[{"data":1,"prerenderedAt":775},["ShallowReactive",2],{"method-hba2023":3},{"method":4,"reference":58,"equipment":79,"figures":99,"results":100},{"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":21,"limitations":25,"sensors":32,"platform":34,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"hba2023","Liu et al., 2023b","HBA","Large-Scale LiDAR Consistent Mapping Using Hierarchical LiDAR Bundle Adjustment",2023,"recent","C06","offline_map_refinement","HBA 針對大場景下原始光達 BA 計算量過大的問題，採「由下而上」分層 BA：在小視窗內做局部 BA 並把視窗內各幀合併為上一層的關鍵影格，逐層向上，最後在頂層做全域 BA；再「由上而下」以位姿圖最佳化把結果平滑回傳到所有原始幀位姿，並以局部 BA 的 Hessian 作為資訊矩陣。作者依計算複雜度推導最佳層數。","HBA splits large-scale LiDAR BA into a bottom-up pyramid of small parallel BA problems and a top-down pose graph that propagates the refinement to all frames, trading a single huge Hessian for many small ones.","full_text_reviewed","peer_reviewed_published","main_body","未見工地驗證。公開資料為 KITTI、MulRan、New College 與 Newer College；自行蒐集的固態光達資料有兩段：約 14 m×16 m×8 m、含不規則管線與機台的室內工廠（7339 幀），以及約 95 m×195 m 的戶外公園（3407 幀），兩段都沒有真值，只以平均地圖熵（MME）比較（Sec. IV-A2、Table VI）。可作為工地掃描軌跡的離線全域一致化模組（推論），但缺少以獨立參考點雲量化的幾何誤差。",[20],"public_benchmark",[22,23,24],"Combines map-consistency benefit of BA with pose-graph efficiency (abstract)","Improved ATE even when initial trajectories were already loop-closed (Sec. IV-A1, Table II)","Lower time and RAM than original BA on New College\u002FNewer College as layers increase (Sec. IV-C; Fig. 12 in the version of record, Fig. 11 in arXiv v1)",[26,27,28,29,30,31],"Requires an initial pose trajectory from LiDAR odometry or SLAM (Sec. III-A)","IMU pre-integration and a LiDAR measurement noise model are not included; left to future work (Sec. V)","Bottom-up local BA ignores features co-visible across different local windows; the top-down PGO is needed to compensate (Sec. I, Sec. III-C)","False feature correspondences in the loosely parameterized top-layer global BA can add incorrect factors; the authors rely on dense lower-layer factors to contain them (Sec. IV-A2)","Self-collected scenes have no ground truth; map quality is shown only by mean map entropy (Sec. IV-A2, Table VI)","Not best on every KITTI sequence with loop-closed input: translation ATE is lower for CT-ICP on Seq. 02, 04, 08, 09 and 10, for LiTAMIN2 on Seq. 02, 04 and 08, and for SuMa and LOAM on Seq. 04 (VoR Table II)",[33],"3D LiDAR (mechanical spinning in the public datasets; solid-state LiDAR of ref. [26], retina-like incommensurable scanning, in the self-collected data)",[35,36,37],"vehicle (public datasets KITTI and MulRan; platform described in the dataset papers, not in HBA)","handheld (public New College and Newer College datasets; platform described in the dataset papers)","self-collected solid-state LiDAR sequences (carrier platform not described)","bottom-up hierarchical local BA in sliding windows (window 10, stride 5, parallel threads) plus global BA on top layer, followed by top-down pose-graph optimization using BA Hessians as information matrices","plane features via adaptive voxelization (BALM) in each layer","discrete poses","input may be raw or deskewed scans (Sec. III-A); deskew not performed by HBA","no place recognition module; can close gaps when the initial trajectory lacks loop closure if overlapping geometry is associated (Sec. IV-A2)","hierarchical BA plus pose graph, iterated until convergence","layered keyframe point clouds; adaptive voxel plane features","initial pose trajectory from any LiDAR odometry