[{"data":1,"prerenderedAt":659},["ShallowReactive",2],{"method-clins2021":3},{"method":4,"reference":63,"equipment":88,"figures":124,"results":165},{"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":23,"limitations":29,"sensors":35,"platform":38,"estimator":43,"association":44,"timeModel":45,"deskew":46,"loopClosure":47,"globalOptimization":48,"mapRepresentation":49,"prior":50,"outputGeometry":51,"compute":52,"codeUrl":53,"codeLicense":54,"relatedVersions":55},"clins2021","Lv et al., 2021","CLINS","CLINS: Continuous-Time Trajectory Estimation for LiDAR-Inertial System",2021,"recent","C05","full_slam_with_global_correction","CLINS 以兩組累積式均勻三次 B 樣條分別表示位置與旋轉，將 LiDAR 慣性系統的軌跡建模為連續時間函數。每個新掃描到達時先以 IMU 積分初始化新增控制點，再在局部視窗內把 LOAM 邊緣與平面特徵以各點自身時間戳記的位姿投影到關鍵掃描子地圖，與原始加速度計、陀螺儀殘差一起做非剛性配準，同時估計控制點與 IMU 偏差，因此去畸變與位姿估計在同一最佳化中完成。迴圈閉合時採兩階段修正：先對關鍵掃描的離散位姿做位姿圖最佳化，再以修正後的位姿為錨點、以原軌跡的局部線速度與角速度維持局部形狀，重新擬合樣條控制點。","Continuous-time LIO that models the trajectory with split cumulative cubic B-splines, estimates new control points and IMU biases by sliding-window non-rigid registration of LOAM edge and planar features tightly coupled with raw IMU residuals, and handles loop closure by discrete key-scan pose-graph optimization followed by refitting the spline to the corrected poses while preserving local velocities.","full_text_reviewed","peer_reviewed_published","supplementary","論文未在營建工地或既有建物中以獨立幾何參考驗證；定量結果只有 LIOM 提供的房間尺度序列，以及校園與城市道路的車載序列（YQ 以 RTK-GPS 為參考）。其連續時間非剛性配準可在同一最佳化中處理手持劇烈運動造成的掃描畸變，概念上適用於工地手持掃描，但作者報告每次配準約 200 ms，原型實作不適合直接即時使用（推論）。GLIM [glim2024] 於 Newer College 比較表中將 CLINS 列為基準方法。",[20,21,22],"public_benchmark","controlled_experiment","independent_reference",[24,25,26,27,28],"Lowest translation RMSE of LOAM, LIO-SAM, LIOM and CLINS on all six LIOM fast, mid and slow sequences, with rotation RMSE similar to LIOM (Table II)","With loop correction, lowest APE RMSE on Kaist-Urban-07 (0.562 m) and Kaist-Urban-08 (1.133 m) among the compared methods (Table IV)","Derivatives of the estimated spline follow the raw IMU signal on fast1, where angular rate spans about -4.9 to 6.2 rad\u002Fs and acceleration reaches 5.8 m\u002Fs2 (Sec. V-A, Fig. 4)","Poses can be queried at any time; CLINS was evaluated at 100 Hz versus about 5 Hz output for LIOM and 10 Hz for LIO-SAM (Sec. V-A)","The globally corrected continuous trajectory can georeference a 2D LiDAR to build a dense 3D reconstruction (Sec. V-C, Fig. 1)",[30,31,32,33,34],"About 200 ms per non-rigid registration with automatic derivatives; authors name analytic Jacobians and efficient spline derivatives as needed for real time (Sec. V-D, VI)","Without loop correction CLINS(odom) had higher APE RMSE than LIO-SAM(odom) on Kaist-Urban-07 (1.383 vs 1.288 m) and Kaist-Urban-08 (3.907 vs 3.524 m) (Table IV)","With loop correction, LIO-SAM was slightly better on YQ-01 (2.220 vs 2.311 m) and LIOM was better on YQ-02 (0.881 vs 1.509 m) (Table IV)","LOAM and LIOM values in Table II were copied from the LIOM paper rather than re-run (Sec. V-A)","Handheld results are qualitative maps only; extrinsics must be pre-calibrated (Sec. V-B, III)",[36,37],"3D spinning LiDAR (Velodyne VLP-16 in the YQ and KAIST Urban tests)","IMU (Xsens MTi-300)",[39,40,41,42],"wheeled UGV (self-assembled small vehicle, YQ sequences)","vehicle (KAIST Urban, LiDAR tilted about 45 deg)","handheld (LIO-SAM