[{"data":1,"prerenderedAt":704},["ShallowReactive",2],{"method-ltaom2024":3},{"method":4,"reference":64,"equipment":90,"figures":120,"results":121},{"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":38,"platform":41,"estimator":44,"association":45,"timeModel":46,"deskew":47,"loopClosure":48,"globalOptimization":49,"mapRepresentation":50,"prior":51,"outputGeometry":52,"compute":53,"codeUrl":54,"codeLicense":55,"relatedVersions":56},"ltaom2024","Zou et al., 2024","LTA-OM","LTA‐OM: Long‐term association LiDAR-IMU odometry and mapping",2024,"recent","C06","full_slam_with_global_correction","LTA-OM 以 FAST-LIO2 作為光達慣性里程計、以 STD 作為迴圈偵測，整合迴圈校正、誤判迴圈剔除、長期關聯（long-term association, LTA）建圖與多時段定位建圖。其 LTA 建圖把校正後的歷史地圖直接作為 LIO 掃描對地圖配準的全域約束，使回到舊地點時里程計不再漂移；多時段模式可儲存校正後的地圖點、最佳化軌跡與描述子資料庫，供後續時段接續使用。","LTA-OM combines FAST-LIO2 and STD loop detection with loop correction, false-loop rejection, and long-term association that feeds the corrected history map back into LIO registration, plus a multi-session mode storing maps and descriptors.","full_text_reviewed","peer_reviewed_published","main_body","無工地測試。公開資料為 MulRan（DCC、KAIST、Riverside）與 NCLT 校園；另以 Livox Avia 與其內建 IMU 在多層建築中蒐集資料，該建築三個樓層有尺寸相同的相似矩形走廊，偵測到的迴圈約半數為誤判，FPR 仍建出一致地圖，而以 Cauchy 穩健估計取代時迴圈閉合失敗（Sec. 5.4、Fig. 11）；此情境與施工中各樓層結構重複相近（推論）。多時段模式可載入前次地圖接續建圖，並在 4.9 至 7.5 s 內於前次地圖上重新定位（Table 9），與工地重複掃描相關（推論）；但地圖一致性只以體素數與衛星影像比對評估，未以參考點雲量化。",[20,21],"public_benchmark","completed_building",[23,24,25,26,27,28,29],"Lowest ATE RMSE on 8 of 9 MulRan sequences (average 4.83 m vs 5.67 m FAST-LIO-SC and 5.82 m LIO-SAM-SC) and on all 9 NCLT sequences (average 1.56 m vs 2.83 m FAST-LIO-SC) (Tables 2-3)","Fewer 5 cm map voxels than FAST-LIO-SC and LIO-SAM-SC on all nine MulRan sequences, and fewer than LTA-OM without LTA on eight of nine (not riverside03) (Table 4)","Average all-thread time 32.22 ms per scan on MulRan and 20.62 ms on NCLT, about 2 and 1.5 times faster than FAST-LIO-SC (Tables 5-6)","Loop-closing convergence 2.9 s versus 39.5 s for LIO-SAM-SC with a robust estimator on riverside02 (Sec. 4.3.2, Fig. 4)","No ICP verification of loop candidates; loop correction averages 83.87 ms per run (Sec. 5.2, Table 7)","Multisession mode lowered ATE in 5 of 6 MulRan tests and relocalized on the prior map within 4.9 s to 7.5 s (Tables 8-9)","Consistent map in a multilevel building where half of the detected loops were false positives, whereas a Cauchy robust estimator failed (Sec. 5.4, Fig. 11)",[31,32,33,34,35,36,37],"Loop detection accumulates about 20 scans per submap, adding delay before loop optimization and correction (Remark 1)","Only loops with overlap ratio above 0.5 are trusted, discarding STD's small-overlap detections (Sec. 4.3.2)","Loop detection time (71.11 ms MulRan, 40.18 ms NCLT) is higher than the Scan Context variants of the baselines (Sec. 5.2)","Multisession mode requires a prior map produced by LTA-OM; maps from other systems are incompatible (Remark 4)","LTA did not reduce the voxel count on riverside03 because of a large non-revisited area, and ATE on DCC01 was slightly worse than FAST-LIO-SC (Sec. 5.1, Tables 2 and 4)","Map consistency evaluated only by voxel counts and comparison with a satellite image; no reference point cloud (Sec. 5.1)","Future work: GNSS fusion and edge or plane factors for global map optimization (Sec. 6)",[39,40],"3D LiDAR","IMU",[42,43],"public datasets MulRan and NCLT (carrier platforms not described in the paper)","multilevel building dataset collected with a Livox Avia LiDAR and its embedded IMU (carrier not described)","FAST-LIO2 tightly coupled iterated Kalman filter LIO with long-term association (history map points reloaded into the ikd-Tree); separate loop optimization on a pose graph of submap poses with odometry factors plus neighbouring and loop-closure key-point factors (STD key points), solved with GTSAM and iSAM2; false-positive rejection by an optimize-and-recover graph consistency check (Sec. 4.1, 4.3)","Direct point-to-plane scan-to-map registration on the ikd-Tree (plane fitted to five neighbours); STD-LCD on submaps of 20 accumulated scans: triangle descriptors of key points, hash-table rough detection, transform clustering with more than 4 supporters, and plane-to-plane overlap verification; neighbouring key-point pairs associated by kd-tree radius search (Sec. 4.1, 4.2, 4.3.1)","IMU forward propagation and backward propagation within each scan (FAST-LIO2); discrete submap poses in the pose graph (Sec. 4.1, 4.3.1)","FAST-LIO2 backward propagation with the IMU kinematic model compensates motion distortion of each scan (Sec. 4.1)","STD-LCD loops on 20-scan submaps; only loops with overlap ratio above 0.5 are trusted; each loop is optimized after backing up the graph and rejected (graph restored) if key-point factor residuals exceed a threshold (2 in the benchmarks); the first loop or loops far from the current pose require two consecutive mutually consistent loops (Sec. 4.2, 4.3.2, 5.1)","iSAM2 pose graph with key-point factors, replaced inside each closed loop cycle by recalculated odometry factors to bound factor count; loop correction every 200 m: history submap points corrected with optimized poses, ikd-Tree rebuilt in a separate thread, scan re-registered by ICP (about 8 ms) and the LIO pose corrected (Sec. 4.3.3, 4.4)","ikd-Tree live map holding recent scan points and corrected, on-tree downsampled history submap points loaded around the current position; stored multisession prior map with submap poses and STD descriptor database (Sec. 4.1, 4.4.1, 4.5)","optional prior session produced by LTA-OM itself (prestored map, submap poses and descriptor database); maps from other systems are not compatible (Sec. 4.5, Remark 4)","corrected point-cloud map and optimized trajectory; multi-session stitched map (abstract)","Real time on an Intel i7-10700 CPU (2.90 GHz, 16 cores, 15.5 GB RAM): average total time of all threads 32.22 ms per scan on MulRan and 20.62 ms on NCLT (10 Hz LiDAR); loop correction 83.87 ms per run on average; loop detection 71.11 ms (MulRan) and 40.18 ms (NCLT) per run (Sec. 5, Tables 5-7)","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FLTAOM","conflicting: LICENSE file shows Apache-2.0 while README states GPLv2 (unresolved)",[57,61],{"relation":58,"title":59,"doi_or_url":60},"preprint","LTA-OM: Long-Term Association LiDAR-IMU Odometry and Mapping (Authorea preprint, 2023-03-06; author list differs from journal version)","10.22541\u002Fau.167810087.79577857\u002Fv1",{"relation":62,"title":63,"doi_or_url":54},"code_release","hku-mars\u002FLTAOM",{"id":5,"kind":65,"shortName":7,"title":66,"authors":67,"year":9,"venue":75,"venueType":76,"publisher":77,"volumeIssuePages":78,"doi":79,"arxivId":80,"url":81,"firstPublicDate":82,"publicationStatus":16,"metadataStatus":83,"fulltextStatus":15,"era":10,"classicReason":84,"codeUrl":54,"cluster":11,"topics":85,"mdpi":86,"verification":87,"label":6,"fulltextRoute":88,"versionRead":89,"addedByCensus":86},"method","LTA‐OM: Long‐term association LiDAR–IMU odometry and mapping",[68,69,70,71,72,73,74],"Zuhao Zou","Chongjian Yuan","Wei Xu","Haotian Li","Shunbo Zhou","Kaiwen Xue","Fu Zhang","Journal of Field