232,242,595,234,232,234,234,236],0.98,[239,232,245,597,234,232,234,234,236],1.55,[239,232,248,599,234,232,234,234,236],0.45,[239,232,251,601,234,232,234,234,236],1.46,[239,232,254,297,234,232,234,234,236],[239,232,257,604,234,232,234,234,236],1.72,[239,232,260,340,234,232,234,234,236],[239,232,263,585,234,232,234,234,236],[239,232,266,608,234,232,234,234,236],1.18,[239,232,269,610,234,232,234,234,236],1.61,[239,236,274,612,234,232,232,234,236],530,[242,232,239,303,234,232,234,234,236],[242,232,242,615,234,232,234,234,236],0.81,[242,232,245,270,234,232,234,234,236],[242,232,248,618,234,232,234,234,236],0.43,[242,232,251,311,234,232,234,234,236],[242,232,254,409,234,232,234,234,236],[242,232,257,407,234,232,234,234,236],[242,232,260,291,234,232,234,234,236],[242,232,263,353,234,232,234,234,236],[242,232,266,415,234,232,234,234,236],[242,232,269,270,234,232,234,234,236],[242,236,274,627,234,232,236,234,236],65,[232,232,450,252,234,232,234,234,239],[232,232,456,255,234,232,234,234,239],[232,232,631,338,234,232,234,234,239],16,[232,232,633,634,234,232,234,234,239],17,0.89,[232,232,636,255,234,232,234,234,239],18,[232,232,638,357,234,232,234,234,239],19,[232,232,640,306,234,232,234,234,239],20,[232,232,642,343,234,232,234,234,239],21,[232,232,644,264,234,232,234,234,239],22,[232,232,646,283,234,232,234,234,239],23,[232,236,648,649,234,232,232,234,239],24,1060,[236,232,450,340,234,232,234,234,239],[236,232,456,652,234,232,234,234,239],1.29,[236,232,631,654,234,232,234,234,239],0.87,[236,232,633,656,234,232,234,234,239],1.64,[236,232,636,399,234,232,234,234,239],[236,232,638,659,234,232,234,234,239],1.48,[236,232,640,661,234,232,234,234,239],6.25,[236,232,642,585,234,232,234,234,239],[236,232,644,664,234,232,234,234,239],0.88,[236,232,646,597,234,232,234,234,239],[236,236,648,667,234,232,232,234,239],90,[239,232,450,669,234,232,234,234,239],1.79,[239,232,456,301,234,232,234,234,239],[239,232,631,672,234,232,234,234,239],0.9,[239,232,633,340,234,232,234,234,239],[239,232,636,675,234,232,234,234,239],1.26,[239,232,638,677,234,232,234,234,239],1.38,[239,232,640,297,234,232,234,234,239],[239,232,642,604,234,232,234,234,239],[239,232,644,681,234,232,234,234,239],1.39,[239,232,646,601,234,232,234,234,239],[239,236,648,684,234,232,232,234,239],475,[242,232,450,686,234,232,234,234,239],0.41,[242,232,456,329,234,232,234,234,239],[242,232,631,315,234,232,234,234,239],[242,232,633,252,234,232,234,234,239],[242,232,636,396,234,232,234,234,239],[242,232,638,389,234,232,234,234,239],[242,232,640,315,234,232,234,234,239],[242,232,642,599,234,232,234,234,239],[242,232,644,297,234,232,234,234,239],[242,232,646,599,234,232,234,234,239],[242,236,648,697,234,232,236,234,239],70,[232,232,699,443,234,232,234,234,242],25,[232,232,701,448,234,232,234,234,242],26,[232,232,703,323,234,232,234,234,242],27,[232,232,705,331,234,232,234,234,242],28,[232,232,707,708,234,232,234,234,242],29,0.06,[232,232,141,710,234,232,234,234,242],4.9,[232,232,712,446,234,232,234,234,242],31,[232,232,714,615,234,232,234,234,242],32,[232,236,716,717,234,232,232,234,242],33,780,[236,232,699,681,234,232,234,234,242],[236,232,701,261,234,232,234