datasets, qualitative only)","not_reported (LIOM room-scale sequences)","sliding-window batch nonlinear least squares (MAP) over the active B-spline control points and IMU biases, tightly coupling LiDAR feature residuals with raw accelerometer and gyroscope residuals; Levenberg-Marquardt in Ceres with automatic differentiation; static control points enter the problem but stay fixed (Sec. IV-B)","LOAM-style edge and planar features selected by local curvature; nearest-neighbour correspondences in a local submap of key-scans; point-to-line and point-to-plane residuals evaluated with the pose at each point's own timestamp (Sec. IV-B)","continuous-time: split representation with cumulative uniform cubic B-splines for position in R3 and orientation in SO(3); knot spacing 0.05 s for the highly dynamic room-scale LIOM sequences and 0.1 s for the slower vehicle data (Sec. III, Fig. 2, V-A, V-B)","implicit in the non-rigid registration: every point is mapped with the spline pose at its timestamp; key-scans added to the submap are undistorted to scan start with the non-active trajectory (Sec. IV-B)","yes; loop closures between key-scans trigger a two-stage correction (loop detection method not described in the paper) (Sec. IV-C)","stage 1: discrete pose-graph optimization over key-scan poses; stage 2: refit spline control points to the corrected key-scan poses while constraining local linear and angular velocities computed from the pre-correction trajectory (Eq. 10, Sec. IV-C)","local submap of key-scans selected by spatio-temporal distance (feature points); global point map assembled from undistorted scans along the continuous trajectory","pre-calibrated LiDAR-IMU extrinsic from the authors' LI-Calib toolbox (Sec. III)","continuous-time trajectory queryable at any timestamp (evaluated at 100 Hz) and an assembled 3D point cloud map; the trajectory was also used to assemble 2D SICK LMS-511 scans into a dense 3D reconstruction (Fig. 1, Sec. V-C)","not real time as reported: non-rigid registration converges in four or five iterations and takes about 200 ms per scan with automatic differentiation; hardware not reported (Sec. V-D)","https:\u002F\u002Fgithub.com\u002FAPRIL-ZJU\u002Fclins","GPL-3.0 (LICENSE file checked)",[56,60],{"relation":57,"title":58,"doi_or_url":59},"preprint","arXiv 2109.04687 v1 (2021-09-10), only version","https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.04687",{"relation":61,"title":62,"doi_or_url":53},"code_release","APRIL-ZJU\u002Fclins",{"id":5,"kind":64,"shortName":7,"title":8,"authors":65,"year":9,"venue":72,"venueType":73,"publisher":74,"volumeIssuePages":75,"doi":76,"arxivId":77,"url":78,"firstPublicDate":79,"publicationStatus":16,"metadataStatus":80,"fulltextStatus":15,"era":10,"classicReason":81,"codeUrl":53,"cluster":11,"topics":82,"mdpi":83,"verification":84,"label":6,"fulltextRoute":85,"versionRead":86,"addedByCensus":87},"method",[66,67,68,69,70,71],"Jiajun Lv","Kewei Hu","Jinhong Xu","Yong Liu","Xiushui Ma","Xingxing Zuo","2021 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)","conference","IEEE","pp. 6657-6663","10.1109\u002Firos51168.2021.9636676","2109.04687","https:\u002F\u002Fdoi.org\u002F10.1109\u002FIROS51168.2021.9636676","2021-09-10","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v1 (2021-09-10; the only arXiv version, comment IROS-2021); IEEE