Robotics","journal","Wiley","41(7):2455-2474","10.1002\u002Frob.22337",null,"https:\u002F\u002Fdoi.org\u002F10.1002\u002Frob.22337","2023-03-06","metadata_verified","not_applicable",[11],false,"corrected","NTU institutional (Chrome)","Version of record, Journal of Field Robotics 41(7):2455-2474, first published 15 April 2024 (Wiley Online Library HTML full text, open access; includes the 9 May 2024 correction notice on the corresponding author)",[91,99,102,108,111,115],{"category":92,"model":93,"canonical":94,"role":95,"dataset":96,"specs":97,"locator":98},"lidar","Livox Avia LiDAR","Livox Avia","method input","multilevel building (authors' own data)","not_reported","Sec. 5.4",{"category":100,"model":101,"canonical":101,"role":95,"dataset":96,"specs":97,"locator":98},"imu","embedded IMU of the Livox Avia",{"category":92,"model":103,"canonical":103,"role":104,"dataset":105,"specs":106,"locator":107},"Ouster LiDAR (model not named)","dataset sensor","MulRan","10 Hz","Sec. 5",{"category":92,"model":109,"canonical":109,"role":104,"dataset":110,"specs":106,"locator":107},"Velodyne LiDAR (model not named)","NCLT",{"category":100,"model":112,"canonical":112,"role":104,"dataset":113,"specs":114,"locator":107},"IMU (model not named)","MulRan; NCLT","more than 100 Hz",{"category":116,"model":117,"canonical":117,"role":118,"dataset":80,"specs":119,"locator":107},"compute","Intel i7-10700 CPU","compute for runtime","2.90 GHz, 16 cores, 15.5 GB RAM",[],{"totalRows":122,"groupCount":123,"groups":124,"others":678},67,9,[125,356,413,548],{"slug":126,"group":127,"sourceId":128,"sourceLabel":129,"table":130,"selfRows":131,"metrics":132,"seqs":137,"entrants":184,"cells":200,"outcomes":348,"locators":350,"hardware":352,"wordings":353,"notes":354},"voxelslam2026-table-2-full-slam-with-lc","voxelslam2026:Table 2 (full SLAM with LC)","voxelslam2026","Liu et al., 2026","Table 2 (full SLAM with LC)",13,[133],{"label":134,"unit":135,"statistic":136,"alignment":97},"absolute trajectory error (RMSE, centimeters)","cm","RMSE",[138,142,145,148,152,156,160,164,168,172,175,178,181],{"dataset":139,"sequence":140,"environment":141},"Hilti handheld sequence exp01-construction (name per Table C1)","hilti01","construction environment (sequence named construction)",{"dataset":143,"sequence":144,"environment":141},"Hilti handheld sequence exp02-construction (name per Table C1)","hilti02",{"dataset":146,"sequence":147,"environment":141},"Hilti handheld sequence exp03-construction (name per Table C1)","hilti03",{"dataset":149,"sequence":150,"environment":151},"Hilti handheld sequence exp07-long-corridor (name per Table C1)","hilti04","long corridor",{"dataset":153,"sequence":154,"environment":155},"Hilti handheld sequence exp09-cupola (name per Table C1)","hilti05","cupola",{"dataset":157,"sequence":158,"environment":159},"Hilti handheld sequence exp11-lower-gallery (name per Table C1)","hilti06","lower gallery",{"dataset":161,"sequence":162,"environment":163},"Hilti handheld sequence exp15-upper-gallery (name per Table C1)","hilti07","upper gallery",{"dataset":165,"sequence":166,"environment":167},"Hilti handheld sequence exp21-outside (name per Table C1)","hilti08","outside",{"dataset":169,"sequence":170,"environment":171},"Hilti handheld sequence site1-handheld-1 (name per Table C1)","hilti09","construction site (same site for hilti09 to hilti13, Sec. 10.3.1)",{"dataset":173,"sequence":174,"environment":171},"Hilti handheld sequence site1-handheld-2 (name per Table C1)","hilti10",{"dataset":176,"sequence":177,"environment":171},"Hilti handheld sequence site1-handheld-3 (name per Table C1)","hilti11",{"dataset":179,"sequence":180,"environment":171},"Hilti handheld sequence