[{"data":1,"prerenderedAt":1223},["ShallowReactive",2],{"method-cticp2022":3},{"method":4,"reference":60,"equipment":82,"figures":128,"results":170},{"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":27,"sensors":34,"platform":36,"estimator":40,"association":41,"timeModel":42,"deskew":43,"loopClosure":44,"globalOptimization":45,"mapRepresentation":46,"prior":47,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"cticp2022","Dellenbach et al., 2022","CT-ICP","CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure",2022,"recent","C05","full_slam_with_global_correction","CT-ICP 以每次掃描的起始與結束兩個位姿參數化掃描內的連續時間軌跡，在點到平面 ICP 中同時估計扭曲，使掃描可「彈性」變形；掃描之間允許不連續，並以位置一致與等速兩項約束抑制過度跳動。地圖為稀疏體素中的稠密點雲。作者再以局部地圖投影成高程影像的迴圈偵測與 g2o 位姿圖構成完整 SLAM，但此迴圈方法假設運動大致在平面上。","LiDAR-only odometry that estimates begin and end poses per scan inside point-to-plane ICP (continuous within, discontinuous between scans), completed into SLAM with elevation-image loop detection and a pose graph.","full_text_reviewed","peer_reviewed_published","main_body","not_reported（測試資料為道路、校園、手持與 Segway 資料集，未含施工現場）",[20,21],"public_benchmark","simulation",[23,24,25,26],"Elastic formulation handles raw (not pre-corrected) scans; replacing it by constant-velocity pre-deskew and a single pose raised KITTI-raw RTE from 0.55% to 0.79% (Sec. V-B)","Robust profile retries registration with conservative parameters on hard cases (Sec. III-B)","Lowest average RTE among compared odometries on all raw-scan datasets, e.g. KITTI-CARLA AVG 0.09% versus 0.81% for the best other method (Table I; Sec. V-B)","Loop closure reduced mean ATE on KITTI-360 00 from 29.87 m to 1.07 m and on KITTI-raw 00 from 6.22 m to 0.66 m (Table II)",[28,29,30,31,32,33],"Loop closure requires mostly planar sensor motion and ground-aligned extrinsic calibration (Sec. IV)","Robustness gain from the robust profile increases runtime (Sec. III-B)","KISS-ICP authors could not reproduce CT-ICP NCLT results (KISS-ICP, Sec. IV-C)","Traj-LO authors report CT-ICP failing on half of NTU VIRAL aerial sequences, attributing it to begin-end linear interpolation under rapid motion (Traj-LO, Sec. IV-B)","Loop closure did not reduce mean ATE on KITTI-CARLA Town01 (0.21 to 0.26 m) or NCD 01_short_experiment (0.22 to 0.36 m) (Table II; table-derived)","Authors list extending the continuous-time formulation to the back end as future work (Sec. VI)",[35],"3D LiDAR only (xyz plus per-point timestamps)",[37,38,39,21],"vehicle","handheld","wheeled UGV","scan-to-map continuous-time ICP (robust loss, iterative least squares) over two poses per scan with location-consistency and constant-velocity regularizers; g2o pose-graph back-end","point-to-plane to dense local map, normals and planarity weights from k=20 neighbours in 27 surrounding voxels","continuous within a scan (linear translation and slerp rotation between begin and end poses), discontinuous between scans","elastic: scan distortion estimated jointly with registration","elevation images of aggregated local maps with rotation-invariant 2D features, RANSAC and ICP refinement; requires mostly planar ground motion and z-axis aligned with ground normal","pose graph (g2o) optimized only when a loop constraint is detected","dense point cloud in a sparse voxel grid (max 20 points per voxel, 10 cm minimum spacing; voxel 1.0 m driving, 0.8 m high-frequency motion)","none","trajectory and aggregated point clouds (Fig. 2); export format not_reported","single-thread CPU by default (model not reported); average 60 ms per scan on the KITTI leaderboard submission (abstract); Table I average time per scan 60 to 80 ms with the Driving profile, but 430 ms on NCD and 180 ms on NCLT with the High-Frequency Motion (robust) profile; loop-closure elevation matching 1.1 s and PGO 1.2 s on average when triggered (Sec. V-C)","https:\u002F\u002Fgithub.com\u002Fjedeschaud\u002Fct_icp","MIT (LICENSE file checked)",[53,57],{"relation":54,"title":55,"doi_or_url":56},"preprint","CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure (arXiv v2)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.12979",{"relation":58,"title":59,"doi_or_url":50},"code_release","jedeschaud\u002Fct_icp; loop closure integrated in Kitware\u002FpyLiDAR-SLAM",{"id":5,"kind":61,"shortName":7,"title":8,"authors":62,"year":9,"venue":67,"venueType":68,"publisher":69,"volumeIssuePages":70,"doi":71,"arxivId":72,"url":73,"firstPublicDate":74,"publicationStatus":16,"metadataStatus":75,"fulltextStatus":15,"era":10,"classicReason":76,"codeUrl":50,"cluster":11,"topics":77,"mdpi":78,"verification":79,"label":6,"fulltextRoute":80,"versionRead":81,"addedByCensus":78},"method",[63,64,65,66],"Pierre Dellenbach","Jean-Emmanuel Deschaud","Bastien