[{"data":1,"prerenderedAt":549},["ShallowReactive",2],{"method-litamin2_2021":3},{"method":4,"reference":56,"equipment":79,"figures":98,"results":99},{"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":26,"sensors":31,"platform":33,"estimator":35,"association":36,"timeModel":37,"deskew":38,"loopClosure":39,"globalOptimization":40,"mapRepresentation":41,"prior":42,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"litamin2_2021","Yokozuka et al., 2021","LiTAMIN2","LiTAMIN2: Ultra Light LiDAR-based SLAM using Geometric Approximation applied with KL-Divergence",2021,"recent","C04","full_slam_with_global_correction","LiTAMIN2 把每次 LiDAR 掃描的點投票到較大的體素（實驗採 3 m），每個體素只以一個常態分布近似，使參與配準的點數降到原始掃描的約 0.5%。為了在點數大減後維持精度，它在 ICP 成本中引入對稱 KL 散度：除了以共變異數加權的距離項，還加入比較兩個分布形狀的項，並以牛頓法求解。迴圈閉合與圖最佳化沿用前作 LiTAMIN，但迴圈約束改用新的成本計算。在 KITTI 上里程計可達每秒數百至上千幀，精度與 SuMa 相近。","ICP\u002FNDT-style LiDAR SLAM that approximates each large voxel (3 m) by one normal distribution and registers distribution to distribution with a symmetric KL-divergence cost (distance plus shape terms), reaching hundreds to over a thousand odometry frames per second on KITTI with SuMa-level accuracy; loop closure and pose graph as in LiTAMIN.","full_text_reviewed","peer_reviewed_published","background","論文只在 KITTI 車載道路資料上評估，沒有施工現場或建築室內資料。作者明確指出室內或狹窄環境需另選體素大小，因此其極低運算量是否能在施工室內維持精度尚未驗證。它在多篇後續論文中被列為比較基準，例如 [mulls2021]、[yuan2022voxelmap]、[hba2023]、[pinslam2024]。",[20],"public_benchmark",[22,23,24,25],"Odometry at 510 FPS (ICP term) or 239 FPS (ICP plus shape term) with 3 m voxels, and above 1000 FPS for coarser voxels with the ICP term only (Table II; Sec. V)","KITTI stats 0.33 deg\u002F100m and 0.85% with loop closure, similar to SuMa frame-to-model (0.32 and 0.89) (Table III)","Average ATE after loop closure 2.4 m versus 3.2 m for SuMa frame-to-model with loop closure (Table IV)","Loop closing succeeded on all sequences for LiTAMIN2, LiTAMIN and SuMa, while loops were not detected for LeGO-LOAM and hdl_graph_slam in the authors' runs (Sec. IV-D)",[27,28,29,30],"Evaluated only on KITTI; the authors state the voxel size must be chosen for indoor or confined environments and that choosing it when the environment changes needs further study (Sec. V; Sec. VI)","Adding the shape term roughly doubles the processing time compared with the ICP term alone (Sec. V)","Finer voxels do not always give better accuracy; accuracy degrades for voxels above about 4 m (Table II; Sec. IV-C)","Loop detection and graph optimization are only described by reference to LiTAMIN (Sec. III-C)",[32],"3D spinning LiDAR only (Velodyne HDL-64E S2 in KITTI)",[34],"vehicle (KITTI)","Newton's method with full Hessian (no Levenberg-Marquardt damping) on a Frobenius-normalized symmetric KL-divergence cost: covariance-weighted point distance term plus a distribution-shape term, each with a robust weight (sigma_ICP = 0.5, sigma_Cov = 3, lambda = 1e-6) (Sec. III-B; Sec. III-C)","input points voted into voxels (3 m in the experiments) and each voxel approximated by one normal distribution; distribution-to-distribution matching with correspondences found by k-d tree search in a voxel map of distributions (Sec. III-A; Sec. III-C; Sec. IV-C)","discrete poses","not described; KITTI clouds are already de-skewed and were fed directly to all methods (Sec. IV-B)","yes; implemented as in LiTAMIN, with the proposed ICP cost used to compute loop constraints; detection details are deferred to LiTAMIN (Sec. III-C)","graph optimizer implemented as in LiTAMIN (details not given in this paper) (Sec. III-C)","voxel map of normal distributions (mean and covariance per