site1-handheld-4 (name per Table C1)","hilti12",{"dataset":182,"sequence":183,"environment":171},"Hilti handheld sequence site1-handheld-5 (name per Table C1)","hilti13",[185,189,192,195,196,198],{"name":186,"methodId":187,"linkable":188,"proposed":86,"self":86},"LeGO-LOAM","legoloam2018",true,{"name":190,"methodId":191,"linkable":188,"proposed":86,"self":86},"LiLi-OM","liliom2021",{"name":193,"methodId":194,"linkable":188,"proposed":86,"self":86},"LIO-SAM","liosam2020",{"name":7,"methodId":5,"linkable":188,"proposed":86,"self":188},{"name":197,"methodId":128,"linkable":188,"proposed":188,"self":86},"Our (Odom+LM+LC)",{"name":199,"methodId":128,"linkable":188,"proposed":188,"self":86},"Our (Full)",[201,205,208,210,213,215,217,219,222,225,227,230,233,235,237,239,240,242,243,244,245,247,249,250,252,254,255,257,259,260,262,263,265,266,268,270,272,274,276,278,280,282,284,286,288,289,291,293,295,297,299,301,303,305,307,309,311,312,314,316,317,319,321,322,323,325,327,328,329,331,333,335,337,339,340,342,344,346],[202,202,202,203,204,202,204,204,202],0,8.8,-1,[202,202,206,207,204,202,204,204,202],1,39,[202,202,209,80,202,202,204,204,202],2,[202,202,211,212,204,202,204,204,202],3,25.3,[202,202,214,80,202,202,204,204,202],4,[202,202,216,122,204,202,204,204,202],5,[202,202,218,80,202,202,204,204,202],6,[202,202,220,221,204,202,204,204,202],7,22.7,[202,202,223,224,204,202,204,204,202],8,12.6,[202,202,123,226,204,202,204,204,202],12.9,[202,202,228,229,204,202,204,204,202],10,27.1,[202,202,231,232,204,202,204,204,202],11,16.2,[202,202,234,80,202,202,204,204,202],12,[206,202,202,236,204,202,204,204,202],6.2,[206,202,206,238,204,202,204,204,202],14.2,[206,202,209,80,202,202,204,204,202],[206,202,211,241,204,202,204,204,202],31,[206,202,214,80,202,202,204,204,202],[206,202,216,229,204,202,204,204,202],[206,202,218,80,202,202,204,204,202],[206,202,220,246,204,202,204,204,202],18.6,[206,202,223,248,204,202,204,204,202],6.9,[206,202,123,223,204,202,204,204,202],[206,202,228,251,204,202,204,204,202],19.9,[206,202,231,253,204,202,204,204,202],22.8,[206,202,234,80,202,202,204,204,202],[209,202,202,256,204,202,204,204,202],6.1,[209,202,206,258,204,202,204,204,202],10.1,[209,202,209,80,202,202,204,204,202],[209,202,211,261,204,202,204,204,202],23.4,[209,202,214,80,202,202,204,204,202],[209,202,216,264,204,202,204,204,202],13.4,[209,202,218,80,202,202,204,204,202],[209,202,220,267,204,202,204,204,202],17.2,[209,202,223,269,204,202,204,204,202],6.6,[209,202,123,271,204,202,204,204,202],5.5,[209,202,228,273,204,202,204,204,202],17.6,[209,202,231,275,204,202,204,204,202],12.5,[209,202,234,277,204,202,204,204,202],74,[211,202,202,279,204,202,204,204,202],1.27,[211,202,206,281,204,202,204,204,202],2.5,[211,202,209,283,204,202,204,204,202],33,[211,202,211,285,204,202,204,204,202],6.7,[211,202,214,287,204,202,204,204,202],40,[211,202,216,209,204,202,204,204,202],[211,202,218,290,204,202,204,204,202],65,[211,202,220,292,204,202,204,204,202],1.2,[211,202,223,294,204,202,204,204,202],2.4,[211,202,123,296,204,202,204,204,202],1.78,[211,202,228,298,204,202,204,204,202],4.1,[211,202,231,300,204,202,204,204,202],2.9,[211,202,234,302,204,202,204,204,202],16,[214,202,202,304,204,202,204,204,202],0.78,[214,202,206,306,204,202,204,204,202],1.8,[214,202,209,308,204,202,204,204,202],2.8,[214,202,211,310,204,202,204,204,202],3.4,[214,202,214,238,204,202,204,204,202],[214,202,216,313,204,202,204,204,202],0.9,[214,202,218,315,204,202,204,204,202],9.2,[214,202,220,313,204,202,204,204,202],[214,202,223,318,204,202,204,204,202],1.25,[214,202,123,320,204,202,204,204,202],1.4,[214,202,228,294,204,202,204,204,202],