Jacquet","François Goulette","2022 International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 5580-5586","10.1109\u002Ficra46639.2022.9811849","2109.12979","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FICRA46639.2022.9811849","2021-09-27","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v2 (2022-02-24); ICRA 2022 version of record (pp. 5580-5586) not read",[83,91,97,101,106,109,113,117,121],{"category":84,"model":85,"canonical":86,"role":87,"dataset":88,"specs":89,"locator":90},"lidar","Velodyne HDL64","Velodyne HDL-64E","dataset sensor","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","Sec. V-A-1",{"category":92,"model":93,"canonical":93,"role":94,"dataset":95,"specs":96,"locator":90},"gnss","GPS\u002FIMU (model not reported)","reference or ground truth","KITTI","ground-truth poses of KITTI",{"category":84,"model":98,"canonical":98,"role":87,"dataset":99,"specs":100,"locator":90},"simulated 64-channel LiDAR","KITTI-CARLA","CARLA simulator, precise ground truth and timestamps",{"category":84,"model":102,"canonical":103,"role":87,"dataset":104,"specs":105,"locator":90},"Velodyne HDL32","Velodyne HDL-32E","ParisLuco","mounted vertically on the authors' vehicle; 4 km, 12751 scans in central Paris",{"category":92,"model":107,"canonical":107,"role":94,"dataset":104,"specs":108,"locator":90},"GPS\u002FIMU (post-processed; model not reported)","ground truth translations only",{"category":84,"model":102,"canonical":103,"role":87,"dataset":110,"specs":111,"locator":112},"NCLT","mounted on a two-wheeled Segway","Sec. V-A-2",{"category":114,"model":115,"canonical":115,"role":87,"dataset":110,"specs":116,"locator":112},"platform","two-wheeled Segway","abrupt rotations about the LiDAR axis",{"category":84,"model":118,"canonical":118,"role":87,"dataset":119,"specs":120,"locator":112},"Ouster 64-channel LiDAR","Newer College Dataset (NCD)","handheld, mounted on a stick",{"category":122,"model":123,"canonical":123,"role":124,"dataset":125,"specs":126,"locator":127},"compute","CPU, single thread (model not reported)","compute for runtime",null,"not_reported","Abstract; Sec. III-A",[129,142,152,161],{"refId":5,"refLabel":6,"fig":130,"whatZh":131,"license":132,"licenseUrl":133,"sourceUrl":134,"src":135,"width":136,"height":137,"thumb":138,"thumbWidth":139,"thumbHeight":140,"modified":141},"Fig. 1 (top)","單次 LiDAR 掃描依各點時間戳記著色，經掃描起點與終點兩個位姿之間的彈性變形後對齊白色地圖點；下方軌跡示意見下一筆","CC BY 4.0","https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2109.12979v2\u002Fimages\u002Ffig1.jpg","\u002Ffigure-files\u002Fcticp2022\u002Ffig-1-top.webp",1400,974,"\u002Ffigure-files\u002Fcticp2022\u002Ffig-1-top.thumb.webp",480,334,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":143,"whatZh":144,"license":132,"licenseUrl":133,"sourceUrl":145,"src":146,"width":147,"height":148,"thumb":149,"thumbWidth":139,"thumbHeight":150,"modified":151},"Fig. 1 (bottom)","CT-ICP 軌跡參數化示意：每次掃描以起訖兩位姿內插，相鄰掃描之間允許不連續","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2109.12979v2\u002Fimages\u002Fschema_v4.png","\u002Ffigure-files\u002Fcticp2022\u002Ffig-1-bottom.webp",960,300,"\u002Ffigure-files\u002Fcticp2022\u002Ffig-1-bottom.thumb.webp",150,"converted to WebP",{"refId":5,"refLabel":6,"fig":153,"whatZh":154,"license":132,"licenseUrl":133,"sourceUrl":155,"src":156,"width":157,"height":158,"thumb":159,"thumbWidth":139,"thumbHeight":160,"modified":151},"Fig. 2","CT-ICP 在 NCLT、KITTI-CARLA、Newer College 與 ParisLuco 產生的累積點雲；連結為 Newer College 手持資料的子圖","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2109.12979v2\u002Fimages\u002Fncd_aggregated.png","\u002Ffigure-files\u002Fcticp2022\u002Ffig-2.webp",986,640,"\u002Ffigure-files\u002Fcticp2022\u002Ffig-2.thumb.webp",312,{"refId":5,"refLabel":6,"fig":162,"whatZh":163,"license":132,"licenseUrl":133,"sourceUrl":164,"src":165,"width":166,"height":167,"thumb":168,"thumbWidth":139,"thumbHeight":169,"modified":151},"Fig. 4","KITTI-360 序列 00 的迴圈閉合結果：局部地圖高程影像、修正前後軌跡與偵測到的迴圈約束","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2109.12979v2\u002Fimages\u002Floop_closure.jpg","\u002Ffigure-files\u002Fcticp2022\u002Ffig-4.webp",1260,1008,"\u002Ffigure-files\u002Fcticp2022\u002Ffig-4.thumb.webp",384,{"totalRows":171,"groupCount":172,"groups":173,"others":1060},204,35,[174,598,845,910],{"slug":175,"group":176,"sourceId":5,"sourceLabel":6,"table":177,"selfRows":178,"metrics":179,"seqs":187,"entrants":263,"cells":274,"outcomes":584,"locators":585,"hardware":588,"wordings":591,"notes":592},"cticp2022-table-i","cticp2022:Table I","Table I",42,[180,184],{"label":181,"unit":182,"statistic":183,"alignment":47},"Relative Translation Error (RTE)","%","mean",{"label":185,"unit":186,"statistic":183,"alignment":76},"Delta