or SLAM","globally consistent point-cloud map and optimized poses","Offline, CPU parallel processing with n = 8 threads (Table I); CPU model and RAM size not reported. On MulRan DCC01, DCC02 and DCC03 the total optimization took 226.10 s, 362.59 s and 248.42 s versus 2830.91 s, 2631.83 s and 3655.10 s for the original BA and 4390.15 s, 4615.90 s and 7522.20 s for a reduced BA (version of record Table VIII). With the optimal layer number the method converges within about 12% of the data time (Sec. IV-C; Fig. 12 in the version of record, Fig. 11 in arXiv v1).","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FHBA","GPL-2.0 (LICENSE file)",[51,55],{"relation":52,"title":53,"doi_or_url":54},"preprint","arXiv 2209.11939 (title spelled 'Hierachical' on arXiv)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.11939",{"relation":56,"title":57,"doi_or_url":48},"code_release","hku-mars\u002FHBA",{"id":5,"kind":59,"shortName":7,"title":8,"authors":60,"year":9,"venue":65,"venueType":66,"publisher":67,"volumeIssuePages":68,"doi":69,"arxivId":70,"url":54,"firstPublicDate":71,"publicationStatus":16,"metadataStatus":72,"fulltextStatus":15,"era":10,"classicReason":73,"codeUrl":48,"cluster":11,"topics":74,"mdpi":75,"verification":76,"label":6,"fulltextRoute":77,"versionRead":78,"addedByCensus":75},"method",[61,62,63,64],"Xiyuan Liu","Zheng Liu","Fanze Kong","Fu Zhang","IEEE Robotics and Automation Letters","journal","IEEE","8(3):1523-1530","10.1109\u002Flra.2023.3238902","2209.11939","2022-09-24","metadata_verified","not_applicable",[11],false,"confirmed","NTU institutional (Chrome)","Version of record, IEEE RA-L 8(3):1523-1530 (IEEE Xplore HTML; tables read from the IEEE table images), cross-checked against arXiv 2209.11939v1 (PDF read in full)",[80,87,92],{"category":81,"model":82,"canonical":82,"role":83,"dataset":84,"specs":85,"locator":86},"lidar","solid-state LiDAR of ref. [26] (retina-like, incommensurable scanning; model not named)","method input","self-collected scene-1 (indoor factory) and scene-2 (outdoor park)","not_reported","Sec. IV-A2",{"category":81,"model":88,"canonical":88,"role":89,"dataset":90,"specs":85,"locator":91},"mechanical spinning LiDAR (model not named)","dataset sensor","KITTI, MulRan, New College, Newer College","Abstract; Sec. IV-A",{"category":93,"model":94,"canonical":94,"role":95,"dataset":96,"specs":97,"locator":98},"compute","CPU with 8 parallel threads (model not named)","compute for runtime",null,"n = 8 threads for parallel processing","Table I; Table VIII",[],{"totalRows":101,"groupCount":102,"groups":103,"others":750},61,8,[104,337,497,636],{"slug":105,"group":106,"sourceId":107,"sourceLabel":108,"table":109,"selfRows":110,"metrics":111,"seqs":117,"entrants":152,"cells":169,"outcomes":328,"locators":332,"hardware":333,"wordings":334,"notes":335},"tao2025oxfordspires-table-3","tao2025oxfordspires:Table 3","tao2025oxfordspires","Tao et al., 2025","Table 3",14,[112],{"label":113,"unit":114,"statistic":115,"alignment":116},"RMS of ATE","m","RMSE","SE3",[118,122,124,126,128,131,133,136,138,140,143,145,147,149],{"dataset":119,"sequence":120,"environment":121},"Oxford Spires","Keble College 02 (290 m)","historic site, outdoor and indoor parts (Keble College, Oxford)",{"dataset":119,"sequence":123,"environment":121},"Keble College 03 (280 m)",{"dataset":119,"sequence":125,"environment":121},"Keble College 04 (780 m)",{"dataset":119,"sequence":127,"environment":121},"Keble College 05 (710 m)",{"dataset":119,"sequence":129,"environment":130},"Radcliffe Observatory Quarter 01 (400 m)","historic site, outdoor and indoor parts (Radcliffe Observatory Quarter, Oxford)",{"dataset":119,"sequence":132,"environment":130},"Radcliffe Observatory Quarter 02 (390 m)",{"dataset":119,"sequence":134,"environment":135},"Blenheim Palace 01 (490 m)","historic site, outdoor and indoor parts (Blenheim Palace, Oxford)",{"dataset":119,"sequence":137,"environment":135},"Blenheim Palace 02 (390 m)",{"dataset":119,"sequence":139,"environment":135},"Blenheim Palace 05 (390 m)",{"dataset":119,"sequence":141,"environment":142},"Christ Church College 01 (920 m)","historic site, outdoor and indoor parts (Christ Church College, Oxford)",{"dataset":119,"sequence":144,"environment":142},"Christ Church College 02 (640 m)",{"dataset":119,"sequence":146,"environment":142},"Christ Church College 03 (340 m)",{"dataset":119,"sequence":148,"environment":142},"Christ Church College 05 (820 m)",{"dataset":119,"sequence":150,"environment":151},"Bodleian Library 02 (690 m)","historic site, outdoor and indoor parts (Bodleian Library, Oxford)",[153,155,157,159,163,166,167],{"name":154,"methodId":96,"linkable":75,"proposed":75,"self":75},"VILENS-SLAM",{"name":156,"methodId":96,"linkable":75,"proposed":75,"self":75},"Fast-LIO-SLAM",{"name":158,"methodId":96,"linkable":75,"proposed":75,"self":75},"SC-LIO-SAM",{"name":160,"methodId":161,"linkable":162,"proposed":75,"self":75},"ImMesh","lin2023immesh",true,{"name":164,"methodId":165,"linkable":162,"proposed":75,"self":75},"Fast-LIVO2","fastlivo2_2025",{"name":7,"methodId":5,"linkable":162,"proposed":75,"self":162},{"name":168,"methodId":96,"linkable":75,"proposed":75,"self":75},"COLMAP",[170,174,177,180,183,186,189,192,194,195,197,198,199,201,202,204,206,207,209,211,212,214,215,217,218,220,221,222,223,224,226,228,230,232,233,234,235,237,238,240,241,242,243,245,247,249,250,251,253,254,256,257,259,261,263,264,265,267,269,270,271,273,274,275,277,279,280,282,284,285,286,288,289,290,292,294,295,297,300,301,302,303,304,305,306,308,310,311,312,313,314,315,318,319,321,323,325,327],[171,171,171,172,173,171,173,173,171],0,0.06,-1,[175,171,171,176,173,171,173,173,171],1,0.25,[178,171,171,179,173,171,173,173,171],2,1.26,[181,171,171,182,173,171,173,173,171],3,0.08,[184,171,171,185,173,171,173,173,171],4,0.95,[187,171,171,188,173,171,173,173,171],5,0.11,[190,171,171,191,173,171,173,173,171],6,0.05,[171,171,175,193,173,171,173,173,171],0.14,[175,171,175,188,173,171,173,173,171],[178,171,175,196,173,171,173,173,171],4.02,[181,171,175,193,173,171,173,173,171],[184,171,175,172,173,171,173,173,171],[187,171,175,200,173,171,173,173,171],0.12,[190,171,175,191,173,171,173,173,171],[171,171,178,203,173,171,173,173,171],0.16,[175,171,178,205,173,171,173,173,171],0.49,[178,171,178,96,171,171,173,173,171],[181,171,178,208,173,171,173,173,171],3.67,[184,171,178,210,173,171,173,173,171],0.09,[187,171,178,200,173,171,173,173,171],[190,171,178,213,173,171,173,173,171],0.07,[171,171,181,188,173,171,173,173,171],[175,171,181,216,173,171,173,173,171],0.29,[178,171,181,96,171,171,173,173,171],[181,171,181,219,173,171,173,173,171],0.13,[184,171,181,188,173,171,173,173,171],[187,171,181,219,173,171,173,173,171],[190,171,181,210,173,171,173,173,171],[171,171,184,172,173,171,173,173,171],[175,171,184,225,173,171,173,173,171],0.17,[178,171,184,227,173,171,173,173,171],0.23,[181,171,184,229,173,171,173,173,171],0.2,[184,171,184,231,