[{"data":1,"prerenderedAt":782},["ShallowReactive",2],{"method-yuan2022voxelmap":3},{"method":4,"reference":63,"equipment":87,"figures":139,"results":140},{"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":30,"sensors":37,"platform":42,"estimator":48,"association":49,"timeModel":50,"deskew":51,"loopClosure":52,"globalOptimization":52,"mapRepresentation":53,"prior":52,"outputGeometry":54,"compute":55,"codeUrl":56,"codeLicense":57,"relatedVersions":58},"yuan2022voxelmap","Yuan et al., 2022","VoxelMap","Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry",2022,"recent","C05","odometry_with_local_mapping","VoxelMap 把空間切成以雜湊表索引的根體素，每個根體素再以八元樹由粗到細細分，直到內部點足以擬合一個平面；每個平面同時估計參數與共變異數，共變異數來自 LiDAR 測距與方位雜訊及位姿估計誤差的傳播。新點以考慮點與平面不確定性的點對面距離配準，並在迭代擴展卡爾曼濾波（IEKF）中形成最大後驗估計。","An adaptive, coarse-to-fine voxel map (hash table plus per-voxel octree) storing plane features with covariances derived from LiDAR noise and pose error, used for probabilistic point-to-plane registration in an IEKF.","full_text_reviewed","peer_reviewed_published","main_body","論文未涉及營建場域，也未評估地圖幾何精度；最接近的情境是手持 L515 在實驗室與倉庫的室內資料（Table IV），以及無人機載 Livox Avia 朝地面掃描山區地形（Sec. IV-C2）。每個平面附帶共變異數，可作為工程點雲品質指標的候選概念，此點屬推論。",[20,21],"public_benchmark","controlled_experiment",[23,24,25,26,27,28,29],"Best overall KITTI ATE among compared odometry methods with loop closure off, especially on long sequences 00, 02 and 08","average 1.2 deg \u002F 2.9 m (Table II)","ablation shows the probabilistic plane module matters more than adaptive voxelization (averages 1.8\u002F4.5 without it vs 1.3\u002F3.4 without adaptive voxels, Table II)","lowest KITTI time per scan, 26.45 ms vs 29.11 ms for FAST-LIO2 (Table III) thanks to near O(1) hash lookup","handheld L515 end-to-end error 0.01 to 0.02 m vs 0.71 to 5.80 m for SSL_SLAM (Table IV)","lower end-to-end error than Faster-LIO and FAST-LIO2 on all four Livox Avia sequences with KITTI parameters (Table V)","works across spinning, solid-state and non-repetitive LiDARs (abstract, Sec. V)",[31,32,33,34,35,36],"No loop closure","KITTI baselines compared with loop closure disabled (Sec. IV-A)","only plane features, adding edge features left to future work (Sec. V)","extra probability computation per point and plane costs time relative to Faster-LIO, e.g. 34.2 ms vs 32.0 ms on the mountain (Sec. IV-C3, Table V)","gain on the slow UGV SSL_SLAM dataset is small, 0.12 m vs 0.15 m (Sec. IV-B)","(reviewer observation) own datasets are evaluated only by end-to-end error without ground-truth trajectories or map accuracy",[38,39,40,41],"Velodyne HDL-64E S2 mechanical LiDAR (KITTI, 10 Hz, 360°×32°)","Intel RealSense L515 solid-state LiDAR (30 Hz, 70°×55°)","Livox Avia non-repetitive solid-state LiDAR (10 Hz, 70°×77°)","Livox Avia built-in IMU at 200 Hz, used only in the LiDAR-inertial experiment",[43,44,45,46,47],"KITTI odometry urban sequences (vehicle platform per KITTI, not described in this paper)","handheld device with Intel L515 in laboratory and warehouse","handheld device with Livox Avia in a park","UAV carrying the Livox Avia facing the ground over a mountain","UGV data of the SSL_SLAM dataset (re-run only)","iterated extended Kalman filter on the IKFoM framework, similar to FAST-LIO2, solved as a MAP problem; prior from a constant-velocity model (LiDAR-only: KITTI and L515) or IMU propagation (LiDAR-inertial: Livox Avia); observation noise of each residual propagated from plane covariance and raw point noise","point-to-plane: the predicted world point finds its root voxel by hash key, all sub-voxel planes are polled; a match is accepted if the point-to-plane distance lies within 3σ of its distribution (σ from plane covariance and point covariance), the most probable plane is chosen when several pass, and points passing no test are discarded","discrete poses","KITTI: in-frame motion already compensated in the dataset, plus a 0.22° vertical angle