T, average running time per scan","ms",[188,192,194,197,199,201,203,205,207,209,211,213,215,216,217,220,221,223,224,225,226,227,228,229,230,231,234,236,238,240,242,244,246,247,248,251,252,255,257,258,259,262],{"dataset":189,"sequence":190,"environment":191},"KITTI-corrected (motion-corrected odometry benchmark scans)","AVG","vehicle, urban, highway and country roads",{"dataset":189,"sequence":193,"environment":191},"all sequences (average time per scan)",{"dataset":195,"sequence":196,"environment":191},"KITTI-raw","00",{"dataset":195,"sequence":198,"environment":191},"01",{"dataset":195,"sequence":200,"environment":191},"02",{"dataset":195,"sequence":202,"environment":191},"04",{"dataset":195,"sequence":204,"environment":191},"05",{"dataset":195,"sequence":206,"environment":191},"06",{"dataset":195,"sequence":208,"environment":191},"07",{"dataset":195,"sequence":210,"environment":191},"08",{"dataset":195,"sequence":212,"environment":191},"09",{"dataset":195,"sequence":214,"environment":191},"10",{"dataset":195,"sequence":190,"environment":191},{"dataset":195,"sequence":193,"environment":191},{"dataset":218,"sequence":196,"environment":219},"KITTI-360","vehicle, same acquisition setup and environment as KITTI (Sec. 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identical)","two-wheeled Segway, University of Michigan campus",{"dataset":110,"sequence":193,"environment":261},[264,266,270,272],{"name":265,"methodId":125,"linkable":78,"proposed":78,"self":78},"IMLS-SLAM [15]",{"name":267,"methodId":268,"linkable":269,"proposed":78,"self":78},"MULLS [4]","mulls2021",true,{"name":271,"methodId":125,"linkable":78,"proposed":78,"self":78},"pyLiDAR F2M [33]",{"name":273,"methodId":5,"linkable":269,"proposed":269,"self":269},"CT-ICP (ours)",[275,279,282,283,285,288,290,292,294,296,298,301,304,307,310,313,316,319,322,325,328,330,332,334,336,338,340,342,344,346,347,349,350,352,354,356,358,360,361,363,364,365,367,369,371,372,374,375,377,379,381,383,385,387,389,390,392,395,398,401,404,406,408,411,413,415,418,421,422,424,426,428,430,432,434,435,437,438,440,442,444,446,447,449,451,452,453,455,456,458,460,462,464,465,467,469,470,471,472,473,475,478,481,484,487,490,493,496,498,501,502,504,505,507,509,510,511,513,514,516,518,520,522,524,526,528,530,531,532,534,535,536,538,539,540,542,543,545,547,549,551,552,554,556,557,560,563,565,568,569,571,572,574,577,580,582],[276,276,276,277,278,276,278,278,276],0,0.55,-1,[276,280,280,281,278,276,276,278,276],1,1250,[280,276,276,277,278,276,278,278,276],[280,280,280,284,278,276,276,278,276],80,[286,276,276,287,278,276,278,278,276],2,0.53,[286,280,280,289,278,276,276,278,276],175,[291,276,276,287,278,276,278,278,276],3,[291,280,280,293,278,276,280,278,276],60,[276,276,286,295,278,276,278,278,280],0.79,[276,276,291,297,278,276,278,278,280],0.86,[276,276,299,300,278,276,278,278,280],4,0.76,[276,276,302,303,278,276,278,278,280],5,0.51,[276,276,305,306,278,276,278,278,280],6,0.48,[276,276,308,309,278,276,278,278,280],7,0.62,[276,276,311,312,278,276,278,278,280],8,0.67,[276,276,314,315,278,276,278,278,280],9,0.97,[276,276,317,318,278,276,278,278,280],10,0.7,[276,276,320,321,278,276,278,278,280],11,0.75,[276,276,323,324,278,276,278,278,280],12,0.71,[276,280,326,327,278,276,276,278,280],13,1070,[280,276,286,329,278,276,278,278,280],1.43,[280,276,291,331,278,276,278,278,280],3.12,[280,276,299,333,278,276,278,278,280],1.01,[280,276,302,335,278,276,278,278,280],0.57,[280,276,305,337,278,276,278,278,280],1.93,[280,276,308,339,278,276,278,278,280],1.6,[280,276,311,341,278,276,278,278,280],0.69,[280,276,314,343,278,276,278,278,280],1.28,[280,276,317,345,278,276,278,278,280],1.49,[280,276,320,324,278,276,278,278,280],[280,276,323,348,278,276,278,278,280],1.41,[280,280,326,284,278,276,276,278,280],[286,276,286,351,278,276,278,278,280],2.2,[286,276,291,353,278,276,278,278,280],0.98,[286,276,299,355,278,276,278,278,280],1.55,[286,276,302,357,278,276,278,278,280],0.45,[286,276,305,359,278,276,278,278,280],1.46,[286,276,308,341,278,276,278,278,280],[286,276,311,362,278,276,278,278,280],1.72,[286,276,314,339,278,276,278,278,280],[286,276,317,343,278,276,278,278,280],[286,276,320,366,278,276,278,278,280],1.18,[286,276,323,368,278,276,278,278,280],1.61,[286,280,326,370,278,276,276,278,280],530,[291,276,286,303,278,276,278,278,280],[291,276,291,373,278,276,278,278,280],0.81,[291,276,299,277,278,276,278,278,280],[291,276,302,376,278,276,278,278,280],0.43,[291,276,305,378,278,276,278,278,280],0.27,[291,276,308,380,278,276,278,278,280],0.28,[291,276,311,382,278,276,278,278,280],0.35,[291,276,314,384,278,276,278,278,280],0.8,[291,276,317,386,278,276,278,278,280],0.47,[291,276,320,388,278,276,278,278,