[{"data":1,"prerenderedAt":634},["ShallowReactive",2],{"method-loglio2024":3},{"method":4,"reference":62,"equipment":86,"figures":128,"results":129},{"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":27,"sensors":33,"platform":36,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":44,"mapRepresentation":45,"prior":46,"outputGeometry":47,"compute":48,"codeUrl":49,"codeLicense":50,"relatedVersions":51},"loglio2024","Huang et al., 2024b","LOG-LIO","LOG-LIO: A LiDAR-Inertial Odometry With Efficient Local Geometric Information Estimation",2024,"recent","C05","odometry_with_local_mapping","LOG-LIO 在 FAST-LIO2 的迭代誤差狀態卡爾曼濾波架構上，加入即時的局部幾何資訊估計。作者提出 Ring FALS：預先依 LiDAR 的環編號與方位角建立方位向量查找表，新掃描到達時只需距離值即可以近似最小平方求得每點法向量，避免鄰域搜尋。地圖以擴充的 ikd-Tree 管理，每個體素節點遞增維護點分布的平均與共變異數，並在收斂後固定。資料關聯先做可見性與法向量一致性檢查，再依序嘗試大尺度面元、小尺度面元，最後才退回點到平面。","FAST-LIO2-style iterated EKF LIO that estimates per-point normals from range only with a ring-indexed lookup table (Ring FALS), maintains incremental per-voxel point distributions in an extended ikd-Tree, and uses hierarchical association that prefers large then small point-to-surfel constraints over point-to-plane after visibility and normal-consistency checks.","full_text_reviewed","peer_reviewed_published","supplementary","原論文僅在 M2DGR 校園地面機器人與 NTU VIRAL 無人機資料上評估，未涉及營建場域。Feng 等人 [feng2025_construction_lidar_eval] 以預設參數在施工中醫院大樓的實際工地（1,004 m）與依 BIM 建立的 Gazebo 模擬工地測試十種方法，LOG-LIO 的 APE RMSE 在實際工地為 5.33 m、模擬工地為 20.81 m，均高於 LIO-SAM（2.26 m、3.19 m）與 FAST-LIO2（3.52 m、10.1 m）。該研究未說明實際工地參考軌跡來源，數值只能視為作者報告值；但它顯示在公開資料集上的小幅改進，未必能移轉到大型、結構重複的施工樓層（推論）。",[20,21],"public_benchmark","independent_reference",[23,24,25,26],"Lowest mean translation RMSE on M2DGR (0.684 m versus 0.799 m FAST-LIO2 and 1.095 m LIO-SAM) and best on 10 of 21 sequences (Table II)","Lowest mean RMSE on NTU VIRAL (0.330 m versus 0.423 m FAST-LIO2), with clearer gains on the high-altitude spms sequences where map overlap is limited (Table III, Sec. VI-D-2)","Ring FALS normal estimation is about one tenth of single-thread PCL time on Velodyne-32 scans and four times faster than PCL with 10 OpenMP threads (Table I)","Visibility check removes double-sided wall associations common indoors (Sec. V-B-2)",[28,29,30,31,32],"About 8 ms more per scan than FAST-LIO2 on average (Table IV, Sec. VI-D-3)","Ring FALS assumes similar range within a small neighbourhood, which fails at wall edges, occlusions and missing returns; such normals rely on smoothing and outlier checks (Sec. IV, VI-C)","Requires ring index and a LiDAR-specific lookup table, so it targets spinning LiDARs (Sec. III-B) (inference for non-repetitive solid-state scanners)","No loop closure or dynamic-object handling (Sec. VII)","LIO-SAM had lower RMSE on several NTU VIRAL sequences (eee_01-03, nya_02, sbs_02, rtp_02-03) and on M2DGR street06, street07, street09 and hall05 (Tables II-III)",[34,35],"3D spinning LiDAR with ring index (Velodyne 32-beam in M2DGR; Ouster OS1 16-channel in NTU VIRAL)","9-axis IMU (VectorNav VN100 in NTU VIRAL)",[37,38],"ground robot (M2DGR ground platform; locomotion type not stated)","UAV (NTU VIRAL)","error-state iterated EKF adopted from FAST-LIO2, with the MAP update augmented by point-to-surfel residuals besides point-to-plane residuals (Eq. 13, Sec. V-C)","hierarchical: k nearest