,234,242],[236,232,703,297,234,232,234,234,242],[236,232,705,722,234,232,234,234,242],1.24,[236,232,707,724,234,232,234,234,242],1.13,[236,232,141,595,234,232,234,234,242],[236,232,712,568,234,232,234,234,242],[236,232,714,728,234,232,234,234,242],1.04,[236,236,716,697,234,232,232,234,242],[239,232,699,731,234,232,234,234,242],16.25,[239,232,701,733,234,232,234,234,242],7.92,[239,232,703,735,234,232,234,234,242],37.16,[239,232,705,737,234,232,234,234,242],10.06,[239,232,707,739,234,232,234,234,242],9.11,[239,232,141,741,234,232,234,234,242],73.29,[239,232,712,743,234,232,234,234,242],2.69,[239,232,714,745,234,232,234,234,242],23.84,[239,236,716,612,234,232,232,234,242],[242,232,699,443,234,232,234,234,242],[242,232,701,749,234,232,234,234,242],0.04,[242,232,703,443,234,232,234,234,242],[242,232,705,443,234,232,234,234,242],[242,232,707,753,234,232,234,234,242],0.02,[242,232,141,749,234,232,234,234,242],[242,232,712,686,234,232,234,234,242],[242,232,714,757,234,232,234,234,242],0.09,[242,236,716,627,234,232,236,234,242],[232,232,760,675,234,232,234,234,242],34,[232,236,762,763,234,232,232,234,242],35,770,[236,232,760,765,234,232,234,234,242],3.03,[236,236,762,767,234,232,232,234,242],55,[239,232,760,710,234,232,234,234,242],[239,236,762,770,234,232,232,234,242],355,[242,232,760,772,234,232,234,234,242],1.11,[242,236,762,551,234,232,236,234,242],[],[776],"Table I (Driving)",[778,779],"not_reported (the paper states only that CT-ICP itself runs single-threaded by default; no CPU model or thread count is given for the baselines)","CPU, single thread by default (model not reported)",[],[782,783,784,785],"KITTI RTE (%) averaged over segments of 100 to 800 m, Driving profile; AVG over all segments of all sequences; one parameter set per method for all driving datasets; scans are raw (not motion-corrected) except KITTI-corrected; per-sequence KITTI-corrected values omitted for row cap","KITTI RTE (%) averaged over segments of 100 to 800 m, Driving profile; AVG over all segments of all sequences; one parameter set per method for all driving datasets; scans are raw (not motion-corrected) except KITTI-corrected; sequence 03 not available as raw scans","KITTI RTE (%) averaged over segments of 100 to 800 m, Driving profile; AVG over all segments of all sequences; one parameter set per method for all driving datasets; scans are raw (not motion-corrected) except KITTI-corrected; timestamps estimated from azimuth","KITTI RTE (%) averaged over segments of 100 to 800 m, Driving profile; AVG over all segments of all sequences; one parameter set per method for all driving datasets; scans are raw (not motion-corrected) except KITTI-corrected",{"slug":787,"group":788,"sourceId":789,"sourceLabel":790,"table":146,"selfRows":638,"metrics":791,"seqs":798,"entrants":852,"cells":864,"outcomes":1015,"locators":1017,"hardware":1018,"wordings":1019,"notes":1020},"madicp2024-table-ii","madicp2024:Table II","madicp2024","Ferrari et al., 2024",[792,794,796],{"label":793,"unit":151,"statistic":152,"alignment":153},"RPE [%] (segments 100-800 m)",{"label":795,"unit":151,"statistic":152,"alignment":153},"RPE [%] (segments 