280],0.49,[291,276,323,277,278,276,278,278,280],[291,280,326,391,278,276,280,278,280],65,[276,276,393,394,278,276,278,278,286],14,0.65,[276,276,396,397,278,276,278,278,286],15,0.63,[276,276,399,400,278,276,278,278,286],16,0.64,[276,276,402,403,278,276,278,278,286],17,0.89,[276,276,405,397,278,276,278,278,286],18,[276,276,407,318,278,276,278,278,286],19,[276,276,409,410,278,276,278,278,286],20,0.54,[276,276,412,312,278,276,278,278,286],21,[276,276,414,295,278,276,278,278,286],22,[276,276,416,417,278,276,278,278,286],23,0.68,[276,280,419,420,278,276,276,278,286],24,1060,[280,276,393,339,278,276,278,278,286],[280,276,396,423,278,276,278,278,286],1.29,[280,276,399,425,278,276,278,278,286],0.87,[280,276,402,427,278,276,278,278,286],1.64,[280,276,405,429,278,276,278,278,286],1.27,[280,276,407,431,278,276,278,278,286],1.48,[280,276,409,433,278,276,278,278,286],6.25,[280,276,412,343,278,276,278,278,286],[280,276,414,436,278,276,278,278,286],0.88,[280,276,416,355,278,276,278,278,286],[280,280,419,439,278,276,276,278,286],90,[286,276,393,441,278,276,278,278,286],1.79,[286,276,396,443,278,276,278,278,286],1.25,[286,276,399,445,278,276,278,278,286],0.9,[286,276,402,339,278,276,278,278,286],[286,276,405,448,278,276,278,278,286],1.26,[286,276,407,450,278,276,278,278,286],1.38,[286,276,409,341,278,276,278,278,286],[286,276,412,362,278,276,278,278,286],[286,276,414,454,278,276,278,278,286],1.39,[286,276,416,359,278,276,278,278,286],[286,280,419,457,278,276,276,278,286],475,[291,276,393,459,278,276,278,278,286],0.41,[291,276,396,461,278,276,278,278,286],0.38,[291,276,399,463,278,276,278,278,286],0.34,[291,276,402,394,278,276,278,278,286],[291,276,405,466,278,276,278,278,286],0.39,[291,276,407,468,278,276,278,278,286],0.42,[291,276,409,463,278,276,278,278,286],[291,276,412,357,278,276,278,278,286],[291,276,414,341,278,276,278,278,286],[291,276,416,357,278,276,278,278,286],[291,280,419,474,278,276,280,278,286],70,[276,276,476,477,278,276,278,278,291],25,0.03,[276,276,479,480,278,276,278,278,291],26,0.05,[276,276,482,483,278,276,278,278,291],27,0.16,[276,276,485,486,278,276,278,278,291],28,0.2,[276,276,488,489,278,276,278,278,291],29,0.06,[276,276,491,492,278,276,278,278,291],30,4.9,[276,276,494,495,278,276,278,278,291],31,0.25,[276,276,497,373,278,276,278,278,291],32,[276,280,499,500,278,276,276,278,291],33,780,[280,276,476,454,278,276,278,278,291],[280,276,479,503,278,276,278,278,291],0.77,[280,276,482,341,278,276,278,278,291],[280,276,485,506,278,276,278,278,291],1.24,[280,276,488,508,278,276,278,278,291],1.13,[280,276,491,353,278,276,278,278,291],[280,276,494,315,278,276,278,278,291],[280,276,497,512,278,276,278,278,291],1.04,[280,280,499,474,278,276,276,278,291],[286,276,476,515,278,276,278,278,291],16.25,[286,276,479,517,278,276,278,278,291],7.92,[286,276,482,519,278,276,278,278,291],37.16,[286,276,485,521,278,276,278,278,291],10.06,[286,276,488,523,278,276,278,278,291],9.11,[286,276,491,525,278,276,278,278,291],73.29,[286,276,494,527,278,276,278,278,291],2.69,[286,276,497,529,278,276,278,278,291],23.84,[286,280,499,370,278,276,276,278,291],[291,276,476,477,278,276,278,278,291],[291,276,479,533,278,276,278,278,291],0.04,[291,276,482,477,278,276,278,278,291],[291,276,485,477,278,276,278,278,291],[291,276,488,537,278,276,278,278,291],0.02,[291,276,491,533,278,276,278,278,291],[291,276,494,459,278,276,278,278,291],[291,276,497,541,278,276,278,278,291],0.09,[291,280,499,391,278,276,280,278,291],[276,276,544,448,278,276,278,278,291],34,[276,280,172,546,278,276,276,278,291],770,[280,276,544,548,278,276,278,278,291],3.03,[280,280,172,550,278,276,276,278,291],55,[286,276,544,492,278,276,278,278,291],[286,280,172,553,278,276,276,278,291],355,[291,276,544,555,278,276,278,278,291],1.11,[291,280,172,284,278,276,280,278,291],[286,276,558,559,278,280,278,278,299],36,1.37,[286,276,561,562,278,280,278,278,299],37,2.63,[286,276,564,351,278,280,278,278,299],38,[286,280,566,567,278,280,276,278,299],39,1065,[291,276,558,306,278,280,278,278,299],[291,276,561,570,278,280,278,278,299],0.58,[291,276,564,277,278,280,278,278,299],[291,280,566,573,278,280,280,278,299],430,[286,276,575,576,278,280,278,278,299],40,19.8,[286,280,578,579,278,280,276,278,299],41,340,[291,276,575,581,278,280,278,278,299],1.17,[291,280,578,583,278,280,280,278,299],180,[],[586,587],"Table I (Driving)","Table I (High-Frequency Motion)",[589,590],"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)",[],[593,594,595,596,597],"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","KITTI RTE (%) with the High-Frequency Motion profile; only pyLiDAR F2M compared; AVG over all