map points and voxels; visibility check (map normal versus ray) and normal consistency check (mean angle below 60 deg); then large merged surfel, else small fixed surfel of the voxel, else LOAM-style point-to-plane; surfel if planarity above 1.0 and lambda2\u002Flambda1 above 100 (Sec. III-E, V-B)","discrete scan poses with IMU backward propagation for undistortion (Sec. V-A)","IMU backward propagation (FAST-LIO2 style) after normal estimation and voxel downsampling (Sec. V-A)","none (listed as future work, Sec. VII)","none","ikd-Tree extended so that each node also stores an incrementally updated point distribution (mean and covariance) of its voxel; distributions are fixed once the Ring FALS normal and the distribution eigenvector agree within 20 deg or after 2 eta = 50 points (Sec. V-D, VI-A)","LiDAR-specific lookup table of bearing vectors per ring and azimuth, precomputed for Ring FALS (Sec. III-B, IV)","odometry and a voxelized point map with per-voxel normals and surfels","CPU real time: mean 28.1 ms per scan over all sequences versus 20.5 ms for FAST-LIO2 on an Intel Xeon Gold 6248R 3.00 GHz with 32 GB RAM; Ring FALS normals take 7.8 ms per 57,600-point Velodyne-32 scan versus 79.8 ms for single-thread PCL (Tables I and IV, Sec. VI-B)","https:\u002F\u002Fgithub.com\u002Ftiev-tongji\u002FLOG-LIO","GPL-2.0 (LICENSE file checked)",[52,56,59],{"relation":53,"title":54,"doi_or_url":55},"preprint","arXiv 2307.09531 (v1 2023-07-18, v2 2023-08-14, v3 2023-10-11)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.09531",{"relation":57,"title":58,"doi_or_url":49},"code_release","tiev-tongji\u002FLOG-LIO",{"relation":57,"title":60,"doi_or_url":61},"tiev-tongji\u002FRingFalsNormal (stand-alone Ring FALS normal estimator, not opened)","https:\u002F\u002Fgithub.com\u002Ftiev-tongji\u002FRingFalsNormal",{"id":5,"kind":63,"shortName":7,"title":8,"authors":64,"year":9,"venue":70,"venueType":71,"publisher":72,"volumeIssuePages":73,"doi":74,"arxivId":75,"url":76,"firstPublicDate":77,"publicationStatus":16,"metadataStatus":78,"fulltextStatus":15,"era":10,"classicReason":79,"codeUrl":49,"cluster":11,"topics":80,"mdpi":81,"verification":82,"label":6,"fulltextRoute":83,"versionRead":84,"addedByCensus":85},"method",[65,66,67,68,69],"Kai Huang","Junqiao Zhao","Zhongyang Zhu","Chen Ye","Tiantian Feng","IEEE Robotics and Automation Letters","journal","IEEE","9(1):459-466","10.1109\u002Flra.2023.3332020","2307.09531","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2023.3332020","2023-07-18","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v3 (2023-10-11); RA-L version of record not read",true,[87,95,100,105,110,114,118,121],{"category":88,"model":89,"canonical":90,"role":91,"dataset":92,"specs":93,"locator":94},"lidar","Velodyne-32 (as written)","Velodyne-32","dataset sensor","M2DGR","about 57,600 points per scan","Sec. VI-B; Table I",{"category":96,"model":97,"canonical":97,"role":91,"dataset":92,"specs":98,"locator":99},"platform","ground robot platform (not further specified)","indoor and outdoor campus scenes, night street sequences","Sec. VI-B",{"category":101,"model":102,"canonical":102,"role":103,"dataset":92,"specs":104,"locator":99},"other","laser 3D tracking, motion capture and RTK receivers (models not stated)","reference