173,171,173,173,171],0.04,[187,171,184,191,173,171,173,173,171],[190,171,184,213,173,171,173,173,171],[171,171,187,210,173,171,173,173,171],[175,171,187,236,173,171,173,173,171],0.24,[178,171,187,193,173,171,173,173,171],[181,171,187,239,173,171,173,173,171],0.27,[184,171,187,213,173,171,173,173,171],[187,171,187,182,173,171,173,173,171],[190,171,187,182,173,171,173,173,171],[171,171,190,244,173,171,173,173,171],0.47,[175,171,190,246,173,171,173,173,171],0.18,[178,171,190,248,173,171,173,173,171],6.74,[181,171,190,239,173,171,173,173,171],[184,171,190,193,173,171,173,173,171],[187,171,190,252,173,171,173,173,171],0.21,[190,171,190,182,173,171,173,173,171],[171,171,255,203,173,171,173,173,171],7,[175,171,255,200,173,171,173,173,171],[178,171,255,258,173,171,173,173,171],4.41,[181,171,255,260,173,171,173,173,171],0.36,[184,171,255,262,173,171,173,173,171],0.22,[187,171,255,182,173,171,173,173,171],[190,171,255,191,173,171,173,173,171],[171,171,102,266,173,171,173,173,171],1.05,[175,171,102,268,173,171,173,173,171],0.28,[178,171,102,96,171,171,173,173,171],[181,171,102,262,173,171,173,173,171],[184,171,102,272,173,171,173,173,171],0.26,[187,171,102,193,173,171,173,173,171],[190,171,102,272,173,171,173,173,171],[171,171,276,172,173,171,173,173,171],9,[175,171,276,278,173,171,173,173,171],0.72,[178,171,276,96,171,171,173,173,171],[181,171,276,281,173,171,173,173,171],0.19,[184,171,276,283,173,171,173,173,171],0.54,[187,171,276,213,173,171,173,173,171],[190,171,276,172,173,171,173,173,171],[171,171,287,225,173,171,173,173,171],10,[175,171,287,205,173,171,173,173,171],[178,171,287,96,171,171,173,173,171],[181,171,287,291,173,171,173,173,171],1.7,[184,171,287,293,173,171,173,173,171],0.63,[187,171,287,200,173,171,173,173,171],[190,171,287,296,173,171,173,173,171],0.15,[171,171,298,299,173,171,173,173,171],11,0.03,[175,171,298,227,173,171,173,173,171],[178,171,298,193,173,171,173,173,171],[181,171,298,203,173,171,173,173,171],[184,171,298,96,175,171,173,173,171],[187,171,298,191,173,171,173,173,171],[190,171,298,213,173,171,173,173,171],[171,171,307,225,173,171,173,173,171],12,[175,171,307,309,173,171,173,173,171],0.3,[178,171,307,96,171,171,173,173,171],[181,171,307,252,173,171,173,173,171],[184,171,307,296,173,171,173,173,171],[187,171,307,200,173,171,173,173,171],[190,171,307,96,178,171,173,173,171],[171,171,316,317,173,171,173,173,171],13,1.11,[175,171,316,176,173,171,173,173,171],[178,171,316,320,173,171,173,173,171],1.71,[181,171,316,322,173,171,173,173,171],0.39,[184,171,316,324,173,171,173,173,171],0.46,[187,171,316,326,173,171,173,173,171],0.89,[190,171,316,239,173,171,173,173,171],[329,330,331],"failed","other: marked ✗ in Table 3 with no value; the caption explains ✗ only for SC-LIO-SAM failures and incomplete COLMAP results, so the reason for this Fast-LIVO2 entry is not stated","other: marked ✗ in Table 3; the caption states that COLMAP gives incomplete results on some sequences (Sec. 6.1.2: multiple disconnected sub-models under poor lighting)",[109],[],[],[336],"ATE RMS (m) against LiDAR-to-TLS ground truth after SE(3) Umeyama alignment; online: VILENS-SLAM, Fast-LIO-SLAM, SC-LIO-SAM, ImMesh, Fast-LIVO2; offline: HBA (input VILENS-SLAM), COLMAP (images only). VILENS-SLAM = VILENS with pose-graph optimisation; Fast-LIO-SLAM and SC-LIO-SAM add Scan Context loop closures to Fast-LIO2 and LIO-SAM. 