segments",{"slug":599,"group":600,"sourceId":601,"sourceLabel":602,"table":603,"selfRows":407,"metrics":604,"seqs":611,"entrants":663,"cells":675,"outcomes":838,"locators":840,"hardware":841,"wordings":842,"notes":843},"madicp2024-table-ii","madicp2024:Table II","madicp2024","Ferrari et al., 2024","Table II",[605,607,609],{"label":606,"unit":182,"statistic":183,"alignment":126},"RPE [%] (segments 100-800 m)",{"label":608,"unit":182,"statistic":183,"alignment":126},"RPE [%] (segments 10-80 m)",{"label":610,"unit":182,"statistic":183,"alignment":126},"RPE [%] (segments mixed)",[612,615,619,623,625,628,630,632,635,637,640,642,645,647,650,651,655,657,659],{"dataset":95,"sequence":613,"environment":614},"KITTI avg (Seq. 00-10)","car, urban and highway (Velodyne HDL-64)",{"dataset":616,"sequence":617,"environment":618},"MulRan","MulRan avg (12 sequences)","car, urban (Ouster OS1-64)",{"dataset":620,"sequence":621,"environment":622},"Newer College NC0 (OS0-128)","cat. easy","handheld, catacombs",{"dataset":620,"sequence":624,"environment":622},"cat. med.",{"dataset":620,"sequence":626,"environment":627},"cloister","handheld campus",{"dataset":620,"sequence":629,"environment":627},"m. easy",{"dataset":620,"sequence":631,"environment":627},"m. med.",{"dataset":620,"sequence":633,"environment":634},"quad easy","handheld campus quad",{"dataset":620,"sequence":636,"environment":634},"quad med.",{"dataset":620,"sequence":638,"environment":639},"stairs","handheld, indoor stairwell",{"dataset":620,"sequence":641,"environment":38},"avg",{"dataset":643,"sequence":644,"environment":627},"Newer College NC1 (OS1-64)","short",{"dataset":643,"sequence":646,"environment":627},"long",{"dataset":643,"sequence":648,"environment":649},"parkland","handheld park",{"dataset":643,"sequence":641,"environment":38},{"dataset":652,"sequence":653,"environment":654},"Hilti 2021 (OS0-64)","drone","quadrotor",{"dataset":652,"sequence":656,"environment":38},"lab",{"dataset":652,"sequence":641,"environment":658},"quadrotor and handheld",{"dataset":660,"sequence":661,"environment":662},"all datasets","tot avg","mixed",[664,666,669,672,674],{"name":665,"methodId":601,"linkable":269,"proposed":269,"self":78},"Ours (MAD-ICP)",{"name":667,"methodId":668,"linkable":269,"proposed":78,"self":78},"KISS-ICP","kissicp2023",{"name":670,"methodId":671,"linkable":269,"proposed":78,"self":78},"F-LOAM","floam2021",{"name":673,"methodId":268,"linkable":269,"proposed":78,"self":78},"MULLS",{"name":7,"methodId":5,"linkable":269,"proposed":78,"self":269},[676,678,679,680,682,683,685,687,689,691,692,694,696,698,699,701,703,705,707,708,710,711,713,715,717,718,720,721,723,725,726,728,730,732,734,735,737,738,740,741,743,745,747,749,751,753,755,757,759,760,762,764,766,768,770,772,773,774,776,777,779,781,783,785,786,788,790,791,793,795,797,798,800,801,803,804,806,808,810,812,814,815,817,818,819,820,822,824,825,827,828,830,832,834,836],[276,276,276,677,278,276,278,278,276],0.82,[280,276,276,410,278,276,278,278,276],[286,276,276,443,278,276,278,278,276],[291,276,276,681,278,276,278,278,276],0.6,[299,276,276,287,278,276,278,278,276],[276,276,280,684,278,276,278,278,276],4.34,[280,276,280,686,278,276,278,278,276],3.82,[286,276,280,688,278,276,278,278,276],6.97,[291,276,280,690,278,276,278,278,276],4.93,[299,276,280,433,278,276,278,278,276],[276,280,286,693,278,276,278,278,276],1.16,[280,280,286,695,278,276,278,278,276],2.06,[286,280,286,697,278,276,278,278,276],1.36,[291,280,286,286,278,276,278,278,276],[299,280,286,700,278,276,278,278,276],1.12,[276,280,291,702,278,276,278,278,276],1.42,[280,280,291,704,276,276,278,278,276],5.7,[286,280,291,706,276,276,278,278,276],11.02,[291,280,291,291,278,276,278,278,276],[299,280,291,709,276,276,278,278,276],10.42,[276,280,299,702,278,276,278,278,276],[280,280,299,712,278,276,278,278,276],3.39,[286,280,299,714,278,276,278,278,276],5.46,[291,280,299,716,278,276,278,278,276],1.45,[299,280,299,427,278,276,278,278,276],[276,280,302,719,278,276,278,278,276],0.4,[280,280,302,459,278,276,278,278,276],[286,280,302,722,278,276,278,278,276],0.85,[291,280,302,724,278,276,278,278,276],0.5,[299,280,302,376,278,276,278,278,276],[276,280,305,727,278,276,278,278,276],0.56,[280,280,305,729,278,276,278,278,276],0.73,[286,280,305,731,276,276,278,278,276],18.05,[291,280,305,733,278,276,278,278,276],1.06,[299,280,305,335,278,276,278,278,276],[276,280,308,736,278,276,278,278,276],2.64,[280,280,308,736,278,276,278,278,276],[286,280,308,739,278,276,278,278,276],3.02,[291,280,308,527,278,276,278,278,276],[299,280,308,742,278,276,278,278,276],2.73,[276,280,311,744,278,276,278,278,276],5.51,[280,280,311,746,278,276,278,278,276],5.98,[286,280,311,748,276,276,278,278,276],31.06,[291,280,311,750,278,276,278,278,276],5.71,[299,280,311,752,278,276,278,278,276],5.65,[276,280,