10-80 m)",{"label":797,"unit":151,"statistic":152,"alignment":153},"RPE [%] (segments mixed)",[799,803,807,811,813,816,818,820,823,825,828,831,834,836,839,840,844,846,848],{"dataset":800,"sequence":801,"environment":802},"KITTI","KITTI avg (Seq. 00-10)","car, urban and highway (Velodyne HDL-64)",{"dataset":804,"sequence":805,"environment":806},"MulRan","MulRan avg (12 sequences)","car, urban (Ouster OS1-64)",{"dataset":808,"sequence":809,"environment":810},"Newer College NC0 (OS0-128)","cat. easy","handheld, catacombs",{"dataset":808,"sequence":812,"environment":810},"cat. med.",{"dataset":808,"sequence":814,"environment":815},"cloister","handheld campus",{"dataset":808,"sequence":817,"environment":815},"m. easy",{"dataset":808,"sequence":819,"environment":815},"m. med.",{"dataset":808,"sequence":821,"environment":822},"quad easy","handheld campus quad",{"dataset":808,"sequence":824,"environment":822},"quad med.",{"dataset":808,"sequence":826,"environment":827},"stairs","handheld, indoor stairwell",{"dataset":808,"sequence":829,"environment":830},"avg","handheld",{"dataset":832,"sequence":833,"environment":815},"Newer College NC1 (OS1-64)","short",{"dataset":832,"sequence":835,"environment":815},"long",{"dataset":832,"sequence":837,"environment":838},"parkland","handheld park",{"dataset":832,"sequence":829,"environment":830},{"dataset":841,"sequence":842,"environment":843},"Hilti 2021 (OS0-64)","drone","quadrotor",{"dataset":841,"sequence":845,"environment":830},"lab",{"dataset":841,"sequence":829,"environment":847},"quadrotor and handheld",{"dataset":849,"sequence":850,"environment":851},"all datasets","tot avg","mixed",[853,855,858,861,862],{"name":854,"methodId":789,"linkable":197,"proposed":197,"self":82},"Ours (MAD-ICP)",{"name":856,"methodId":857,"linkable":197,"proposed":82,"self":82},"KISS-ICP","kissicp2023",{"name":859,"methodId":860,"linkable":197,"proposed":82,"self":82},"F-LOAM","floam2021",{"name":7,"methodId":5,"linkable":197,"proposed":82,"self":197},{"name":863,"methodId":470,"linkable":197,"proposed":82,"self":82},"CT-ICP",[865,866,867,868,869,870,872,874,876,878,879,881,883,885,886,887,888,890,892,893,895,896,898,900,902,903,904,905,906,907,908,909,910,912,913,914,916,917,919,920,922,924,926,928,930,932,934,936,938,939,941,943,945,947,949,951,952,953,955,956,957,959,961,963,965,966,967,968,970,972,974,975,977,978,980,981,983,985,987,989,991,992,994,995,996,997,999,1001,1002,1004,1005,1007,1009,1011,1013],[232,232,232,279,234,232,234,234,232],[236,232,232,306,234,232,234,234,232],[239,232,232,301,234,232,234,234,232],[242,232,232,451,234,232,234,234,232],[245,232,232,281,234,232,234,234,232],[232,232,236,871,234,232,234,234,232],4.34,[236,232,236,873,234,232,234,234,232],3.82,[239,232,236,875,234,232,234,234,232],6.97,[242,232,236,877,234,232,234,234,232],4.93,[245,232,236,661,234,232,234,234,232],[232,236,239,880,234,232,234,234,232],1.16,[236,236,239,882,234,232,234,234,232],2.06,[239,236,239,884,234,232,234,234,232],1.36,[242,236,239,239,234,232,234,234,232],[245,236,239,258,234,232,234,234,232],[232,236,242,371,234,232,234,234,232],[236,236,242,889,232,232,234,234