'x' in the table = failed or incomplete. Authors note methods could improve with further tuning.",{"slug":338,"group":339,"sourceId":5,"sourceLabel":6,"table":340,"selfRows":307,"metrics":341,"seqs":344,"entrants":371,"cells":388,"outcomes":490,"locators":491,"hardware":493,"wordings":494,"notes":495},"hba2023-table-ii","hba2023:Table II","Table II",[342],{"label":343,"unit":114,"statistic":115,"alignment":85},"RMSE of the ATE, translation part of deg\u002Fm pair",[345,349,351,353,355,357,359,361,363,365,367,369],{"dataset":346,"sequence":347,"environment":348},"KITTI","Seq. 00 (loops)","urban and rural driving",{"dataset":346,"sequence":350,"environment":348},"Seq. 01",{"dataset":346,"sequence":352,"environment":348},"Seq. 02 (loops)",{"dataset":346,"sequence":354,"environment":348},"Seq. 03",{"dataset":346,"sequence":356,"environment":348},"Seq. 04",{"dataset":346,"sequence":358,"environment":348},"Seq. 05 (loops)",{"dataset":346,"sequence":360,"environment":348},"Seq. 06 (loops)",{"dataset":346,"sequence":362,"environment":348},"Seq. 07 (loops)",{"dataset":346,"sequence":364,"environment":348},"Seq. 08 (loops)",{"dataset":346,"sequence":366,"environment":348},"Seq. 09 (loops)",{"dataset":346,"sequence":368,"environment":348},"Seq. 10",{"dataset":346,"sequence":370,"environment":348},"Avg.",[372,374,377,380,382,385],{"name":373,"methodId":5,"linkable":162,"proposed":162,"self":162},"Proposed",{"name":375,"methodId":376,"linkable":162,"proposed":75,"self":75},"CT-ICP","cticp2022",{"name":378,"methodId":379,"linkable":162,"proposed":75,"self":75},"MULLS","mulls2021",{"name":381,"methodId":96,"linkable":75,"proposed":75,"self":75},"LiTAMIN2",{"name":383,"methodId":384,"linkable":162,"proposed":75,"self":75},"SuMa","suma2018",{"name":386,"methodId":387,"linkable":162,"proposed":75,"self":75},"LOAM","loam2014",[389,391,393,395,397,398,400,401,402,404,406,408,410,411,413,415,417,418,419,420,421,423,425,426,428,429,430,432,433,434,435,436,437,439,441,442,444,445,447,449,450,451,452,453,455,456,457,458,460,461,463,465,466,467,468,469,470,472,473,474,475,476,478,480,481,482,483,484,485,487,488,489],[171,171,171,390,173,171,173,173,171],0.8,[171,171,175,392,173,171,173,173,171],1.9,[171,171,178,394,173,171,173,173,171],5.1,[171,171,181,396,173,171,173,173,171],0.6,[171,171,184,390,173,171,173,173,171],[171,171,187,399,173,171,173,173,171],0.4,[171,171,190,229,173,171,173,173,171],[171,171,255,309,173,171,173,173,171],[171,171,102,403,173,171,173,173,171],2.7,[171,171,276,405,173,171,173,173,171],1.3,[171,171,287,407,173,171,173,173,171],1.1,[171,171,298,409,173,171,173,173,171],1.4,[175,171,171,291,173,171,173,173,171],[175,171,175,412,173,171,173,173,171],4.2,[175,171,178,414,173,171,173,173,171],4.1,[175,171,181,416,173,171,173,173,171],0.7,[175,171,184,416,173,171,173,173,171],[175,171,187,390,173,171,173,173,171],[175,171,190,309,173,171,173,173,171],[175,171,255,309,173,171,173,173,171],[175,171,102,422,173,171,173,173,171],2.5,[175,171,276,424,173,171,173,173,171],0.9,[175,171,287,390,173,171,173,173,171],[175,171,298,427,173,171,173,173,171],1.5,[178,171,171,407,173,171,173,173,171],[178,171,175,392,173,171,173,173,171],[178,171,178,431,173,171,173,173,171],5.4,[178,171,181,416,173,171,173,173,171],[178,171,184,424,173,171,173,173,171],[178,171,187,175,173,171,173,173,171],[178,171,190,309,173,171,173,173,171],[178,171,255,399,173,171,173,173,171],[178,171,102,438,173,171,173,173,171],2.9,[178,171,276,440,173,171,173,173,171],2.1,[178,171,287,407,173,171,173,173,171],[178,171,298,443,173,171,173,173,171],1.6,[181,171,171,405