or ground truth","ground-truth trajectories",{"category":88,"model":106,"canonical":107,"role":91,"dataset":108,"specs":109,"locator":94},"Ouster OS1 16-channel (horizontal)","Ouster OS1-16","NTU VIRAL","16,384 points per scan",{"category":111,"model":112,"canonical":112,"role":91,"dataset":108,"specs":113,"locator":99},"imu","VectorNav VN100","9-axis IMU",{"category":115,"model":116,"canonical":116,"role":103,"dataset":108,"specs":117,"locator":99},"total_station","laser-tracker total station (model not stated)","centimetre-level ground truth",{"category":96,"model":119,"canonical":119,"role":91,"dataset":108,"specs":120,"locator":99},"UAV (not further specified)","not_reported",{"category":122,"model":123,"canonical":124,"role":125,"dataset":126,"specs":127,"locator":99},"compute","Intel Core Xeon(R) Gold 6248R (as written)","Intel Core Xeon(R) Gold 6248R","compute for runtime",null,"3.00 GHz, 32 GB RAM, Ubuntu 18.04",[],{"totalRows":130,"groupCount":131,"groups":132,"others":633},55,4,[133,356,501,594],{"slug":134,"group":135,"sourceId":5,"sourceLabel":6,"table":136,"selfRows":137,"metrics":138,"seqs":143,"entrants":189,"cells":197,"outcomes":350,"locators":351,"hardware":352,"wordings":353,"notes":354},"loglio2024-table-ii","loglio2024:Table II","Table II",22,[139],{"label":140,"unit":141,"statistic":142,"alignment":120},"translation RMSE","m","RMSE",[144,147,149,151,153,155,157,159,161,163,165,167,169,171,173,175,177,179,181,183,185,187],{"dataset":92,"sequence":145,"environment":146},"gate01 (172 s)","ground robot; indoor door and hall sequences, outdoor gate and street sequences (street on wide campus roads at night); walk not classified in the text (Sec. VI-D-1)",{"dataset":92,"sequence":148,"environment":146},"gate02 (327 s)",{"dataset":92,"sequence":150,"environment":146},"gate03 (283 s)",{"dataset":92,"sequence":152,"environment":146},"walk01 (291 s)",{"dataset":92,"sequence":154,"environment":146},"door01 (461 s)",{"dataset":92,"sequence":156,"environment":146},"door02 (127 s)",{"dataset":92,"sequence":158,"environment":146},"street01 (1028 s)",{"dataset":92,"sequence":160,"environment":146},"street02 (1227 s)",{"dataset":92,"sequence":162,"environment":146},"street03 (354 s)",{"dataset":92,"sequence":164,"environment":146},"street04 (858 s)",{"dataset":92,"sequence":166,"environment":146},"street05 (469 s)",{"dataset":92,"sequence":168,"environment":146},"street06 (494 s)",{"dataset":92,"sequence":170,"environment":146},"street07 (829 s)",{"dataset":92,"sequence":172,"environment":146},"street08 (491 s)",{"dataset":92,"sequence":174,"environment":146},"street09 (907 s)",{"dataset":92,"sequence":176,"environment":146},"street10 (810 s)",{"dataset":92,"sequence":178,"environment":146},"hall01 (351 s)",{"dataset":92,"sequence":180,"environment":146},"hall02 (128 s)",{"dataset":92,"sequence":182,"environment":146},"hall03 (164 s)",{"dataset":92,"sequence":184,"environment":146},"hall04 (181 s)",{"dataset":92,"sequence":186,"environment":146},"hall05 (402 