correction as in IMLS-SLAM (Sec. IV-A); L515: no motion compensation described, the constant-velocity model only provides the state prior (Sec. IV-B, III-D); Livox Avia: built-in IMU compensates motion distortion and provides the prior, similar to FAST-LIO2 (Sec. IV-C)","none","adaptive coarse-to-fine voxel map: hash table of root voxels at the coarse resolution, each split as an octree until its points pass a planarity test (minimum eigenvalue below a threshold) or the maximum layer is reached; each (sub)voxel holds one plane (normal, center) with covariance; 3 m root voxels with 3 layers (minimum 0.375 m) for KITTI and Livox Avia, 0.5 m with 2 layers for L515; a simulation in Fig. 4 (point noise variance 0.1 m^2) shows the normal covariance converging once about 50 points are reached, and after convergence the method discards the historical points, keeps the plane parameters and covariance, and uses the latest 10 points to detect change and trigger reconstruction","LiDAR poses and a probabilistic plane-feature voxel map; an aggregated colored point cloud of registered scans is shown for the L515 warehouse run (colors from the L515, Supp. Fig. 1); raw points inside a voxel are discarded once its plane converges, so the plane map does not keep all raw points; export format not_reported","desktop Intel i7-10700 @ 2.9 GHz, 16 GB RAM, CPU only (GTX 730 2 GB used only by the GPU baseline SuMa); KITTI mean 26.45 ms (std 9.87 ms) per scan (Table III); L515 6.38 to 7.26 ms per scan (Table IV); Livox Avia 7.7 to 8.3 ms per scan in the park and 34.2 ms on the mountain, averaged over 5 runs (Table V)","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FVoxelMap","GPL-2.0 (per repository README)",[59],{"relation":60,"title":61,"doi_or_url":62},"preprint","arXiv:2109.07082 (v1 2021-09-15, up to v5)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.07082",{"id":5,"kind":64,"shortName":7,"title":8,"authors":65,"year":9,"venue":71,"venueType":72,"publisher":73,"volumeIssuePages":74,"doi":75,"arxivId":76,"url":62,"firstPublicDate":77,"publicationStatus":16,"metadataStatus":78,"fulltextStatus":15,"era":10,"classicReason":79,"codeUrl":56,"cluster":11,"topics":80,"mdpi":83,"verification":84,"label":6,"fulltextRoute":85,"versionRead":86,"addedByCensus":83},"method",[66,67,68,69,70],"Chongjian Yuan","Wei Xu","Xiyuan Liu","Xiaoping Hong","Fu Zhang","IEEE Robotics and Automation Letters","journal","IEEE","7(3):8518-8525","10.1109\u002Flra.2022.3187250","2109.07082","2021-09-15","metadata_verified","not_applicable",[11,81,82],"C12","C13",false,"corrected","NTU institutional (curl)","IEEE RA-L version of record, vol. 7, no. 3, pp. 8518-8525 (date of publication 2022-07-01, current version 2022-07-12), compared word by word with arXiv 2109.07082v5 (2022-07-08); supplementary material PDF from the GitHub repository also read",[88,96,103,107,112,117,121,125,129,135],{"category":89,"model":90,"canonical":91,"role":92,"dataset":93,"specs":94,"locator":95},"lidar","Velodyne HDL-64E S2","Velodyne HDL-64E","dataset sensor","KITTI odometry","mechanical, 10 Hz, repetitive scan, FoV 360°×32°; KITTI in-frame motion pre-compensated; 0.22° vertical angle correction applied as in IMLS-SLAM","Table I; Sec. IV-A",{"category":89,"model":97,"canonical":98,"role":99,"dataset":100,"specs":101,"locator":102},"Realsense L515","Intel RealSense L515","method input",null,"solid-state, 30 Hz, repetitive scan, FoV 70°×55° (Table I); written as 'Intel L515' in Sec. IV-B; point colors in the supplementary warehouse map are provided by the L515 (Supp. Fig. 1 caption)","Table I; Sec. IV-B; Supp. Fig. 1",{"category":89,"model":104,"canonical":104,"role":99,"dataset":100,"specs":105,"locator":106},"Livox Avia","solid-state, 10 Hz, non-repetitive scan, FoV 70°×77°","Table I; Sec. IV-C",{"category":108,"model":109,"canonical":109,"role":99,"dataset":100,"specs":110,"locator":111},"imu","Livox