[{"data":1,"prerenderedAt":863},["ShallowReactive",2],{"method-pinslam2024":3},{"method":4,"reference":67,"equipment":91,"figures":164,"results":165},{"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":28,"sensors":35,"platform":43,"estimator":47,"association":48,"timeModel":49,"deskew":50,"loopClosure":51,"globalOptimization":52,"mapRepresentation":53,"prior":54,"outputGeometry":55,"compute":56,"codeUrl":57,"codeLicense":58,"relatedVersions":59},"pinslam2024","Pan et al., 2024","PIN-SLAM","PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency",2024,"recent","C09","full_slam_with_global_correction","PIN-SLAM 以稀疏可最佳化的神經點編碼局部符號距離場（SDF），里程計採不需最近點配對的點對隱式 SDF 配準，並以局部地圖產生的描述子偵測迴圈、做位姿圖最佳化。因神經點隨所屬影格一起移動，迴圈修正後隱式地圖可保持全域一致並輸出網格。論文另在 Newer College 以毫米級 TLS 參考地圖評估網格精度，並在 Hilti-21（含營建工地序列）報告軌跡誤差。","LiDAR SLAM on elastic neural points encoding an SDF, with correspondence-free registration, loop closure and pose-graph-driven map deformation.","full_text_reviewed","peer_reviewed_published","main_body","作者在 Hilti-21 資料集評估軌跡；論文 Sec. V-A1 說明該資料集含戶外營建工地序列，參考軌跡由全測站追蹤系統或動作捕捉系統量測。Table VIII 的 cons2 欄對應營建工地序列（名稱對應為推論）；PIN-SLAM 在 cons2 的 ATE RMSE 為 0.41 m，為所比較方法中最佳，但也是其六個 Hilti-21 序列中最大的誤差；作者並指出這些序列較短且無明確迴圈。此為公開基準的軌跡層級證據，未評估工地點雲或網格品質。另在 Nebula 天然洞穴資料以 TLS 地圖做定性誤差展示（非營建隧道）。",[20,21,22,23],"public_benchmark","independent_reference","real_construction_site","underground_or_tunnel",[25,26,27],"Localization on par or better than LiDAR odometry\u002FSLAM baselines (abstract; Sec. V-B)","Best completeness, Chamfer and F-score among compared mesh-producing methods on two Newer College scenes vs TLS reference (Sec. V-D1; Table XI)","Sensor-rate operation on a moderate GPU (Sec. V-F2)",[29,30,31,32,33,34],"No IMU; future work (Sec. VI)","Fixed neural point resolution (Sec. VI)","Loop\u002FPGO frames exceed 200 ms (Sec. V-F2)","Hilti-21 sequences are short without explicit loops, so odometry and SLAM are not distinguished there (Sec. V-B2)","Loop closure detection recall at Top-1 averages 94.5%, below BEVPlace at 99.3% (Table X)","(derived from Table VI) on the loop-free IPB-Car 2023-0 drive PIN-SLAM has 87.59 m ATE RMSE, equal to PIN-LO, and all compared methods exceed 78 m",[36,37,38,39,40,41,42],"Velodyne HDL64 (KITTI)","Ouster OS1-64 (MulRAN; Newer College long sequences; IPB-Car 2020)","OS1-128 (IPB-Car 2023)","OS0-128 (Newer College shorter sequences)","OS0-64 (Hilti-21, handheld)","32-beam LiDAR on a Spot robot (Nebula, qualitative)","synthetic RGB-D (Replica)",[44,45,46],"vehicle","handheld","legged","correspondence-free scan-to-implicit-SDF registration with second-order (Levenberg-Marquardt) optimization and robust weights; pose graph optimization after loop closure","point-to-implicit SDF (no closest-point association)","discrete poses","constant-velocity prediction with per-point timestamps before odometry, then re-deskew with the odometry estimate (Sec. III-B); motion compensation disabled on KITTI because those scans are already deskewed (Sec. V-B1)","distance-based local loop check plus polar context descriptors computed from the local neural map; verification by scan-to-map registration","pose graph optimization; neural points move with their associated frames (elastic map)","sparse optimizable neural points indexed by voxel hashing with a shared decoder to SDF","none","mesh via marching cubes from the SDF; compact neural point map","single NVIDIA Quadro A4000: full 7.1 Hz (0.14 s per frame), light 11.3 Hz (0.09 s per frame) average on KITTI 00-10 (Table XVI); frames with PGO occasionally >200 ms (Sec. V-F2); odometry and map optimization each take about 40% of runtime (Fig. 13); map memory 66.3 to 138.8 MB on KITTI 00, 05 and 08 and 76.8 MB on Newer College 02, about 0.3% to 1.1% of the raw point cloud (Table XV)","https:\u002F\u002Fgithub.com\u002FPRBonn\u002FPIN_SLAM","MIT",[60,64],{"relation":61,"title":62,"doi_or_url":63},"preprint","arXiv:2401.09101","https:\u002F\u002Farxiv.org\u002Fabs\u002F2401.09101",{"relation":65,"title":66,"doi_or_url":57},"code_release","PRBonn\u002FPIN_SLAM",{"id":5,"kind":68,"shortName":7,"title":8,"authors":69,"year":9,"venue":76,"venueType":77,"publisher":78,"volumeIssuePages":79,"doi":80,"arxivId":81,"url":82,"firstPublicDate":83,"publicationStatus":16,"metadataStatus":84,"fulltextStatus":15,"era":10,"classicReason":85,"codeUrl":57,"cluster":11,"topics":86,"mdpi":87,"verification":88,"label":6,"fulltextRoute":89,"versionRead":90,"addedByCensus":87},"method",[70,71,72,73,74,75],"Yue Pan","Xingguang Zhong","Louis Wiesmann","Thorbjörn Posewsky","Jens Behley","Cyrill Stachniss","IEEE Transactions on Robotics","journal","IEEE","40, 4045-4064","10.1109\u002Ftro.2024.3422055","2401.09101","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002Ftro.2024.3422055","2024-01-17","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v2 (2024-07-02, 20 pages), the accepted IEEE T-RO manuscript (footnote: received 2024-01-16, revised 2024-05-28, accepted 2024-06-26), read in full; the typeset IEEE version was not compared",[92,100,105,109,113,117,122,127,132,138,142,146,150,154,157],{"category":93,"model":94,"canonical":95,"role":96,"dataset":97,"specs":98,"locator":99},"lidar","Velodyne HDL64","Velodyne HDL-64E","dataset sensor","KITTI odometry","64-beam, car-mounted; reference poses from GNSS-INS","Sec. V-A1; Table I",{"category":93,"model":101,"canonical":101,"role":96,"dataset":102,"specs":103,"locator":104},"Ouster OS1-64","MulRAN","car-mounted; field of view partly blocked by the radar sensor; reference poses from GNSS-INS","Sec. V-A1",{"category":93,"model":106,"canonical":101,"role":96,"dataset":107,"specs":108,"locator":104},"OS1-64","Newer College","handheld, used for the two longer sequences",{"category":93,"model":110,"canonical":111,"role":96,"dataset":107,"specs":112,"locator":104},"OS0-128","Ouster OS0-128","handheld, used for the shorter sequences",{"category":93,"model":114,"canonical":101,"role":96,"dataset":115,"specs":116,"locator":104},"OS1-64 (2020) and OS1-128 (2023)","IPB-Car","robot car in Bonn; self-collected",{"category":93,"model":118,"canonical":119,"role":96,"dataset":120,"specs":121,"locator":104},"OS0-64","Ouster OS0-64","Hilti-21","handheld; indoor offices, labs, basements and outdoor construction sites",{"category":93,"model":123,"canonical":123,"role":96,"dataset":124,"specs":125,"locator":126},"32-beam LiDAR (model not named)","Nebula","carried by a Spot1 robot moving back and forth in the Valentine Cave","Fig. 7",{"category":128,"model":129,"canonical":129,"role":96,"dataset":124,"specs":130,"locator":131},"platform","Spot1 robot","quadruped robot moving back and forth in a cave tunnel (Valentine Cave)","Fig. 7; Sec. V-B4",{"category":133,"model":134,"canonical":134,"role":135,"dataset":136,"specs":137,"locator":104},"gnss","GNSS-INS (model not named)","reference or ground truth","KITTI odometry; MulRAN","poses regarded as the evaluation reference",{"category":139,"model":140,"canonical":140,"role":135,"dataset":115,"specs":141,"locator":104},"tls_scanner","geo-referenced terrestrial laser scanner (model not named)","global map used for