s)",{"dataset":92,"sequence":188,"environment":146},"mean",[190,191,194],{"name":7,"methodId":5,"linkable":85,"proposed":85,"self":85},{"name":192,"methodId":193,"linkable":85,"proposed":81,"self":81},"FAST-LIO2","fastlio2_2022",{"name":195,"methodId":196,"linkable":85,"proposed":81,"self":81},"LIO-SAM","liosam2020",[198,202,205,208,211,213,216,219,222,224,227,230,233,236,239,242,245,248,251,254,257,260,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,298,300,302,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348],[199,199,199,200,201,199,201,201,199],0,0.097,-1,[199,199,203,204,201,199,201,201,199],1,0.27,[199,199,206,207,201,199,201,201,199],2,0.085,[199,199,209,210,201,199,201,201,199],3,0.078,[199,199,131,212,201,199,201,201,199],0.251,[199,199,214,215,201,199,201,201,199],5,0.172,[199,199,217,218,201,199,201,201,199],6,0.246,[199,199,220,221,201,199,201,201,199],7,2.448,[199,199,223,200,201,199,201,201,199],8,[199,199,225,226,201,199,201,201,199],9,0.485,[199,199,228,229,201,199,201,201,199],10,0.331,[199,199,231,232,201,199,201,201,199],11,0.342,[199,199,234,235,201,199,201,201,199],12,2.916,[199,199,237,238,201,199,201,201,199],13,0.13,[199,199,240,241,201,199,201,201,199],14,3.164,[199,199,243,244,201,199,201,201,199],15,0.388,[199,199,246,247,201,199,201,201,199],16,0.256,[199,199,249,250,201,199,201,201,199],17,0.274,[199,199,252,253,201,199,201,201,199],18,0.345,[199,199,255,256,201,199,201,201,199],19,0.944,[199,199,258,259,201,199,201,201,199],20,1.045,[199,199,261,262,201,199,201,201,199],21,0.684,[203,199,199,264,201,199,201,201,199],0.091,[203,199,203,266,201,199,201,201,199],0.279,[203,199,206,268,201,199,201,201,199],0.109,[203,199,209,270,201,199,201,201,199],0.112,[203,199,131,272,201,199,201,201,199],0.271,[203,199,214,274,201,199,201,201,199],0.2,[203,199,217,276,201,199,201,201,199],0.329,[203,199,220,278,201,199,201,201,199],2.754,[203,199,223,280,201,199,201,201,199],0.106,[203,199,225,282,201,199,201,201,199],0.552,[203,199,228,284,201,199,201,201,199],0.377,[203,199,231,286,201,199,201,201,199],0.434,[203,199,234,288,201,199,201,201,199],3.512,[203,199,237,290,201,199,201,201,199],0.17,[203,199,240,292,201,199,201,201,199],3.648,[203,199,243,294,201,199,201,201,199],0.956,[203,199,246,296,201,199,201,201,199],0.258,[203,199,249,250,201,199,201,201,199],[203,199,252,299,201,199,201,201,199],0.343,[203,199,255,301,201,199,201,201,199],0.952,[203,199,258,303,201,199,201,201,199],1.049,[203,199,261,305,201,199,201,201,199],0.799,[206,199,199,307,201,199,201,201,199],0.122,[206,199,203,309,201,199,201,201,199],0.288,[206,199,206,311,201,199,201,201,199],0.095,[206,199,209,313,201,199,201,201,199],0.08,[206,199,131,315,201,199,201,201,199],0.269,[206,199,214,317,201,199,201,201,199],0.18,[206,199,217,319,201,199,201,201,199],0.559,[206,199,220,321,201,199,201,201,199],3.32,[206,199,223,323,201,199,201,201,199],0.102,[206,199,225,325,201,199,201,201,199],1.009,[206,199,228,327,201,199,201,201,199],0.407,[206,199,231,329,201,199,201,201,199],0.332,[206,199,234,331,201,199,201,201,199],1.614,[206,199,237,333,201,199,201,201,199],0.161,[206,199,240,335,201,199,201,201,199],2.657,[206,199,243,337,201,199,201,201,199],8.56,[206,199,246,339,201,199,201,201,199],0.281,[206,199,249,341,201,199,201,201,199],0.285,[206,199,252,343,201,199,201,201,199],0.579,[206,199,255,345,201,199,201,201,199],1.076,[206,199,258,347,201,199,201,201,199],1.015,[206,199,261,349,201,199,201,201,199],1.095,[],[136],[],[],[355],"M2DGR; 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