Avia built-in IMU","200 Hz; used for motion-distortion compensation and as motion prior (LiDAR-inertial mode)","Sec. IV-C",{"category":113,"model":114,"canonical":114,"role":99,"dataset":100,"specs":115,"locator":116},"platform","handheld device (L515)","three sequences in laboratory and warehouse; hand-carried with much faster motion (especially rotation) than the slow UGV data of SSL_SLAM; routes start and end at the same place","Sec. IV-B",{"category":113,"model":118,"canonical":118,"role":99,"dataset":100,"specs":119,"locator":120},"handheld device (Livox Avia)","park with trees; two 485 m loops and one 815 m loop starting and ending at the same place","Sec. IV-C1",{"category":113,"model":122,"canonical":122,"role":99,"dataset":100,"specs":123,"locator":124},"UAV","carries the Livox Avia facing the ground over a mountain, 3490 m trajectory; outdoor aerial experiment helped by Ambit-Geospatial (Acknowledgment)","Sec. IV-C2; Table V; Acknowledgment",{"category":113,"model":126,"canonical":126,"role":92,"dataset":127,"specs":128,"locator":116},"UGV","SSL_SLAM dataset [33]","moving quite stably and slowly (sensor not restated in this paper)",{"category":130,"model":131,"canonical":131,"role":132,"dataset":100,"specs":133,"locator":134},"compute","Intel i7-10700","compute for runtime","desktop computer, 2.9 GHz, 16 GB RAM; used for all experiments","Sec. IV (intro); Sec. IV-C",{"category":130,"model":136,"canonical":136,"role":132,"dataset":100,"specs":137,"locator":138},"Nvidia GeForce GTX 730","2 GB RAM; used only when a method needs a GPU (SuMa)","Sec. IV (intro); Sec. IV-A Runtime",[],{"totalRows":141,"groupCount":142,"groups":143,"others":731},76,13,[144,379,533,652],{"slug":145,"group":146,"sourceId":5,"sourceLabel":6,"table":147,"selfRows":142,"metrics":148,"seqs":156,"entrants":183,"cells":205,"outcomes":372,"locators":374,"hardware":375,"wordings":376,"notes":377},"yuan2022voxelmap-table-ii","yuan2022voxelmap:Table II","Table II",[149,153],{"label":150,"unit":151,"statistic":152,"alignment":152},"ATE (translation part) [m]","m","not_reported",{"label":154,"unit":155,"statistic":152,"alignment":152},"ATE (rotation part) [deg]","deg",[157,161,163,165,167,169,171,173,175,177,179,181],{"dataset":158,"sequence":159,"environment":160},"KITTI odometry (training)","00 (3724 m)","urban (Velodyne HDL-64E S2 data, in-frame motion pre-compensated)",{"dataset":158,"sequence":162,"environment":160},"01 (2453 m)",{"dataset":158,"sequence":164,"environment":160},"02 (5067 m)",{"dataset":158,"sequence":166,"environment":160},"03 (560 m)",{"dataset":158,"sequence":168,"environment":160},"04 (393 m)",{"dataset":158,"sequence":170,"environment":160},"05 (2205 m)",{"dataset":158,"sequence":172,"environment":160},"06 (1232 m)",{"dataset":158,"sequence":174,"environment":160},"07 (694 m)",{"dataset":158,"sequence":176,"environment":160},"08 (3222 m)",{"dataset":158,"sequence":178,"environment":160},"09 (1705 m)",{"dataset":158,"sequence":180,"environment":160},"10 (919 m)",{"dataset":158,"sequence":182,"environment":160},"Avg. of all scans",[184,187,189,191,193,196,199,202],{"name":185,"methodId":5,"linkable":186,"proposed":186,"self":186},"Ours (full)",true,{"name":188,"methodId":100,"linkable":83,"proposed":83,"self":83},"Ours (w\u002Fo adaptive)",{"name":190,"methodId":100,"linkable":83,"proposed":83,"self":83},"Ours (w\u002Fo prob.)",{"name":192,"methodId":100,"linkable":83,"proposed":83,"self":83},"LiTAMIN2 [24]",{"name":194,"methodId":195,"linkable":186,"proposed":83,"self":83},"MULLS [30]","mulls2021",{"name":197,"methodId":198,"linkable":186,"proposed":83,"self":83},"Suma [14]","suma2018",{"name":200,"methodId":201,"linkable":186,"proposed":83,"self":83},"FAST-LIO2 [13]","fastlio2_2022",{"name":203,"methodId":204,"linkable":186,"proposed":83,"self":83},"Lego-Loam [7]","legoloam2018",[206,210,213,216,219,222,225,228,230,233,236,239,242,243,245,247,249,250,251,253,255,256,257,258,259,261,262,264,266,268,270,271,272,274,275,277,278,280,282,284,286,288,290,292,293,295,296,297,299,301,302,304,305,306,307,309,310,311,312,313,314,316,317,318,320,321,322,324,326,327,328,329,330,331,332,333,334,335,337,339,341,343,344,345,346,347,348,349,350,351,353,354,356,358,360,361,362,363,364,365,367,368,369,371],[207,207,207,208,209,207,209,209,207],0,2.8,-1,[207,207,211,212,209,207,209,209,207],1,7.8,[207,207,214,215,209,207,209,209,207],2,6.1,[207,207,217,218,209,207,209,209,207],3,0.7,[207,207,220,221,209,207,209,209,207],4,0.3,[207,207,223,224,209,207,209,209,207],5,1.2,[207,207,226,227,209,207,209,209,207],6,0.4,[207,207,229,218,209,207,209,209,207],7,[207,207,231,232,209,207,209,209,207],8,2.3,[207,207,234,235,209,207,209,209,207],9,1.9,[207,207,237,238,209,207,209,209,207],10,1.1,[207,207,240,241,209,207,209,209,207],11,2.9,[207,211,240,224,209,207,209,209,207],[211,207,207,244,209,207,209,209,207],3.1,[211,207,211,246,209,207,209,209,207],8.2,[211,207,214,248,209,207,209,209,207],7.1,[211,207,217,218,209,207,209,209,207],[211,207,220,100,207,207,209,209,207],[211,207,223,252,209,207,209,209,207],1.3,[211,207,226,254,209,207,209,209,207],0.5,[211,207,229,218,209,207,209,209,207],[211,207,231,244,209,207,209,209,207],[211,207,234,214,209,207,209,209,207],[211,207,237,224,209,207,209,209,207],[211,207,240,260,209,207,209,209,207],3.4,[211,211,240,252,209,207,209,209,207],[214,207,207,263,209,207,209,209,207],3.9,[214,207,211,265,209,207,209,209,207],9.5,[214,207,214,267,209,207,209,209,207],8.1,[214,207,217,269,209,207,209,209,207],0.9,[214,207,220,227,209,207,209,209,207],[214,207,223,224,209,207,209,209,207],[214,207,226,273,209,207,209,209,207],0.6,[214,207,229,273,209,207,209,209,207],[214,207,231,276,209,207,209,209,207],6.8,[214,207,234,235,209,207,209,209,207],[214,207,237,279,209,207,209,209,207],1.5,[214,207,240,281,209,207,209,209,207],4.5,[214,211,240,283,209,207,209,209,207],1.8,[217,207,207,285,209,207,209,209,207],5.8,[217,207,211,287,209,207,209,209,207],15.9,[217,207,214,289,209,207,209,209,207],10.7,[217,207,217,291,209,207,209,209,207],0.8,[217,207,220,218,209,207,209,209,207],[217,207,223,294,209,207,209,209,207],2.4,[217,207,226,269,209,207,209,209,207],[217,207,229,273,209,207,209,209,207],[217,207,231,298,209,207,209,209,207],2.5,[217,207,234,300,209,207,209,209,207],2.1,[217,207,237,211,209,207,209,209,207],[217,207,240,303,209,207,209,209,207],5.1,[217,211,240,283,209,207,209,209,207],[220,207,207,215,209,207,209,209,207],[220,207,211,294,209,207,209,209,207],[220,207,214,308,209,207,209,209,207],10.6,[220,207,217,218,209,207,209,209,207],[220,207,220,269,209,207,209,209,207],[220,207,223,294,209,207,209,209,207],[220,207,226,273,209,207,209,209,207],[220,207,229,273,209,207,209,209,207],[220,207,231,315,209,207,209,209,207],4.3,[220,207,234,298,209,207,209,209,207],[220,207,237,238,209,207,209,209,207],[220,207,240,319,209,207,209,209,207],4.8,[220,211,240,279,209,207,209,209,207],[223,207,207,241,209,207,209,209,207],[223,207,211,323,209,207,209,209,207],13.8,[223,207,214,325,209,207,209,209,207],8.4,[223,207,217,269,209,207,209,209,207],[223,207,220,227,209,207,209,209,207],[223,207,223,224,209,207,209,209,207],[223,207,226,227,209,207,209,209,207],[223,207,229,254,209,207,209,209,207],[223,207,231,208,209,207,209,209,207],[223,207,234,241,209,207,209,209,207],[223,207,237,252,209,207,209,209,207],[223,207,240,263,209,207,209,209,207],[223,211,240,336,209,207,209,209,207],1.4,[226,207,207,338,209,207,209,209,207],3.8,[226,207,211,340,209,207,209,209,207],11.9,[226,207,214,342,209,207,209,209,207],12.8,[226,207,217,291,209,207,209,209,207],[226,207,220,254,209,207,209,209,207],[226,207,223,300,209,207,209,209,207],[226,207,226,224,209,207,209,209,207],[226,207,229,291,209,207,209,209,207],[226,207,231,217,209,207,209,209