scan-to-map constraints with the OS1-128 in the factor graph that generates reference poses (fused with GNSS-INS, LiDAR odometry and loop closures)",{"category":139,"model":143,"canonical":143,"role":135,"dataset":107,"specs":144,"locator":145},"survey-grade TLS point cloud map (scanner model not named)","mm-level accuracy; reference poses obtained by aligning each scan to it; also the reference model for Table XI","Sec. V-A1; Table XI caption",{"category":147,"model":148,"canonical":148,"role":135,"dataset":120,"specs":149,"locator":104},"total_station","total station tracking system (model not named)","reference trajectories for some sequences (others from a motion capture system)",{"category":151,"model":152,"canonical":152,"role":135,"dataset":120,"specs":153,"locator":104},"other","motion capture system (model not named)","reference trajectories for some sequences",{"category":139,"model":155,"canonical":155,"role":135,"dataset":124,"specs":156,"locator":126},"terrestrial laser scanner survey-grade map (model not named)","used only for a qualitative mapping-error visualisation",{"category":158,"model":159,"canonical":159,"role":160,"dataset":161,"specs":162,"locator":163},"compute","NVIDIA Quadro A4000","compute for runtime",null,"single GPU; 7.1 Hz full and 11.3 Hz light on KITTI","Sec. V-F2; Table XVI",[],{"totalRows":166,"groupCount":167,"groups":168,"others":801},99,15,[169,412,572,702],{"slug":170,"group":171,"sourceId":172,"sourceLabel":173,"table":174,"selfRows":175,"metrics":176,"seqs":188,"entrants":199,"cells":214,"outcomes":406,"locators":407,"hardware":408,"wordings":409,"notes":410},"pings2025-table-ii","pings2025:Table II","pings2025","Pan et al., 2025","Table II",16,[177,181,183,185],{"label":178,"unit":179,"statistic":180,"alignment":180},"Accuracy error","m","not_reported",{"label":182,"unit":179,"statistic":180,"alignment":180},"Completeness error",{"label":184,"unit":179,"statistic":180,"alignment":180},"Chamfer Distance",{"label":186,"unit":187,"statistic":180,"alignment":180},"F-score (0.1 m threshold)","fraction (0 to 1)",[189,193,195,197],{"dataset":190,"sequence":191,"environment":192},"Oxford Spires","Blenheim Palace 05","Oxford Spires sequences Blenheim Palace 05, Christ Church 02, Keble College 04 and Observatory Quarter 01, handheld LiDAR-camera rig; scene type not described in the paper",{"dataset":190,"sequence":194,"environment":192},"Christ Church 02",{"dataset":190,"sequence":196,"environment":192},"Keble College 04",{"dataset":190,"sequence":198,"environment":192},"Observatory Quarter 01",[200,202,204,206,210,212],{"name":201,"methodId":161,"linkable":87,"proposed":87,"self":87},"OpenMVS [5] (offline)",{"name":203,"methodId":161,"linkable":87,"proposed":87,"self":87},"Nerfacto [62] (offline)",{"name":205,"methodId":161,"linkable":87,"proposed":87,"self":87},"GSS [11]",{"name":207,"methodId":208,"linkable":209,"proposed":87,"self":87},"VDB-Fusion [67]","vizzo2022vdbfusion",true,{"name":211,"methodId":5,"linkable":209,"proposed":87,"self":209},"PIN-SLAM [51]",{"name":213,"methodId":172,"linkable":209,"proposed":209,"self":87},"PINGS (Ours)",[215,219,222,225,228,230,232,234,236,238,240,242,244,246,248,250,252,255,257,259,261,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,295,297,299,301,303,305,307,309,311,313,315,317,318,320,322,324,326,328,330,332,333,335,337,339,341,343,344,346,348,350,351,353,355,357,359,361,363,364,366,368,370,372,374,376,378,380,382,384,385,387,389,391,393,395,397,399,400,402,404],[216,216,216,217,218,216,218,218,216],0,0.126,-1,[216,220,216,221,218,216,218,218,216],1,1.045,[216,223,216,224,218,216,218,218,216],2,0.586,[216,226,216,227,218,216,218,218,216],3,0.458,[220,216,216,229,218,216,218,218,216],0.302,[220,220,216,231,218,216,218,218,216],0.676,[220,223,216,233,218,216,218,218,216],0.489,[220,226,216,235,218,216,218,218,216],0.309,[223,216,216,237,218,216,218,218,216],0.204,[223,220,216,239,218,216,218,218,216],0.254,[223,223,216,241,218,216,218,218,216],0.229,[223,226,216,243,218,216,218,218,216],0.266,[226,216,216,245,218,216,218,218,216],0.098,[226,220,216,247,218,216,218,218,216],0.123,[226,223,216,249,218,216,218,218,216],0.111,[226,226,216,251,218,216,218,218,216],0.692,[253,216,216,254,218,216,218,218,216],4,0.078,[253,220,216,256,218,216,218,218,216],0.136,[253,223,216,258,218,216,218,218,216],0.107,[253,226,216,260,218,216,218,218,216],0.739,[262,216,216,263,218,216,218,218,216],5,0.072,[262,220,216,265,218,216,218,218,216],0.133,[262,223,216,267,218,216,218,218,216],0.102,[262,226,216,269,218,216,218,218,216],0.758,[216,216,220,271,218,216,218,218,216],0.046,[216,220,220,273,218,216,218,218,216],5.381,[216,223,220,275,218,216,218,218,216],2.714,[216,226,220,277,218,216,218,218,216],0.41,[220,216,220,279,218,216,218,218,216],0.219,[220,220,220,281,218,216,218,218,216],4.435,[220,223,220,283,218,216,218,218,216],2.327,[220,226,220,285,218,216,218,218,216],0.343,[223,216,220,287,218,216,218,218,216],0.174,[223,220,220,289,218,216,218,218,216],0.292,[223,223,220,291,218,216,218,218,216],0.233,[223,226,220,293,218,216,218,218,216],0.346,[226,216,220,245,218,216,218,218,216],[226,220,220,296,218,216,218,218,216],0.243,[226,223,220,298,218,216,218,218,216],0.171,[226,226,220,300,218,216,218,218,216],0.582,[253,216,220,302,218,216,218,218,216],0.069,[253,220,220,304,218,216,218,218,216],0.252,[253,223,220,306,218,216,218,218,216],0.16,[253,226,220,308,218,216,218,218,216],0.617,[262,216,220,310,218,216,218,218,216],0.067,[262,220,220,312,218,216,218,218,216],0.251,[262,223,220,314,218,216,218,218,216],0.159,[262,226,220,316,218,216,218,218,216],0.622,[216,216,223,310,218,216,218,218,216],[216,220,223,319,218,216,218,218,216],0.342,[216,223,223,321,218,216,218,218,216],0.205,[216,226,223,323,218,216,218,218,216],0.806,[220,216,223,325,218,216,218,218,216],0.137,[220,220,223,327,218,216,218,218,216],0.15,[220,223,223,329,218,216,218,218,216],0.144,[220,226,223,331,218,216,218,218,216],0.68,[223,216,223,298,218,216,218,218,216],[223,220,223,334,218,216,218,218,216],0.162,[223,223,223,336,218,216,218,218,216],0.167,[223,226,223,338,218,216,218,218,216],0.466,[226,216,223,340,218,216,218,218,216],0.103,[226,220,223,342,218,216,218,218,216],0.101,[226,223,223,267,218,216,218,218,216],[226,226,223,345,218,216,218,218,216],0.719,[253,216,223,347,218,216,218,218,216],0.096,[253,220,223,349,218,216,218,218,216],0.108,[253,223,223,267,218,216,218,218,216],[253,226,223,352,218,216,218,218,216],0.744,[262,216,223,354,218,216,218,218,216],0.093,[262,220,223,356,218,216,218,218,216],0.106,[262,223,223,358,218,216,218,218,216],0.099,[262,226,223,360,218,216,218,218,216],0.749,[216,216,226,362,218,216,218,218,216],0.048,[216,220,226,316,218,216,218,218,216],[216,223,226,365,218,216,218,218,216],0.335,[216,226,226,367,218,216,218,218,216],0.734,[220,216,226,369,218,216,218,218,216],0.197,[220,220,226,371,218,216,218,218,216],0.398,[220,223,226,373,218,216,218,218,216],0.298,[220,226,226,375,218,216,218,218,216],0.592,[223,216,226,377,218,216,218,218,216],0.179,[223,220,226,379,218,216,218,218,216],0.184,[223,223,226,381,218,216,218