314,754,278,276,278,278,276],0.91,[280,280,314,756,276,276,278,278,276],17903.09,[286,280,314,758,278,276,278,278,276],8.04,[291,280,314,529,276,276,278,278,276],[299,280,314,761,276,276,278,278,276],29.69,[276,280,317,763,278,276,278,278,276],1.84,[280,280,317,765,278,276,278,278,276],2.6,[286,280,317,767,278,276,278,278,276],3.34,[291,280,317,769,278,276,278,278,276],2.24,[299,280,317,771,278,276,278,278,276],2.02,[276,276,320,297,278,276,278,278,276],[280,276,320,436,278,276,278,278,276],[286,276,320,775,278,276,278,278,276],1.56,[291,276,320,343,278,276,278,278,276],[299,276,320,778,278,276,278,278,276],0.83,[276,276,323,780,278,276,278,278,276],0.96,[280,276,323,782,278,276,278,278,276],0.95,[286,276,323,784,276,276,278,278,276],9.87,[291,276,323,581,278,276,278,278,276],[299,276,323,787,278,276,278,278,276],0.93,[276,276,326,789,278,276,278,278,276],1.03,[280,276,326,512,278,276,278,278,276],[286,276,326,792,276,276,278,278,276],14.79,[291,276,326,794,276,276,278,278,276],6.11,[299,276,326,796,278,276,278,278,276],1.05,[276,276,393,787,278,276,278,278,276],[280,276,393,799,278,276,278,278,276],0.94,[286,276,393,775,278,276,278,278,276],[291,276,393,802,278,276,278,278,276],1.21,[299,276,393,445,278,276,278,278,276],[276,280,396,805,278,276,278,278,276],3.86,[280,280,396,807,278,276,278,278,276],4.02,[286,280,396,809,276,276,278,278,276],19.49,[291,280,396,811,278,276,278,278,276],4.22,[299,280,396,813,278,276,278,278,276],3.87,[276,280,399,300,278,276,278,278,276],[280,280,399,816,278,276,278,278,276],2.13,[286,280,399,450,278,276,278,278,276],[291,280,399,359,278,276,278,278,276],[299,280,399,300,278,276,278,278,276],[276,280,402,821,278,276,278,278,276],2.79,[280,280,402,823,278,276,278,278,276],3.37,[286,280,402,450,278,276,278,278,276],[291,280,402,826,278,276,278,278,276],3.26,[299,280,402,821,278,276,278,278,276],[276,286,405,829,278,276,278,278,276],2.14,[280,286,405,831,278,276,278,278,276],2.25,[286,286,405,833,278,276,278,278,276],2.9,[291,286,405,835,278,276,278,278,276],2.45,[299,286,405,837,278,276,278,278,276],2.5,[839],"failed (red in Table II: wrong registration produced local-map duplicates and a trajectory discontinuity; excluded from averages)",[603],[],[],[844],"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":846,"group":847,"sourceId":5,"sourceLabel":6,"table":603,"selfRows":323,"metrics":848,"seqs":855,"entrants":869,"cells":874,"outcomes":899,"locators":900,"hardware":901,"wordings":902,"notes":903},"cticp2022-table-ii","cticp2022:Table II",[849,853],{"label":850,"unit":851,"statistic":183,"alignment":852},"ATE (LO), mean Absolute Trajectory Error","m","SE3",{"label":854,"unit":851,"statistic":183,"alignment":852},"ATE (LO+LC), mean Absolute Trajectory Error",[856,858,860,861,864,866],{"dataset":195,"sequence":196,"environment":857},"vehicle, urban",{"dataset":218,"sequence":196,"environment":859},"vehicle, same environment as KITTI",{"dataset":99,"sequence":232,"environment":21},{"dataset":104,"sequence":862,"environment":863},"LuxembourgGarden","vehicle, city centre",{"dataset":119,"sequence":253,"environment":865},"handheld stick, campus",{"dataset":110,"sequence":867,"environment":868},"2012-01-08","two-wheeled Segway, campus",[870,872],{"name":871,"methodId":5,"linkable":269,"proposed":269,"self":269},"CT-ICP odometry only (LO)",{"name":873,"methodId":5,"linkable":269,"proposed":269,"self":269},"CT-ICP with loop closure and pose graph (LO+LC)",[875,877,879,881,883,885,887,889,891,893,895,897],[276,276,276,876,278,276,278,278,276],6.22,[280,280,276,878,278,276,278,278,276],0.66,[276,276,280,880,278,276,278,278,280],29.87,[280,280,280,882,278,276,278,278,280],1.07,[276,276,286,884,278,276,278,278,286],0.21,[280,280,286,886,278,276,278,278,286],0.26,[276,276,291,888,278,276,278,278,291],29.16,[280,280,291,890,278,276,278,278,291],9.65,[276,276,299,892,278,276,278,278,299],0.22,[280,280,299,894,278,276,278,278,299],0.36,[276,276,302,896,278,276,278,278,302],2.97,[280,280,302,898,278,276,278,278,302],2.58,[],[603],[],[],[904,905,906,907,908,909],"Loop closure evaluation; mean ATE (m) after the best rigid transform between ground truth and estimate; Nmap=100, Noverlap=30; Nloop=69 loops detected","Loop closure evaluation; mean ATE (m) after the best rigid transform between ground truth and estimate; Nmap=100, Noverlap=30; Nloop=234 loops detected","Loop closure evaluation; mean ATE (m) after the best rigid transform between ground truth and estimate; Nmap=100, Noverlap=30; Nloop=78 loops detected","Loop closure evaluation; mean ATE (m) after the best rigid transform between ground truth and estimate; Nmap=100, Noverlap=30; Nloop=292 loops detected","Loop closure evaluation; mean