voxel) (Sec. III-A; Sec. III-C)","none","trajectory and voxelized normal-distribution map; Fig. 1 colours distributions by normal direction","tracking and mapping in one thread; with 3 m voxels odometry runs at 510 FPS (ICP term only) or 239 FPS (ICP plus shape term) and all KITTI sequences take 58 s or 119 s including loop closing, on a desktop PC with Intel Core i9-9900K and 32 GB RAM (the PC also had an RTX 2080 Ti; GPU use by LiTAMIN2 not stated) (Sec. III-C; Sec. IV-A; Table II; Table V)",null,"not_applicable (no official code found; a third-party reimplementation exists and is not listed)",[48,52],{"relation":49,"title":50,"doi_or_url":51},"preprint","LiTAMIN2 (arXiv v1)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.00784",{"relation":53,"title":54,"doi_or_url":55},"predecessor_method","LiTAMIN: LiDAR-based Tracking and MappINg by Stabilized ICP for Geometry Approximation with Normal Distributions (IROS 2020); folded into this entry, full text not read","https:\u002F\u002Fdoi.org\u002F10.1109\u002FIROS45743.2020.9341341",{"id":5,"kind":57,"shortName":7,"title":8,"authors":58,"year":9,"venue":63,"venueType":64,"publisher":65,"volumeIssuePages":66,"doi":67,"arxivId":68,"url":69,"firstPublicDate":70,"publicationStatus":16,"metadataStatus":71,"fulltextStatus":15,"era":10,"classicReason":72,"codeUrl":45,"cluster":11,"topics":73,"mdpi":74,"verification":75,"label":6,"fulltextRoute":76,"versionRead":77,"addedByCensus":78},"method",[59,60,61,62],"Masashi Yokozuka","Kenji Koide","Shuji Oishi","Atsuhiko Banno","2021 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 11619-11625","10.1109\u002Ficra48506.2021.9560947","2103.00784","https:\u002F\u002Fdoi.org\u002F10.1109\u002FICRA48506.2021.9560947","2021-03-01","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v1 (2021-03-01), the only arXiv version; IEEE ICRA 2021 version of record not read",true,[80,88,94],{"category":81,"model":82,"canonical":83,"role":84,"dataset":85,"specs":86,"locator":87},"lidar","Velodyne HDL-64E S2","Velodyne HDL-64E","dataset sensor","KITTI Vision Benchmark (odometry)","on-board KITTI sensor; point clouds provided already de-skewed","Sec. IV-B",{"category":89,"model":90,"canonical":90,"role":91,"dataset":45,"specs":92,"locator":93},"compute","Intel Core i9-9900K","compute for runtime","desktop PC with 32 GB RAM","Sec. IV-A",{"category":89,"model":95,"canonical":96,"role":91,"dataset":45,"specs":97,"locator":93},"NVIDIA GeForce RTX 2080 Ti","NVIDIA Geforce RTX2080Ti","GPU in the same desktop PC; used for all experiments (use by LiTAMIN2 itself not stated)",[],{"totalRows":100,"groupCount":101,"groups":102,"others":548},32,4,[103,397,452,506],{"slug":104,"group":105,"sourceId":5,"sourceLabel":6,"table":106,"selfRows":107,"metrics":108,"seqs":113,"entrants":138,"cells":167,"outcomes":391,"locators":392,"hardware":393,"wordings":394,"notes":395},"litamin2-2021-table-iii","litamin2_2021:Table III","Table III",22,[109],{"label":110,"unit":111,"statistic":112,"alignment":72},"KITTI stats translation [%]","%","mean",[114,118,120,122,124,126,128,130,132,134,136],{"dataset":115,"sequence":116,"environment":117},"KITTI odometry","00 (4541 frames)","vehicle, road",{"dataset":115,"sequence":119,"environment":117},"01 (1101 frames)",{"dataset":115,"sequence":121,"environment":117},"02 (4661 frames)",{"dataset":115,"sequence":123,"environment":117},"03 (801 frames)",{"dataset":115,"sequence":125,"environment":117},"04 (271 frames)",{"dataset":115,"sequence":127,"environment":117},"05 (2761 frames)",{"dataset":115,"sequence":129,"environment":117},"06 (1101 