,232],5.7,[239,236,242,891,232,232,234,234,232],11.02,[242,236,242,242,234,232,234,234,232],[245,236,242,894,232,232,234,234,232],10.42,[232,236,245,371,234,232,234,234,232],[236,236,245,897,234,232,234,234,232],3.39,[239,236,245,899,234,232,234,234,232],5.46,[242,236,245,901,234,232,234,234,232],1.45,[245,236,245,656,234,232,234,234,232],[232,236,248,347,234,232,234,234,232],[236,236,248,686,234,232,234,234,232],[239,236,248,365,234,232,234,234,232],[242,236,248,277,234,232,234,234,232],[245,236,248,618,234,232,234,234,232],[232,236,251,377,234,232,234,234,232],[236,236,251,375,234,232,234,234,232],[239,236,251,911,232,232,234,234,232],18.05,[242,236,251,361,234,232,234,234,232],[245,236,251,249,234,232,234,234,232],[232,236,254,915,234,232,234,234,232],2.64,[236,236,254,915,234,232,234,234,232],[239,236,254,918,234,232,234,234,232],3.02,[242,236,254,743,234,232,234,234,232],[245,236,254,921,234,232,234,234,232],2.73,[232,236,257,923,234,232,234,234,232],5.51,[236,236,257,925,234,232,234,234,232],5.98,[239,236,257,927,232,232,234,234,232],31.06,[242,236,257,929,234,232,234,234,232],5.71,[245,236,257,931,234,232,234,234,232],5.65,[232,236,260,933,234,232,234,234,232],0.91,[236,236,260,935,232,232,234,234,232],17903.09,[239,236,260,937,234,232,234,234,232],8.04,[242,236,260,745,232,232,234,234,232],[245,236,260,940,232,232,234,234,232],29.69,[232,236,263,942,234,232,234,234,232],1.84,[236,236,263,944,234,232,234,234,232],2.6,[239,236,263,946,234,232,234,234,232],3.34,[242,236,263,948,234,232,234,234,232],2.24,[245,236,263,950,234,232,234,234,232],2.02,[232,232,266,243,234,232,234,234,232],[236,232,266,664,234,232,234,234,232],[239,232,266,954,234,232,234,234,232],1.56,[242,232,266,585,234,232,234,234,232],[245,232,266,387,234,232,234,234,232],[232,232,269,958,234,232,234,234,232],0.96,[236,232,269,960,234,232,234,234,232],0.95,[239,232,269,962,232,232,234,234,232],9.87,[242,232,269,964,234,232,234,234,232],1.17,[245,232,269,327,234,232,234,234,232],[232,232,274,441,234,232,234,234,232],[236,232,274,728,234,232,234,234,232],[239,232,274,969,232,232,234,234,232],14.79,[242,232,274,971,232,232,234,234,232],6.11,[245,232,274,973,234,232,234,234,232],1.05,[232,232,450,327,234,232,234,234,232],[236,232,450,976,234,232,234,234,232],0.94,[239,232,450,954,234,232,234,234,232],[242,232,450,979,234,232,234,234,232],1.21,[245,232,450,672,234,232,234,234,232],[232,236,456,982,234,232,234,234,232],3.86,[236,236,456,984,234,232,234,234,232],4.02,[239,236,456,986,232,232,234,234,232],19.49,[242,236,456,988,234,232,234,234,232],4.22,[245,236,456,990,234,232,234,234,232],3.87,[232,236,631,561,234,232,234,234,232],[236,236,631,993,234,232,234,234,232],2.13,[239,236,631,677,234,232,234,234,232],[242,236,631,601,234,232,234,234,232],[245,236,631,561,234,232,234,234,232],[232,236,633,998,234,232,234,234,232],2.79,[236,236,633,1000,234,232,234,234,232],3.37,[239,236,633,677,234,232,234,234,232],[242,236,633,1003,234,232,234,234,232],3.26,[245,236,633,998,234,232,234,234,232],[232,239,636,1006,234,232,234,234,232],2.14,[236,239,636,1008,234,232,234,234,232],2.25,[239,239,636,1010,234,232,234,234,232],2.9,[242,239,636,1012,234,232,234,234,232],2.45,[245,239,636,1014,234,232,234,234,232],2.5,[1016],"failed (red in Table II: wrong registration produced local-map duplicates and a trajectory discontinuity; excluded from averages)",[146],[],[],[1021],"KITTI benchmark RPE (%); segments 100-800 m for KITTI, MulRan and NC1, 10-80 m for NC0 and Hilti; averages exclude failures; per-sequence KITTI 00-10 and MulRan rows omitted (averages kept); values identical in arXiv v1 and version of record",{"slug":1023,"group":1024,"sourceId":857,"sourceLabel":1025,"table":1026,"selfRows":631,"metrics":1027,"seqs":1038,"entrants":1048,"cells":1058,"outcomes":1160,"locators":1161,"hardware":1162,"wordings":1163,"notes":1164},"kissicp2023-table-iii","kissicp2023:Table III","Vizzo et al., 2023","Table III",[1028,1030,1032,1035],{"label":1029,"unit":151,"statistic":152,"alignment":153},"Avg. tra. (KITTI relative translational error)",{"label":1031,"unit":153,"statistic":152,"alignment":153},"Avg. rot. (KITTI relative rotational error; unit not stated)",{"label":1033,"unit":1034,"statistic":153,"alignment":153},"ATE tra. [m]","m",{"label":1036,"unit":1037,"statistic":153,"alignment":153},"ATE rot. [rad]","rad",[1039,1042,1044,1046],{"dataset":804,"sequence":1040,"environment":1041},"KAIST","urban driving",{"dataset":804,"sequence":1043,"environment":1041},"DCC",{"dataset":804,"sequence":1045,"environment":1041},"Riverside",{"dataset":804,"sequence":1047,"environment":1041},"Sejong* (asterisk not explained in the text read; SuMa not listed)",[1049,1051,1054,1056],{"name":1050,"methodId":5,"linkable":197,"proposed":82,"self":197},"MULLS [21]",{"name":1052,"methodId":1053,"linkable":197,"proposed":82,"self":82},"SuMa [1]","suma2018",{"name":1055,"methodId":860,"linkable":197,"proposed":82,"self":82},"F-LOAM [33]",{"name":1057,"methodId":857,"linkable":197,"proposed":197,"self":82},"Ours (KISS-ICP)",[1059,1061,1062,1064,1066,1068,1070,1072,1074,1076,1078,1080,1082,1084,1085,1087,1088,1090,1091,1093,1095,1097,1099,1101,1102,1104,1106,1108,1109,1111,1112,1114,1115,1117,1119,1121,1122,1124,1125,1127,1128,1130,1131,1133,1135,1137,1138,1140,1141,1143,1144,1146,1147,1149,1151,1153,1154,1156,1157,1159],[232,232,232,1060,234,232,234,234,232],2.94,[232,236,232,243,234,232,234,234,232],[232,239,232,1063,234,232,234,234,232],37.24,[232,242,232,1065,234,232,234,234,232],0.11,[236,232,232,1067,234,232,234,234,232],5.59,[236,236,232,1069,234,232,234,234,232],1.73,[236,239,232,1071,234,232,234,234,232],43.61,[236,242,232,1073,234,232,234,234,232],0.14,[239,232,232,1075,234,232,234,234,232],3.43,[239,236,232,1077,234,232,234,234,232],0.99,[239,239,232,1079,234,232,234,234,232],46.17,[239,242,232,1081,234,232,234,234,232],0.15,[242,232,232,1083,234,232,234,234,232],2.28,[242,236,232,283,234,232,234,234,232],[242,239,232,1086,234,232,234,234,232],17.4,[242,242,232,708,234,232,234,234,232],[232,232,236,1089,234,232,234,234,232],2.96,[232,236,236,595,234,232,234,234,232],[232,239,236,1092,234,232,234,234,232],38.35,[232,242,236,1094,234,232,234,234,232],0.12,[236,232,236,1096,234,232,234,234,232],5.2,[236,236,236,1098,234,232,234,234,232],