ATE (m) after the best rigid transform between ground truth and estimate; Nmap=100, Noverlap=30; Nloop=59 loops detected","Loop closure evaluation; mean ATE (m) after the best rigid transform between ground truth and estimate; Nmap=100, Noverlap=30; Nloop=520 loops detected",{"slug":911,"group":912,"sourceId":913,"sourceLabel":914,"table":603,"selfRows":323,"metrics":915,"seqs":919,"entrants":945,"cells":958,"outcomes":1053,"locators":1054,"hardware":1056,"wordings":1057,"notes":1058},"hba2023-table-ii","hba2023:Table II","hba2023","Liu et al., 2023b",[916],{"label":917,"unit":851,"statistic":918,"alignment":126},"RMSE of the ATE, translation part of deg\u002Fm 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10",{"dataset":95,"sequence":944,"environment":922},"Avg.",[946,948,949,950,952,955],{"name":947,"methodId":913,"linkable":269,"proposed":269,"self":78},"Proposed",{"name":7,"methodId":5,"linkable":269,"proposed":78,"self":269},{"name":673,"methodId":268,"linkable":269,"proposed":78,"self":78},{"name":951,"methodId":125,"linkable":78,"proposed":78,"self":78},"LiTAMIN2",{"name":953,"methodId":954,"linkable":269,"proposed":78,"self":78},"SuMa","suma2018",{"name":956,"methodId":957,"linkable":269,"proposed":78,"self":78},"LOAM","loam2014",[959,960,962,964,965,966,967,968,970,972,974,976,978,980,982,984,985,986,987,988,989,990,991,992,994,995,996,998,999,1000,1001,1002,1003,1004,1006,1007,1008,1009,1011,1013,1014,1015,1016,1017,1018,1019,1020,1021,1023,1024,1026,1028,1029,1030,1031,1032,1033,1035,1036,1037,1038,1039,1041,1043,1044,1045,1046,1047,1048,1050,1051,1052],[276,276,276,384,278,276,278,278,276],[276,276,280,961,278,276,278,278,276],1.9,[276,276,286,963,278,276,278,278,276],5.1,[276,276,291,681,278,276,278,278,276],[276,276,299,384,278,276,278,278,276],[276,276,302,719,278,276,278,278,276],[276,276,305,486,278,276,278,278,276],[276,276,308,969,278,276,278,278,276],0.3,[276,276,311,971,278,276,278,278,276],2.7,[276,276,314,973,278,276,278,278,276],1.3,[276,276,317,975,278,276,278,278,276],1.1,[276,276,320,977,278,276,278,278,276],1.4,[280,276,276,979,278,276,278,278,276],1.7,[280,276,280,981,278,276,278,278,276],4.2,[280,276,286,983,278,276,278,278,276],4.1,[280,276,291,318,278,276,278,278,276],[280,276,299,318,278,276,278,278,276],[280,276,302,384,278,276,278,278,276],[280,276,305,969,278,276,278,278,276],[280,276,308,969,278,276,278,278,276],[280,276,311,837,278,276,278,278,276],[280,276,314,445,278,276,278,278,276],[280,276,317,384,278,276,278,278,276],[280,276,320,993,278,276,278,278,276],1.5,[286,276,276,975,278,276,278,278,276],[286,276,280,961,278,276,278,278,276],[286,276,286,997,278,276,278,278,276],5.4,[286,276,291,318,278,276,278,278,276],[286,276,299,445,278,276,278,278,276],[286,276,302,280,278,276,278,278,276],[286,276,305,969,278,276,278,278,276],[286,276,308,719,278,276,278,278,276],[286,276,311,833,278,276,278,278,276],[286,276,314,1005,278,276,278,278,276],2.1,[286,276,317,975,278,276,278,278,276],[286,276,320,339,278,276,278,278,276],[291,276,276,973,278,276,278,278,276],[291,276,280,1010,278,276,278,278,276],15.9,[291,276,286,1012,278,276,278,278,276],3.2,[291,276,291,384,278,276,278,278,276],[291,276,299,318,278,276,278,278,276],[291,276,302,681,278,276,278,278,276],[291,276,305,384,278,276,278,278,276],[291,276,308,724,278,276,278,278,276],[291,276,311,1005,278,276,278,278,276],[291,276,314,1005,278,276,278,278,276],[291,276,317,280,278,276,278,278,276],[291,276,320,1022,278,276,278,278,276],2.4,[299,276,276,280,278,276,278,278,276],[299,276,280,1025,278,276,278,278,276],13.8,[299,276,286,1027,278,276,278,278,276],7.1,[299,276,291,445,278,276,278,278,276],[299,276,299,719,278,276,278,278,276],[299,276,302,681,278,276,278,278,276],[299,276,305,681,278,276,278,278,276],[299,276,308,280,278,276,278,278,276],[299,276,311,1034,278,276,278,278,276],3.4,[299,276,314,975,278,276,278,278,276],[299,276,317,973,278,276,278,278,276],[299,276,320,1012,278,276,278,278,276],[302,276,276,993,278,276,278,278,276],[302,276,280,1040,278,276,278,278,276],17.2,[302,276,286,1042,278,276,278,278,276],17.9,[302,276,291,384,278,276,278,278,276],[302,276,299,719,278,276,278,278,276],[302,276,302,318,278,276,278,278,276],[302,276,305,384,278,276,278,278,276],[302,276,308,724,278,276,278,278,276],[302,276,311,1049,278,276,278,278,276],3.8,[302,276,314,975,278,276,278,278,276],[302,276,317,973,278,276,278,278,276],[302,276,320,981,278,276,278,278,276],[],[1055],"Table II (version of record)",[],[],[1059],"KITTI with loop-closed MULLS poses as HBA input; RMSE of ATE printed as rotation (deg)\u002Ftranslation (m); only the translation part is extracted; the '(loops)' sequence labels follow the asterisks