frames)",{"dataset":115,"sequence":131,"environment":117},"07 (1101 frames)",{"dataset":115,"sequence":133,"environment":117},"08 (4071 frames)",{"dataset":115,"sequence":135,"environment":117},"09 (1591 frames)",{"dataset":115,"sequence":137,"environment":117},"10 (1201 frames)",[139,141,143,145,148,150,153,156,159,162,165],{"name":140,"methodId":5,"linkable":78,"proposed":78,"self":78},"LiTAMIN2 (ICP+Cov), without loop closure",{"name":142,"methodId":5,"linkable":78,"proposed":78,"self":78},"LiTAMIN2 (ICP), without loop closure",{"name":144,"methodId":45,"linkable":74,"proposed":74,"self":74},"LiTAMIN [2], without loop closure",{"name":146,"methodId":147,"linkable":78,"proposed":74,"self":74},"SuMa (Frame-to-Frame)","suma2018",{"name":149,"methodId":147,"linkable":78,"proposed":74,"self":74},"SuMa (Frame-to-Model), without loop closure",{"name":151,"methodId":152,"linkable":78,"proposed":74,"self":74},"LeGO-LOAM","legoloam2018",{"name":154,"methodId":155,"linkable":74,"proposed":74,"self":74},"hdl graph slam","koide2019_hdlgraphslam",{"name":157,"methodId":158,"linkable":78,"proposed":74,"self":74},"LOAM (open source, run by authors)","loam2014",{"name":160,"methodId":161,"linkable":78,"proposed":74,"self":74},"LOAM (from [10])","loam2017_auro",{"name":163,"methodId":164,"linkable":78,"proposed":74,"self":74},"LO-Net (Frame-to-Frame)","lonet2019",{"name":166,"methodId":164,"linkable":78,"proposed":74,"self":74},"LO-Net 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,[195,169,192,370,171,169,171,171,169],2.12,[195,169,195,372,171,169,171,171,169],1.37,[195,169,198,374,171,169,171,171,169],1.8,[198,169,169,170,171,169,171,171,169],[198,169,173,377,171,169,171,171,169],1.42,[198,169,176,193,171,169,171,171,169],[198,169,179,380,171,169,171,171,169],0.73,[198,169,101,382,171,169,171,171,169],0.56,[198,169,184,384,171,169,171,171,169],0.62,[198,169,187,185,171,169,171,171,169],[198,169,189,382,171,169,171,171,169],[198,169,192,388,171,169,171,171,169],1.08,[198,169,195,353,171,169,171,171,169],[198,169,198,341,171,169,171,171,169],[],[106],[],[],[396],"KITTI odometry sequences 00-10; KITTI stats translation error (%) averaged over 100-800 m segments with the benchmark code; rows without loop closure; LiTAMIN2 with 3 m voxels; LOAM (from [10]), LO-Net and DeepLO values copied from their papers",{"slug":398,"group":399,"sourceId":5,"sourceLabel":6,"table":400,"selfRows":101,"metrics":401,"seqs":404,"entrants":407,"cells":424,"outcomes":446,"locators":447,"hardware":448,"wordings":449,"notes":450},"litamin2-2021-table-iii-overall","litamin2_2021:Table III (overall)","Table III (overall)",[402],{"label":403,"unit":111,"statistic":112,"alignment":72},"KITTI stats [deg\u002F100m] \u002F [%], translation part",[405],{"dataset":115,"sequence":406,"environment":117},"00-10 overall",[408,409,411,412,414,415,417,418,419,421,422,423],{"name":140,"methodId":5,"linkable":78,"proposed":78,"self":78},{"name":410,"methodId":5,"linkable":78,"proposed":78,"self":78},"LiTAMIN2 (ICP+Cov), with loop 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voxels",{"slug":453,"group":454,"sourceId":5,"sourceLabel":6,"table":455,"selfRows":101,"metrics":456,"seqs":461,"entrants":464,"cells":477,"outcomes":500,"locators":501,"hardware":502,"wordings":503,"notes":504},"litamin2-2021-table-iv","litamin2_2021:Table IV","Table IV",[457],{"label":458,"unit":459,"statistic":112,"alignment":460},"Absolute trajectory error, Avg. of all frames [m]","m","not_reported",[462],{"dataset":115,"sequence":463,"environment":117},"00-10, average of all 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