1.71,[236,239,236,1100,234,232,234,234,232],36.22,[236,242,236,1065,234,232,234,234,232],[239,232,236,1103,234,232,234,234,232],3.83,[239,236,236,1105,234,232,234,234,232],1.14,[239,239,236,1107,234,232,234,234,232],42.7,[239,242,236,272,234,232,234,234,232],[242,232,236,1110,234,232,234,234,232],2.34,[242,236,236,338,234,232,234,234,232],[242,239,236,1113,234,232,234,234,232],15.16,[242,242,236,448,234,232,234,234,232],[232,232,239,1116,234,232,234,234,232],5.42,[232,236,239,1118,234,232,234,234,232],2.21,[232,239,239,1120,234,232,234,234,232],91.16,[232,242,239,323,234,232,234,234,232],[236,232,239,1123,234,232,234,234,232],13.86,[236,236,239,993,234,232,234,234,232],[236,239,239,1126,234,232,234,234,232],227.24,[236,242,239,329,234,232,234,234,232],[239,232,239,1129,234,232,234,234,232],5.47,[239,236,239,608,234,232,234,234,232],[239,239,239,1132,234,232,234,234,232],138.09,[239,242,239,1134,234,232,234,234,232],0.22,[242,232,239,1136,234,232,234,234,232],2.89,[242,236,239,338,234,232,234,234,232],[242,239,239,1139,234,232,234,234,232],49.02,[242,242,239,423,234,232,234,234,232],[232,232,242,1142,234,232,234,234,232],5.93,[232,236,242,267,234,232,234,234,232],[232,239,242,1145,234,232,234,234,232],2151,[232,242,242,415,234,232,234,234,232],[239,232,242,1148,234,232,234,234,232],7.87,[239,236,242,1150,234,232,234,234,232],1.2,[239,239,242,1152,234,232,234,234,232],3448.97,[239,242,242,279,234,232,234,234,232],[242,232,242,1155,234,232,234,234,232],4.69,[242,236,242,357,234,232,234,234,232],[242,239,242,1158,234,232,234,234,232],1369.54,[242,242,242,285,234,232,234,234,232],[],[1026],[],[],[1165],"MulRan; values are averages over the three runs per sequence; CT-ICP not evaluated because it lacks MulRan support",[1167,1173,1178,1183,1189,1196,1202,1206,1211,1215,1219,1224,1230,1235,1241,1245,1249,1254,1258,1262,1268,1273,1279,1285,1289,1294],{"group":1168,"slug":1169,"sourceLabel":1170,"table":146,"selfRows":274,"datasets":1171},"yuan2022voxelmap:Table II","yuan2022voxelmap-table-ii","Yuan et al., 2022",[1172],"KITTI odometry (training)",{"group":1174,"slug":1175,"sourceLabel":1176,"table":146,"selfRows":269,"datasets":1177},"hba2023:Table II","hba2023-table-ii","Liu et al., 2023b",[800],{"group":1179,"slug":1180,"sourceLabel":1176,"table":1181,"selfRows":269,"datasets":1182},"hba2023:Table V","hba2023-table-v","Table V",[800],{"group":1184,"slug":1185,"sourceLabel":1186,"table":1026,"selfRows":260,"datasets":1187},"kissslam2025:Table III","kissslam2025-table-iii","Guadagnino et al., 2025a",[1188],"HeLiPR",{"group":1190,"slug":1191,"sourceLabel":1192,"table":1193,"selfRows":257,"datasets":1194},"pinslam2024:Table VII","pinslam2024-table-vii","Pan et al., 2024","Table VII",[1195],"Newer College",{"group":1197,"slug":1198,"sourceLabel":1186,"table":1199,"selfRows":254,"datasets":1200},"kissslam2025:Table IV","kissslam2025-table-iv","Table IV",[1201],"Apollo",{"group":1203,"slug":1204,"sourceLabel":1186,"table":1181,"selfRows":254,"datasets":1205},"kissslam2025:Table V","kissslam2025-table-v",[1195],{"group":1207,"slug":1208,"sourceLabel":1209,"table":146,"selfRows":245,"datasets":1210},"genzicp2025:Table