,207],[226,207,234,214,209,207,209,209,207],[226,207,237,279,209,207,209,209,207],[226,207,240,352,209,207,209,209,207],4.9,[226,211,240,283,209,207,209,209,207],[229,207,207,355,209,207,209,209,207],6.3,[229,207,211,357,209,207,209,209,207],119.4,[229,207,214,359,209,207,209,209,207],14.7,[229,207,217,269,209,207,209,209,207],[229,207,220,291,209,207,209,209,207],[229,207,223,208,209,207,209,209,207],[229,207,226,291,209,207,209,209,207],[229,207,229,218,209,207,209,209,207],[229,207,231,366,209,207,209,209,207],3.5,[229,207,234,300,209,207,209,209,207],[229,207,237,283,209,207,209,209,207],[229,207,240,370,209,207,209,209,207],11.1,[229,211,240,208,209,207,209,209,207],[373],"other: cell printed as '0.7\u002F0\u002F5' in both the IEEE version and arXiv v5; translation value ambiguous (typo in source)",[147],[],[],[378],"KITTI odometry training sequences 00 to 10, LiDAR odometry with loop closure off for all methods; each cell printed as rotation [deg] \u002F translation [m] (unit label '[deg]\u002F[m]' printed in the header of the average column; caption says ATE in meters). All VoxelMap variants use identical parameters on all sequences (max voxel 3 m, 3 octree layers). Ours (w\u002Fo adaptive) = ablation with fixed 2 m voxels; Ours (w\u002Fo prob.) = ablation with zero plane uncertainty. FAST-LIO2 run by the authors with a constant-velocity prior (no IMU in KITTI); MULLS run by the authors with default parameters; LiTAMIN2, SuMa and LeGO-LOAM values copied from the LiTAMIN2 paper [24]. Rotation part kept only for the average column (truncation, see notes).",{"slug":380,"group":381,"sourceId":382,"sourceLabel":383,"table":384,"selfRows":385,"metrics":386,"seqs":390,"entrants":417,"cells":434,"outcomes":526,"locators":527,"hardware":529,"wordings":530,"notes":531},"hba2023-table-v","hba2023:Table V","hba2023","Liu et al., 2023b","Table V",12,[387],{"label":388,"unit":151,"statistic":389,"alignment":152},"RMSE of the ATE, translation part of deg\u002Fm pair","RMSE",[391,395,397,399,401,403,405,407,409,411,413,415],{"dataset":392,"sequence":393,"environment":394},"KITTI","Seq. 00 (loops)","urban and rural driving",{"dataset":392,"sequence":396,"environment":394},"Seq. 01",{"dataset":392,"sequence":398,"environment":394},"Seq. 02 (loops)",{"dataset":392,"sequence":400,"environment":394},"Seq. 03",{"dataset":392,"sequence":402,"environment":394},"Seq. 04",{"dataset":392,"sequence":404,"environment":394},"Seq. 05 (loops)",{"dataset":392,"sequence":406,"environment":394},"Seq. 06 (loops)",{"dataset":392,"sequence":408,"environment":394},"Seq. 07 (loops)",{"dataset":392,"sequence":410,"environment":394},"Seq. 08 (loops)",{"dataset":392,"sequence":412,"environment":394},"Seq. 09 (loops)",{"dataset":392,"sequence":414,"environment":394},"Seq. 10",{"dataset":392,"sequence":416,"environment":394},"Avg.",[418,420,423,425,427,429,431],{"name":419,"methodId":382,"linkable":186,"proposed":186,"self":83},"Proposed",{"name":421,"methodId":422,"linkable":186,"proposed":83,"self":83},"CT-ICP","cticp2022",{"name":424,"methodId":195,"linkable":186,"proposed":83,"self":83},"MULLS",{"name":426,"methodId":5,"linkable":186,"proposed":83,"self":186},"Voxel Map",{"name":428,"methodId":198,"linkable":186,"proposed":83,"self":83},"SuMa",{"name":430,"methodId":100,"linkable":83,"proposed":83,"self":83},"LiTAMIN2",{"name":432,"methodId":433,"linkable":186,"proposed":83,"self":83},"LOAM","loam2014",[435,436,437,438,439,440,441,443,444,445,446,447,448,449,450,452,453,454,455,456,457,458,459,460,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,513,515,516,517,518,519,520,522,524,525],[207,207,207,224,209,207,209,209,207],[207,207,211,294,209,207,209,209,207],[207,207,214,234,209,207,209,209,207],[207,207,217,273,209,207,209,209,207],[207,207,220,269,209,207,209,209,207],[207,207,223,218,209,207,209,209,207],[207,207,226,442,209,207,209,209,207],0.