,218,216],0.181,[223,226,226,383,218,216,218,218,216],0.407,[226,216,226,247,218,216,218,218,216],[226,220,226,386,218,216,218,218,216],0.109,[226,223,226,388,218,216,218,218,216],0.116,[226,226,226,390,218,216,218,218,216],0.645,[253,216,226,392,218,216,218,218,216],0.105,[253,220,226,394,218,216,218,218,216],0.129,[253,223,226,396,218,216,218,218,216],0.117,[253,226,226,398,218,216,218,218,216],0.665,[262,216,226,267,218,216,218,218,216],[262,220,226,401,218,216,218,218,216],0.124,[262,223,226,403,218,216,218,218,216],0.113,[262,226,226,405,218,216,218,218,216],0.681,[],[174],[],[],[411],"Oxford Spires surface reconstruction against the millimetre-accurate Leica RTC360 TLS reference map; localization disabled and ground-truth poses used for all methods; OpenMVS and Nerfacto results taken from the benchmark (offline batch); meshes at 0.1 m resolution; F-score threshold 0.1 m; precision and recall columns not extracted",{"slug":413,"group":414,"sourceId":5,"sourceLabel":6,"table":415,"selfRows":175,"metrics":416,"seqs":420,"entrants":438,"cells":458,"outcomes":564,"locators":567,"hardware":568,"wordings":569,"notes":570},"pinslam2024-table-vii","pinslam2024:Table VII","Table VII",[417],{"label":418,"unit":179,"statistic":419,"alignment":180},"ATE RMSE [m]","RMSE",[421,424,426,428,430,432,434,436],{"dataset":107,"sequence":422,"environment":423},"01","outdoor and indoor campus, 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('-')",[415],[],[],[571],"Newer College handheld LiDAR (OS1-64 long sequences, OS0-128 shorter sequences), reference poses from registering each scan to a survey-grade TLS map; all sequences contain loops; failure marked by a cross, '-' not reported or unavailable; ATE RMSE [m] with Umeyama trajectory alignment (Sec. V-B2; whether scale is estimated is not stated)",{"slug":573,"group":574,"sourceId":575,"sourceLabel":576,"table":577,"selfRows":578,"metrics":579,"seqs":582,"entrants":603,"cells":614,"outcomes":695,"locators":697,"hardware":698,"wordings":699,"notes":700},"kissslam2025-table-iii","kissslam2025:Table III","kissslam2025","Guadagnino et al., 2025a","Table III",9,[580],{"label":581,"unit":179,"statistic":180,"alignment":180},"ATE [m] (evo)",[583,587,589,591,593,595,597,599,601],{"dataset":584,"sequence":585,"environment":586},"HeLiPR","Bridge Aeva","urban driving with Aeva, Avia and Ouster LiDARs",{"dataset":584,"sequence":588,"environment":586},"Bridge Avia",{"dataset":584,"sequence":590,"environment":586},"Bridge Ouster",{"dataset":584,"sequence":592,"environment":586},"Roundabout Aeva",{"dataset":584,"sequence":594,"environment":586},"Roundabout Avia",{"dataset":584,"sequence":596,"environment":586},"Roundabout Ouster",{"dataset":584,"sequence":598,"environment":586},"Town Aeva",{"dataset":584,"sequence":600,"environment":586},"Town Avia",{"dataset":584,"sequence":602,"environment":586},"Town Ouster",[604,605,607,610,612],{"name":7,"methodId":5,"linkable":209,"proposed":87,"self":209},{"name":606,"methodId":447,"linkable":209,"proposed":87,"self":87},"SuMa",{"name":608,"methodId":609,"linkable":209,"proposed":87,"self":87},"CT-ICP","cticp2022",{"name":611,"methodId":450,"linkable":209,"proposed":87,"self":87},"MULLS",{"name":613,"methodId":575,"linkable":209,"proposed":209,"self":87},"Ours (KISS-SLAM)",[615,616,618,620,621,623,625,627,629,632,633,634,636,637,638,640,642,643,644,645,647,649,651,653,655,657,659,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,689,691,693],[216,216,216,161,216,216,218,218,216],[216,216,220,617,218,216,218,218,216],365.72,[216,216,223,619,218,216,218,218,216],19.23,[216,216,226,161,216,216,218,218,216],[216,216,253,622,218,216,218,218,216],7.02,[216,216,262,624,218,216,218,218,216],1.47,[216,216,471,626,218,216,218,218,216],41.19,[216,216,473,628,218,216,218,218,216],11.4,[216,216,630,631,218,216,218,218,216],8,2.55,[220,216,216,161,216,216,218,218,216],[220,216,220,161,216,216,218,218,216],[220,216,223,635,218,216,218,218,216],140.01,[220,216,226,161,216,216,218,218,216],[220,216,253,161,216,216,218,218,216],[220,216,262,639,218,216,218,218,216],14.88,[220,216,471,641,218,216,218,218,216],132.17,[220,216,473,161,216,216,218,218,216],[220,216,630,161,216,216,218,218,216],[223,216,216,161,216,216,218,218,216],[223,216,220,646,218,216,218,218,216],41.47,[223,216,223,648,218,216,218,218,216],579.62,[223,216,226,650,218,216,218,218,216],10.04,[223,216,253,652,218,216,218,218,216],3.36,[223,216,262,654,218,216,218,218,216],1.81,[223,216,471,656,218,216,218,218,216],65.29,[223,216,473,658,218,216,218,218,216],63.72,[223,216,630,161,216,216,218,218,216],[226,216,216,661,218,216,218,218,216],356.06,[226,216,220,663,218,216,218,218,216],321.87,[226,216,223,665,218,216,218,218,216],52.65,[226,216,226,667,218,216,218,218,216],19.08,[226,216,253,669,218,216,218,218,216],16.39,[226,216,262,671,218,216,218,218,216],2.65,[226,216,471,673,218,216,218,218,216],39.82,[226,216,473,675,218,216,218,218,216],14.93,[226,216,630,677,218,216,218,218,216],4.45,[253,216,216,679,218,216,218,218,216],98.61,[253,216,220,681,218,216,218,218,216],148.88,[253,216,223,683,218,216,218,218,216],19.47,[253,216,226,685,218,216,218,218,216],6.06,[253,216,253,687,218,216,218,218,216],3.84,[253,216,262,503,218,216,218,218,216],[253,216,471,690,218,216,218,218,216],14.44,[253,216,473,692,218,216,218,218,216],12.01,[253,216,630,694,218,216,218,218,216],1.99,[696],"failed ('-': error exceeded a sequence-specific threshold)",[577],[],[],[701],"ATE from evo; the paired relative KITTI metric (%) is omitted; '-' means the run failed because errors exceeded a sequence-specific threshold (version of record); same KISS-SLAM configuration for all runs; values averaged over three runs per scene",{"slug":703,"group":704,"sourceId":172,"sourceLabel":173,"table":577,"selfRows":630,"metrics":705,"seqs":712,"entrants":719,"cells":734,"outcomes":795,"locators":796,"hardware":797,"wordings":798,"notes":799},"pings2025-table-iii","pings2025:Table III",[706,710],{"label":707,"unit":708,"statistic":709,"alignment":54},"ARTE [%] (average relative translation error)","%","mean",{"label":711,"unit":179,"statistic":180,"alignment":180},"ATE [m] (absolute trajectory error; statistic not stated)",[713,717],{"dataset":714,"sequence":715,"environment":716},"in-house car dataset","Seq. 1 (5.0 km)","outdoor urban driving (robot car)",{"dataset":714,"sequence":718,"environment":716},"Seq. 2 (3.7 km)",[720,722,724,726,728,730,732,733],{"name":721,"methodId":441,"linkable":209,"proposed":87,"self":87},"F-LOAM [69]",{"name":723,"methodId":444,"linkable":209,"proposed":87,"self":87},"KISS-ICP [68]",{"name":725,"methodId":5,"linkable":209,"proposed":87,"self":209},"PIN odometry [51]",{"name":727,"methodId":172,"linkable":209,"proposed":209,"self":87},"PINGS odometry",{"name":729,"methodId":447,"linkable":209,"proposed":87,"self":87},"SuMa [3]",{"name":731,"methodId":450,"linkable":209,"proposed":87,"self":87},"MULLS [50]",{"name":211,"methodId":5,"linkable":209,"proposed":87,"self":209},{"name":213,"methodId":172,"linkable":209,"proposed":209,"self":87},[735,737,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,779,781,783,784,786,787,789,790,791,793],[216,216,216,736,218,216,218,218,216],1.96,[216,220,216,738,218,216,218,218,216],28.52,[216,216,220,740,218,216,218,218,216],1.93,[216,220,220,742,218,216,218,218,216],27,[220,216,216,744,218,216,218,218,216],1.49,[220,220,216,746,218,216,218,218,216],8.17,[220,216,220,748,218,216,218,218,216],1.38,[220,220,220,750,218,216,218,218,216],8.22,[223,216,216,752,218,216,218,218,216],0.95,[223,220,216,754,218,216,218,218,216],4.51,[223,216,220,756,218,216,218,218,216],0.98,[223,220,220,758,218,216,218,218,216],5.64,[226,216,216,760,218,216,218,218,216],0.73,[226,220,216,762,218,216,218,218,216],5.17,[226,216,220,764,218,216,218,218,216],0.59,[226,220,220,766,218,216,218,218,216],4.78,[253,216,216,768,218,216,218,218,216],5.55,[253,220,216,770,218,216,218,218,216],39.9,[253,216,220,772,218,216,218,218,216],4.42,[253,220,220,774,218,216,218,218,216],44.78,[262,216,216,776,218,216,218,218,216],2.23,[262,220,216,778,218,216,218,218,216],40.37,[262,216,220,780,218,216,218,218,216],1.64,[262,220,220,782,218,216,218,218,216],33.82,[471,216,216,220,218,216,218,218,216],[471,220,216,785,218,216,218,218,216],3.17,[471,216,220,756,218,216,218,218,216],[471,220,220,788,218,216,218,218,216],4.44,[473,216,216,331,218,216,218,218,216],[473,220,216,694,218,216,218,218,216],[473,216,220,792,218,216,218,218,216],0.58,[473,220,220,794,218,216,218,218,216],3.47,[],[577],[],[],[800],"In-house car dataset (Bonn), full sequences; reference poses from offline LiDAR bundle adjustment with RTK-GNSS, point cloud alignment and geo-referenced TLS constraints; odometry methods above, SLAM methods below; ATE alignment not stated",[802,807,813,818,824,829,834,838,844,850,857],{"group":803,"slug":804,"sourceLabel":6,"table":805,"selfRows":630,"datasets":806},"pinslam2024:Table XI","pinslam2024-table-xi","Table XI",[107],{"group":808,"slug":809,"sourceLabel":576,"table":810,"selfRows":473,"datasets":811},"kissslam2025:Table IV","kissslam2025-table-iv","Table IV",[812],"Apollo",{"group":814,"slug":815,"sourceLabel":576,"table":816,"selfRows":473,"datasets":817},"kissslam2025:Table V","kissslam2025-table-v","Table V",[107],{"group":819,"slug":820,"sourceLabel":6,"table":821,"selfRows":473,"datasets":822},"pinslam2024:Table VIII","pinslam2024-table-viii","Table VIII",[823],"Hilti-21 (Hilti SLAM Challenge 2021)",{"group":825,"slug":826,"sourceLabel":6,"table":827,"selfRows":471,"datasets":828},"pinslam2024:Table XVI","pinslam2024-table-xvi","Table XVI",[97],{"group":830,"slug":831,"sourceLabel":576,"table":174,"selfRows":253,"datasets":832},"kissslam2025:Table II","kissslam2025-table-ii",[833],"MulRan",{"group":835,"slug":836,"sourceLabel":6,"table":810,"selfRows":253,"datasets":837},"pinslam2024:Table IV","pinslam2024-table-iv",[97],{"group":839,"slug":840,"sourceLabel":6,"table":841,"selfRows":226,"datasets":842},"pinslam2024:Table IX","pinslam2024-table-ix","Table IX",[843],"Replica",{"group":845,"slug":846,"sourceLabel":847,"table":805,"selfRows":223,"datasets":848},"tosi2026survey:Table XI","tosi2026survey-table-xi","Tosi et al., 2026",[849,843],"KITTI",{"group":851,"slug":852,"sourceLabel":853,"table":854,"selfRows":220,"datasets":855},"ghadimzadeh2025slamnde:Table 3","ghadimzadeh2025slamnde-table-3","Ghadimzadeh Alamdari et al., 2025","Table 3",[856],"Luleå SubT tunnel dataset (Koval et al. 2022)",{"group":858,"slug":859,"sourceLabel":576,"table":860,"selfRows":220,"datasets":861},"kissslam2025:Table I","kissslam2025-table-i","Table I",[862],"NCLT",1790510657515]