II","genzicp2025-table-ii","Lee et al., 2025a",[804],{"group":1212,"slug":1213,"sourceLabel":1025,"table":146,"selfRows":245,"datasets":1214},"kissicp2023:Table II","kissicp2023-table-ii",[91],{"group":1216,"slug":1217,"sourceLabel":1186,"table":146,"selfRows":245,"datasets":1218},"kissslam2025:Table II","kissslam2025-table-ii",[804],{"group":1220,"slug":1221,"sourceLabel":6,"table":1181,"selfRows":245,"datasets":1222},"mulls2021:Table V","mulls2021-table-v",[1223],"not_reported (typical frame)",{"group":1225,"slug":1226,"sourceLabel":1227,"table":1026,"selfRows":245,"datasets":1228},"pings2025:Table III","pings2025-table-iii","Pan et al., 2025",[1229],"in-house car dataset",{"group":1231,"slug":1232,"sourceLabel":1025,"table":1199,"selfRows":242,"datasets":1233},"kissicp2023:Table IV","kissicp2023-table-iv",[1234,1195],"NCLT",{"group":1236,"slug":1237,"sourceLabel":1238,"table":1239,"selfRows":242,"datasets":1240},"molalo2025:Table 3","molalo2025-table-3","Blanco-Claraco, 2025","Table 3",[91],{"group":1242,"slug":1243,"sourceLabel":1209,"table":472,"selfRows":239,"datasets":1244},"genzicp2025:Table I","genzicp2025-table-i",[1195],{"group":1246,"slug":1247,"sourceLabel":1209,"table":1026,"selfRows":239,"datasets":1248},"genzicp2025:Table III","genzicp2025-table-iii",[91],{"group":1250,"slug":1251,"sourceLabel":1025,"table":1181,"selfRows":239,"datasets":1252},"kissicp2023:Table V","kissicp2023-table-v",[1253],"KITTI raw",{"group":1255,"slug":1256,"sourceLabel":1192,"table":1199,"selfRows":239,"datasets":1257},"pinslam2024:Table IV","pinslam2024-table-iv",[91],{"group":1259,"slug":1260,"sourceLabel":1170,"table":1026,"selfRows":239,"datasets":1261},"yuan2022voxelmap:Table III","yuan2022voxelmap-table-iii",[1172],{"group":1263,"slug":1264,"sourceLabel":1265,"table":1266,"selfRows":239,"datasets":1267},"zhang2024_3dlidarslam_survey:Table 8","zhang2024-3dlidarslam-survey-table-8","Zhang et al., 2024a","Table 8",[91],{"group":1269,"slug":1270,"sourceLabel":1265,"table":1271,"selfRows":239,"datasets":1272},"zhang2024_3dlidarslam_survey:Table 9","zhang2024-3dlidarslam-survey-table-9","Table 9",[91],{"group":1274,"slug":1275,"sourceLabel":1276,"table":1277,"selfRows":236,"datasets":1278},"balm2_2023:Supplementary Table VII","balm2-2023-supplementary-table-vii","Liu et al., 2023a","Supplementary Table VII",[91],{"group":1280,"slug":1281,"sourceLabel":1282,"table":1239,"selfRows":236,"datasets":1283},"ghadimzadeh2025slamnde:Table 3","ghadimzadeh2025slamnde-table-3","Ghadimzadeh Alamdari et al., 2025",[1284],"Luleå SubT tunnel dataset (Koval et al. 2022)",{"group":1286,"slug":1287,"sourceLabel":1186,"table":472,"selfRows":236,"datasets":1288},"kissslam2025:Table I","kissslam2025-table-i",[1234],{"group":1290,"slug":1291,"sourceLabel":790,"table":1026,"selfRows":236,"datasets":1292},"madicp2024:Table III","madicp2024-table-iii",[1293],"all Table II datasets",{"group":1295,"slug":1296,"sourceLabel":6,"table":1297,"selfRows":236,"datasets":1298},"mulls2021:Text Sec. IV-B1","mulls2021-text-sec-iv-b1","Text Sec. IV-B1",[1299],"ISPRS MIMAP (backpack, VLP32C and HDL32E)",1790510653925]