2,[207,207,229,221,209,207,209,209,207],[207,207,231,298,209,207,209,209,207],[207,207,234,279,209,207,209,209,207],[207,207,237,238,209,207,209,209,207],[207,207,240,235,209,207,209,209,207],[211,207,207,281,209,207,209,209,207],[211,207,211,315,209,207,209,209,207],[211,207,214,451,209,207,209,209,207],7.5,[211,207,217,218,209,207,209,209,207],[211,207,220,218,209,207,209,209,207],[211,207,223,336,209,207,209,209,207],[211,207,226,227,209,207,209,209,207],[211,207,229,227,209,207,209,209,207],[211,207,231,298,209,207,209,209,207],[211,207,234,252,209,207,209,209,207],[211,207,237,291,209,207,209,209,207],[211,207,240,461,209,207,209,209,207],2.2,[214,207,207,215,209,207,209,209,207],[214,207,211,294,209,207,209,209,207],[214,207,214,289,209,207,209,209,207],[214,207,217,218,209,207,209,209,207],[214,207,220,269,209,207,209,209,207],[214,207,223,294,209,207,209,209,207],[214,207,226,273,209,207,209,209,207],[214,207,229,273,209,207,209,209,207],[214,207,231,315,209,207,209,209,207],[214,207,234,244,209,207,209,209,207],[214,207,237,238,209,207,209,209,207],[214,207,240,217,209,207,209,209,207],[217,207,207,208,209,207,209,209,207],[217,207,211,212,209,207,209,209,207],[217,207,214,215,209,207,209,209,207],[217,207,217,218,209,207,209,209,207],[217,207,220,221,209,207,209,209,207],[217,207,223,224,209,207,209,209,207],[217,207,226,227,209,207,209,209,207],[217,207,229,218,209,207,209,209,207],[217,207,231,232,209,207,209,209,207],[217,207,234,235,209,207,209,209,207],[217,207,237,238,209,207,209,209,207],[217,207,240,241,209,207,209,209,207],[220,207,207,241,209,207,209,209,207],[220,207,211,323,209,207,209,209,207],[220,207,214,325,209,207,209,209,207],[220,207,217,269,209,207,209,209,207],[220,207,220,227,209,207,209,209,207],[220,207,223,224,209,207,209,209,207],[220,207,226,227,209,207,209,209,207],[220,207,229,254,209,207,209,209,207],[220,207,231,208,209,207,209,209,207],[220,207,234,241,209,207,209,209,207],[220,207,237,252,209,207,209,209,207],[220,207,240,263,209,207,209,209,207],[223,207,207,285,209,207,209,209,207],[223,207,211,287,209,207,209,209,207],[223,207,214,289,209,207,209,209,207],[223,207,217,291,209,207,209,209,207],[223,207,220,218,209,207,209,209,207],[223,207,223,294,209,207,209,209,207],[223,207,226,269,209,207,209,209,207],[223,207,229,273,209,207,209,209,207],[223,207,231,298,209,207,209,209,207],[223,207,234,300,209,207,209,209,207],[223,207,237,211,209,207,209,209,207],[223,207,240,303,209,207,209,209,207],[226,207,207,232,209,207,209,209,207],[226,207,211,512,209,207,209,209,207],17.7,[226,207,214,514,209,207,209,209,207],37.9,[226,207,217,291,209,207,209,209,207],[226,207,220,227,209,207,209,209,207],[226,207,223,232,209,207,209,209,207],[226,207,226,291,209,207,209,209,207],[226,207,229,254,209,207,209,209,207],[226,207,231,521,209,207,209,209,207],3.7,[226,207,234,523,209,207,209,209,207],1.6,[226,207,237,252,209,207,209,209,207],[226,207,240,355,209,207,209,209,207],[],[528],"Table V (version of record)",[],[],[532],"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":534,"group":535,"sourceId":536,"sourceLabel":537,"table":147,"selfRows":231,"metrics":538,"seqs":541,"entrants":557,"cells":564,"outcomes":622,"locators":623,"hardware":624,"wordings":625,"notes":650},"glim2024-table-ii","glim2024:Table II","glim2024","Koide et al., 2024",[539],{"label":540,"unit":151,"statistic":152,"alignment":152},"Absolute Trajectory Error [m], reported as value ± (± not defined in the paper)",[542,545,547,548,549,551,553,555],{"dataset":543,"sequence":396,"environment":544},"authors' flat-wall degeneration sequences","indoor flat wall between pillars (real range degeneration)",{"dataset":543,"sequence":546,"environment":544},"Seq. 02",{"dataset":543,"sequence":400,"environment":544},{"dataset":543,"sequence":402,"environment":544},{"dataset":543,"sequence":550,"environment":544},"Seq. 