[214,202,231,320,204,202,204,204,202],[214,202,234,324,204,202,204,204,202],1.26,[216,202,202,326,204,202,204,204,202],0.62,[216,202,206,320,204,202,204,204,202],[216,202,209,300,204,202,204,204,202],[216,202,211,330,204,202,204,204,202],3.3,[216,202,214,332,204,202,204,204,202],13.8,[216,202,216,334,204,202,204,204,202],0.7,[216,202,218,336,204,202,204,204,202],7.8,[216,202,220,338,204,202,204,204,202],0.82,[216,202,223,206,204,202,204,204,202],[216,202,123,341,204,202,204,204,202],1.14,[216,202,228,343,204,202,204,204,202],1.3,[216,202,231,345,204,202,204,204,202],1.22,[216,202,234,347,204,202,204,204,202],0.8,[349],"failed (dash; text states LeGO-LOAM, LiLi-OM, LINS and LIO-SAM failed in these sequences)",[351],"Table 2",[],[],[355],"Hilti handheld sequences; ATE exported from the Hilti evaluation website; full SLAM with loop closure (Our (Full) adds global mapping)",{"slug":357,"group":358,"sourceId":5,"sourceLabel":6,"table":359,"selfRows":234,"metrics":360,"seqs":364,"entrants":378,"cells":383,"outcomes":407,"locators":408,"hardware":409,"wordings":410,"notes":411},"ltaom2024-table-8","ltaom2024:Table 8","Table 8",[361],{"label":362,"unit":363,"statistic":136,"alignment":97},"absolute trajectory error (root-mean-square error, meters)","m",[365,368,370,372,374,376],{"dataset":105,"sequence":366,"environment":367},"R2-1","urban and campus driving",{"dataset":105,"sequence":369,"environment":367},"R2-3",{"dataset":105,"sequence":371,"environment":367},"K3-1",{"dataset":105,"sequence":373,"environment":367},"K3-2",{"dataset":105,"sequence":375,"environment":367},"D3-1",{"dataset":105,"sequence":377,"environment":367},"D3-2",[379,381],{"name":380,"methodId":5,"linkable":188,"proposed":86,"self":188},"LTA-OM single-session",{"name":382,"methodId":5,"linkable":188,"proposed":188,"self":188},"LTA-OM multisession",[384,386,388,390,392,394,396,397,399,401,403,405],[202,202,202,385,204,202,204,204,202],6.98,[206,202,202,387,204,202,204,204,202],6.16,[202,202,206,389,204,202,204,204,202],10.38,[206,202,206,391,204,202,204,204,202],8.36,[202,202,209,393,204,202,204,204,202],3.25,[206,202,209,395,204,202,204,204,202],3.1,[202,202,211,395,204,202,204,204,202],[206,202,211,398,204,202,204,204,202],2.99,[202,202,214,400,204,202,204,204,202],5.29,[206,202,214,402,204,202,204,204,202],5.31,[202,202,216,404,204,202,204,204,202],2.94,[206,202,216,406,204,202,204,204,202],2.85,[],[359],[],[],[412],"Multisession mode on MulRan: the sequence with the smallest RMSE of each scene provides the prior map; 'R2-1' = testing on R1 with prior from R2; single-session values copied from Table 2",{"slug":414,"group":415,"sourceId":5,"sourceLabel":6,"table":351,"selfRows":228,"metrics":416,"seqs":419,"entrants":441,"cells":452,"outcomes":540,"locators":543,"hardware":544,"wordings":545,"notes":546},"ltaom2024-table-2","ltaom2024:Table 2",[417],{"label":418,"unit":363,"statistic":136,"alignment":97},"absolute trajectory error (RMSE, meters)",[420,423,425,427,429,431,433,435,437,439],{"dataset":105,"sequence":421,"environment":422},"R1 riverside01","urban and campus driving with buildings, mountain, river, bridges",{"dataset":105,"sequence":424,"environment":422},"R2 riverside02",{"dataset":105,"sequence":426,"environment":422},"R3 riverside03",{"dataset":105,"sequence":428,"environment":422},"K1 KAIST01",{"dataset":105,"sequence":430,"environment":422},"K2 KAIST02",{"dataset":105,"sequence":432,"environment":422},"K3 KAIST03",{"dataset":105,"sequence":434,"environment":422},"D1 DCC01",{"dataset":105,"sequence":436,"environment":422},"D2 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