,173,171,173,173,171],[181,171,175,446,173,171,173,173,171],15.9,[181,171,178,448,173,171,173,173,171],3.2,[181,171,181,390,173,171,173,173,171],[181,171,184,416,173,171,173,173,171],[181,171,187,396,173,171,173,173,171],[181,171,190,390,173,171,173,173,171],[181,171,255,454,173,171,173,173,171],0.5,[181,171,102,440,173,171,173,173,171],[181,171,276,440,173,171,173,173,171],[181,171,287,175,173,171,173,173,171],[181,171,298,459,173,171,173,173,171],2.4,[184,171,171,175,173,171,173,173,171],[184,171,175,462,173,171,173,173,171],13.8,[184,171,178,464,173,171,173,173,171],7.1,[184,171,181,424,173,171,173,173,171],[184,171,184,399,173,171,173,173,171],[184,171,187,396,173,171,173,173,171],[184,171,190,396,173,171,173,173,171],[184,171,255,175,173,171,173,173,171],[184,171,102,471,173,171,173,173,171],3.4,[184,171,276,407,173,171,173,173,171],[184,171,287,405,173,171,173,173,171],[184,171,298,448,173,171,173,173,171],[187,171,171,427,173,171,173,173,171],[187,171,175,477,173,171,173,173,171],17.2,[187,171,178,479,173,171,173,173,171],17.9,[187,171,181,390,173,171,173,173,171],[187,171,184,399,173,171,173,173,171],[187,171,187,416,173,171,173,173,171],[187,171,190,390,173,171,173,173,171],[187,171,255,454,173,171,173,173,171],[187,171,102,486,173,171,173,173,171],3.8,[187,171,276,407,173,171,173,173,171],[187,171,287,405,173,171,173,173,171],[187,171,298,412,173,171,173,173,171],[],[492],"Table II (version of record)",[],[],[496],"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",{"slug":498,"group":499,"sourceId":5,"sourceLabel":6,"table":500,"selfRows":307,"metrics":501,"seqs":503,"entrants":516,"cells":526,"outcomes":629,"locators":630,"hardware":632,"wordings":633,"notes":634},"hba2023-table-v","hba2023:Table V","Table 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V (version of record)",[],[],[635],"KITTI with MULLS poses without loop closure as HBA input; RMSE of ATE printed as rotation (deg)\u002Ftranslation (m); only translation extracted; baselines run without loop closure; the '(loops)' sequence labels follow the asterisks of arXiv v1 Table V (the version-of-record table image carries no asterisks); version of record adds CT-ICP and LOAM rows",{"slug":637,"group":638,"sourceId":639,"sourceLabel":640,"table":641,"selfRows":307,"metrics":642,"seqs":653,"entrants":667,"cells":674,"outcomes":744,"locators":745,"hardware":746,"wordings":747,"notes":748},"lemon2026-table-iii","lemon2026:Table III","lemon2026","Wang et al., 2026","Table 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study on self-collected Mid360 data; spatial BA versus BALM2 (sliding window) and HBA, all on raw odometry without loop-based refinement; z-drift and z-RMSE relative to the z-value of the first frame; MME via MapEval (lower is better)",[751,757,763,770],{"group":752,"slug":753,"sourceLabel":6,"table":754,"selfRows":190,"datasets":755},"hba2023:Table VIII","hba2023-table-viii","Table VIII",[756],"MulRan",{"group":758,"slug":759,"sourceLabel":6,"table":760,"selfRows":178,"datasets":761},"hba2023:Table VI","hba2023-table-vi","Table VI",[762],"self-collected",{"group":764,"slug":765,"sourceLabel":766,"table":767,"selfRows":178,"datasets":768},"pinslam2024:Table IV","pinslam2024-table-iv","Pan et al., 2024","Table IV",[769],"KITTI odometry",{"group":771,"slug":772,"sourceLabel":6,"table":767,"selfRows":175,"datasets":773},"hba2023:Table IV","hba2023-table-iv",[774],"New College (Newer College dataset family)",1790510654913]