05",{"dataset":543,"sequence":552,"environment":544},"Seq. 06",{"dataset":543,"sequence":554,"environment":544},"Seq. 07",{"dataset":543,"sequence":556,"environment":544},"Seq. 08",[558,560,562],{"name":559,"methodId":201,"linkable":186,"proposed":83,"self":83},"FAST-LIO2 [5]",{"name":561,"methodId":5,"linkable":186,"proposed":83,"self":186},"VoxelMap [61]",{"name":563,"methodId":536,"linkable":186,"proposed":186,"self":83},"GLIM",[565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,596,599,602,605,608,611,614,617,620],[207,207,207,566,209,207,209,207,207],0.815,[211,207,207,568,209,207,209,211,207],0.577,[214,207,207,570,209,207,209,214,207],0.118,[207,207,211,572,209,207,209,217,207],0.822,[211,207,211,574,209,207,209,220,207],0.146,[214,207,211,576,209,207,209,223,207],0.299,[207,207,214,578,209,207,209,226,207],0.873,[211,207,214,580,209,207,209,229,207],0.95,[214,207,214,582,209,207,209,231,207],0.04,[207,207,217,584,209,207,209,234,207],1.137,[211,207,217,586,209,207,209,237,207],0.586,[214,207,217,588,209,207,209,240,207],0.389,[207,207,220,590,209,207,209,385,207],1.048,[211,207,220,592,209,207,209,142,207],0.786,[214,207,220,594,209,207,209,595,207],0.228,14,[207,207,223,597,209,207,209,598,207],15.551,15,[211,207,223,600,209,207,209,601,207],0.807,16,[214,207,223,603,209,207,209,604,207],0.056,17,[207,207,226,606,209,207,209,607,207],0.635,18,[211,207,226,609,209,207,209,610,207],0.366,19,[214,207,226,612,209,207,209,613,207],0.017,20,[207,207,229,615,209,207,209,616,207],0.297,21,[211,207,229,618,209,207,209,619,207],0.279,22,[214,207,229,574,209,207,209,621,207],23,[],[147],[],[626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649],"Absolute Trajectory Error [m], reported as value ± 0.5 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.157 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.047 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.255 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.057 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.116 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.364 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.263 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.015 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.412 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.299 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.145 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.582 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.4 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.083 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 8.78 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.309 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.023 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.218 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.111 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.007 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.11 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.082 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.066 (± not defined in the paper)",[651],"Eight real sequences (path 2.2-4.8 m) with a Livox Avia moved between two pillars while facing a flat wall; ground truth from AprilTag bundle adjustment and camera-IMU batch optimization; LIO-SAM gave no decent result",{"slug":653,"group":654,"sourceId":5,"sourceLabel":6,"table":384,"selfRows":231,"metrics":655,"seqs":662,"entrants":674,"cells":680,"outcomes":724,"locators":725,"hardware":726,"wordings":728,"notes":729},"yuan2022voxelmap-table-v","yuan2022voxelmap:Table V",[656,659],{"label":657,"unit":151,"statistic":658,"alignment":152},"End-to-End error (m), mean of 5 runs","mean",{"label":660,"unit":661,"statistic":658,"alignment":152},"Avg. Comp. Time (ms), mean of 5 runs","ms",[663,667,669,671],{"dataset":664,"sequence":665,"environment":666},"authors' Livox Avia datasets","Park1 (485 