of arXiv v1 Table II (the version-of-record table image carries no asterisks); version of record adds CT-ICP and LOAM rows",[1061,1066,1072,1078,1084,1090,1096,1101,1106,1112,1116,1121,1127,1132,1138,1143,1148,1152,1158,1164,1168,1173,1177,1182,1188,1194,1198,1203,1208,1212,1217],{"group":1062,"slug":1063,"sourceLabel":914,"table":1064,"selfRows":323,"datasets":1065},"hba2023:Table V","hba2023-table-v","Table V",[95],{"group":1067,"slug":1068,"sourceLabel":1069,"table":177,"selfRows":323,"datasets":1070},"trajlo2024:Table I","trajlo2024-table-i","Zheng & Zhu, 2024",[1071],"KITTI odometry",{"group":1073,"slug":1074,"sourceLabel":1069,"table":1075,"selfRows":323,"datasets":1076},"trajlo2024:Table III","trajlo2024-table-iii","Table III",[1077],"Hilti 2021 SLAM challenge",{"group":1079,"slug":1080,"sourceLabel":1081,"table":1075,"selfRows":314,"datasets":1082},"kissslam2025:Table III","kissslam2025-table-iii","Guadagnino et al., 2025a",[1083],"HeLiPR",{"group":1085,"slug":1086,"sourceLabel":1087,"table":603,"selfRows":311,"datasets":1088},"dlio2023:Table II","dlio2023-table-ii","Chen et al., 2023",[1089],"UCLA Campus (self-collected)",{"group":1091,"slug":1092,"sourceLabel":1081,"table":1093,"selfRows":308,"datasets":1094},"kissslam2025:Table IV","kissslam2025-table-iv","Table IV",[1095],"Apollo",{"group":1097,"slug":1098,"sourceLabel":1081,"table":1064,"selfRows":308,"datasets":1099},"kissslam2025:Table V","kissslam2025-table-v",[1100],"Newer College",{"group":1102,"slug":1103,"sourceLabel":1087,"table":177,"selfRows":305,"datasets":1104},"dlio2023:Table I","dlio2023-table-i",[1105],"Newer College Dataset",{"group":1107,"slug":1108,"sourceLabel":1109,"table":1064,"selfRows":299,"datasets":1110},"genzicp2025:Table 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(test)",{"group":1139,"slug":1140,"sourceLabel":6,"table":1141,"selfRows":286,"datasets":1142},"cticp2022:Text Sec.V-B","cticp2022-text-sec-v-b","Text Sec.V-B",[218,195],{"group":1144,"slug":1145,"sourceLabel":6,"table":1146,"selfRows":286,"datasets":1147},"cticp2022:Text Sec.V-C","cticp2022-text-sec-v-c","Text Sec.V-C",[660],{"group":1149,"slug":1150,"sourceLabel":1109,"table":177,"selfRows":286,"datasets":1151},"genzicp2025:Table I","genzicp2025-table-i",[1100],{"group":1153,"slug":1154,"sourceLabel":1109,"table":1155,"selfRows":286,"datasets":1156},"genzicp2025:Table VI","genzicp2025-table-vi","Table VI",[1157],"SubT-MRS",{"group":1159,"slug":1160,"sourceLabel":1161,"table":1075,"selfRows":286,"datasets":1162},"helmberger2022hilti:Table III","helmberger2022hilti-table-iii","Helmberger et al., 2022",[1163],"Hilti SLAM Challenge Dataset (2021)",{"group":1165,"slug":1166,"sourceLabel":1119,"table":603,"selfRows":286,"datasets":1167},"kissicp2023:Table II","kissicp2023-table-ii",[1071],{"group":1169,"slug":1170,"sourceLabel":1119,"table":1064,"selfRows":286,"datasets":1171},"kissicp2023:Table V","kissicp2023-table-v",[1172],"KITTI raw",{"group":1174,"slug":1175,"sourceLabel":1130,"table":603,"selfRows":286,"datasets":1176},"steamlio2025:Table II","steamlio2025-table-ii",[1105],{"group":1178,"slug":1179,"sourceLabel":1069,"table":1064,"selfRows":286,"datasets":1180},"trajlo2024:Table V","trajlo2024-table-v",[1181],"NTU VIRAL",{"group":1183,"slug":1184,"sourceLabel":1185,"table":603,"selfRows":286,"datasets":1186},"zhang2023hiltioxford:Table II","zhang2023hiltioxford-table-ii","Zhang et al., 2023c",[1187],"Hilti-Oxford (Hilti SLAM Challenge 2022)",{"group":1189,"slug":1190,"sourceLabel":1191,"table":1192,"selfRows":280,"datasets":1193},"balm2_2023:Supplementary Table VII","balm2-2023-supplementary-table-vii","Liu et al., 2023a","Supplementary Table VII",[1071],{"group":1195,"slug":1196,"sourceLabel":1109,"table":1075,"selfRows":280,"datasets":1197},"genzicp2025:Table III","genzicp2025-table-iii",[1071],{"group":1199,"slug":1200,"sourceLabel":1109,"table":1093,"selfRows":280,"datasets":1201},"genzicp2025:Table IV","genzicp2025-table-iv",[1202],"HILTI-Oxford 2022",{"group":1204,"slug":1205,"sourceLabel":914,"table":1093,"selfRows":280,"datasets":1206},"hba2023:Table IV","hba2023-table-iv",[1207],"New College (Newer College dataset family)",{"group":1209,"slug":1210,"sourceLabel":1081,"table":177,"selfRows":280,"datasets":1211},"kissslam2025:Table I","kissslam2025-table-i",[110],{"group":1213,"slug":1214,"sourceLabel":602,"table":1075,"selfRows":280,"datasets":1215},"madicp2024:Table III","madicp2024-table-iii",[1216],"all Table II datasets",{"group":1218,"slug":1219,"sourceLabel":1220,"table":1221,"selfRows":280,"datasets":1222},"zhang2024_3dlidarslam_survey:Table 8","zhang2024-3dlidarslam-survey-table-8","Zhang et al., 2024a","Table 8",[1071],1790510654021]