m)","outdoor park with trees, handheld",{"dataset":664,"sequence":668,"environment":666},"Park2 (485 m)",{"dataset":664,"sequence":670,"environment":666},"Park3 (815 m)",{"dataset":664,"sequence":672,"environment":673},"Mountain (3490 m)","unstructured mountain terrain, UAV-borne, LiDAR facing the ground",[675,676,679],{"name":185,"methodId":5,"linkable":186,"proposed":186,"self":186},{"name":677,"methodId":678,"linkable":186,"proposed":83,"self":83},"Faster-LIO [35]","fasterlio2022",{"name":200,"methodId":201,"linkable":186,"proposed":83,"self":83},[681,683,684,686,688,690,692,694,696,698,700,702,703,705,707,708,710,712,714,716,718,720,721,722],[207,207,207,682,209,207,209,209,207],0.03,[207,211,207,267,209,207,207,209,207],[207,207,211,685,209,207,209,209,207],0.02,[207,211,211,687,209,207,207,209,207],8.3,[207,207,214,689,209,207,209,209,207],0.01,[207,211,214,691,209,207,207,209,207],7.7,[207,207,217,693,209,207,209,209,207],6.9,[207,211,217,695,209,207,207,209,207],34.2,[211,207,207,697,209,207,209,209,207],0.88,[211,211,207,699,209,207,207,209,207],9.8,[211,207,211,701,209,207,209,209,207],0.99,[211,211,211,699,209,207,207,209,207],[211,207,214,704,209,207,209,209,207],0.07,[211,211,214,706,209,207,207,209,207],9.9,[211,207,217,699,209,207,209,209,207],[211,211,217,709,209,207,207,209,207],32,[214,207,207,711,209,207,209,209,207],1.09,[214,211,207,713,209,207,207,209,207],12.9,[214,207,211,715,209,207,209,209,207],0.76,[214,211,211,717,209,207,207,209,207],13.4,[214,207,214,719,209,207,209,209,207],0.08,[214,211,214,713,209,207,207,209,207],[214,207,217,248,209,207,209,209,207],[214,211,217,723,209,207,207,209,207],44.3,[],[384],[727],"desktop Intel i7-10700 @ 2.9 GHz, 16 GB RAM (platform stated for all experiments, Sec. IV and IV-C); CPU or GPU use not stated for this experiment",[],[730],"Livox Avia with built-in IMU (10 Hz LiDAR, 200 Hz IMU), LiDAR-inertial mode; same parameters as KITTI (max voxel 3 m, 3 layers); all three methods share the IKFoM framework and plane features and differ only in map structure; default 1:3 downsampling of Faster-LIO and FAST-LIO2 turned off; results averaged over 5 runs. Park = handheld, loops start and end at the same place; Mountain = UAV with LiDAR facing the ground. Cells: end-to-end error (m) \u002F average computation time (ms).",[732,739,745,750,755,759,768,772,777],{"group":733,"slug":734,"sourceLabel":735,"table":736,"selfRows":229,"datasets":737},"voxelmappp2024:Table I","voxelmappp2024-table-i","Wu et al., 2024b","Table I",[738],"M2DGR",{"group":740,"slug":741,"sourceLabel":6,"table":742,"selfRows":226,"datasets":743},"yuan2022voxelmap:Table IV","yuan2022voxelmap-table-iv","Table IV",[744],"authors' L515 handheld datasets",{"group":746,"slug":747,"sourceLabel":735,"table":147,"selfRows":223,"datasets":748},"voxelmappp2024:Table II","voxelmappp2024-table-ii",[749],"VoxelMap++ own datasets",{"group":751,"slug":752,"sourceLabel":735,"table":753,"selfRows":223,"datasets":754},"voxelmappp2024:Table III","voxelmappp2024-table-iii","Table III",[749],{"group":756,"slug":757,"sourceLabel":735,"table":742,"selfRows":220,"datasets":758},"voxelmappp2024:Table IV","voxelmappp2024-table-iv",[749],{"group":760,"slug":761,"sourceLabel":762,"table":763,"selfRows":217,"datasets":764},"lee2024lidarodom_survey:Table 4","lee2024lidarodom-survey-table-4","Lee et al., 2024b","Table 4",[765,766,767],"ConSLAM","HeLiPR","NTU VIRAL",{"group":769,"slug":770,"sourceLabel":6,"table":753,"selfRows":214,"datasets":771},"yuan2022voxelmap:Table III","yuan2022voxelmap-table-iii",[158],{"group":773,"slug":774,"sourceLabel":6,"table":775,"selfRows":214,"datasets":776},"yuan2022voxelmap:Text Sec.IV-B","yuan2022voxelmap-text-sec-iv-b","Text Sec.IV-B",[127],{"group":778,"slug":779,"sourceLabel":537,"table":780,"selfRows":211,"datasets":781},"glim2024:Table X","glim2024-table-x","Table X",[767],1790510655556]