version of record not read",true,[89,96,101,107,112,117,120],{"category":90,"model":91,"canonical":91,"role":92,"dataset":93,"specs":94,"locator":95},"lidar","Velodyne VLP-16","method input","YQ (authors' campus sequences)","3D LiDAR on the self-assembled vehicle","Sec. V-B; Fig. 5",{"category":97,"model":98,"canonical":98,"role":92,"dataset":93,"specs":99,"locator":100},"imu","Xsens-300 IMU (as written in Sec. V-B)","not_reported","Sec. V-B",{"category":102,"model":103,"canonical":103,"role":104,"dataset":93,"specs":105,"locator":106},"gnss","JingLing-K50 RTK-GPS","reference or ground truth","RTK-GPS; shown as the 'GPS' reference trajectory in Fig. 6","Sec. V-B; Fig. 6",{"category":108,"model":109,"canonical":109,"role":92,"dataset":93,"specs":110,"locator":111},"platform","self-assembled small unmanned ground vehicle","sensors rigidly mounted; red-boxed sensors used for YQ","Fig. 5",{"category":90,"model":91,"canonical":91,"role":113,"dataset":114,"specs":115,"locator":116},"dataset sensor","KAIST Urban (Complex Urban dataset)","3D LiDAR mounted on the vehicle at a tilt of about 45 degrees","Sec. V-B; Fig. 1 caption",{"category":97,"model":118,"canonical":118,"role":113,"dataset":114,"specs":99,"locator":119},"Xsens MTi-300","Fig. 1 caption",{"category":90,"model":121,"canonical":121,"role":113,"dataset":114,"specs":122,"locator":123},"SICK LMS-511","2D LiDAR; not used for estimation, only assembled with the CLINS trajectory for dense reconstruction","Fig. 1; Sec. V-C",[125,138,148,156],{"refId":5,"refLabel":6,"fig":126,"whatZh":127,"license":128,"licenseUrl":129,"sourceUrl":130,"src":131,"width":132,"height":133,"thumb":134,"thumbWidth":135,"thumbHeight":136,"modified":137},"Fig. 1","以 CLINS 連續時間軌跡組合 SICK LMS-511 二維 LiDAR 掃描，得到 KAIST Urban-07 的稠密三維重建","CC BY 4.0","https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2109.04687v1\u002Ffigure\u002Fpng\u002Fkaist_ubran-07.png","\u002Ffigure-files\u002Fclins2021\u002Ffig-1.webp",1215,926,"\u002Ffigure-files\u002Fclins2021\u002Ffig-1.thumb.webp",480,366,"converted to WebP",{"refId":5,"refLabel":6,"fig":139,"whatZh":140,"license":128,"licenseUrl":129,"sourceUrl":141,"src":142,"width":143,"height":144,"thumb":145,"thumbWidth":135,"thumbHeight":146,"modified":147},"Fig. 2","連續時間軌跡示意：活動區段、靜態控制點與新增控制點在三次 B 樣條上的關係","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2109.04687v1\u002Flocal_window.png","\u002Ffigure-files\u002Fclins2021\u002Ffig-2.webp",1400,990,"\u002Ffigure-files\u002Fclins2021\u002Ffig-2.thumb.webp",339,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":111,"whatZh":149,"license":128,"licenseUrl":129,"sourceUrl":150,"src":151,"width":152,"height":153,"thumb":154,"thumbWidth":135,"thumbHeight":155,"modified":137},"作者自組的無人地面車感測器平台，紅框為收集 YQ 序列所用感測器","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2109.04687v1\u002Fcar.png","\u002Ffigure-files\u002Fclins2021\u002Ffig-5.webp",774,867,"\u002Ffigure-files\u002Fclins2021\u002Ffig-5.thumb.webp",538,{"refId":5,"refLabel":6,"fig":157,"whatZh":158,"license":128,"licenseUrl":129,"sourceUrl":159,"src":160,"width":161,"height":162,"thumb":163,"thumbWidth":135,"thumbHeight":164,"modified":137},"Fig. 6","Fig. 6 下半部：CLINS 含迴圈修正在 YQ-01 建立的點雲地圖（約 700 m 乘 500 m），作者表示與 Google Earth 影像一致；連結僅為地圖子圖","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2109.04687v1\u002Ffigure\u002Fyq-01-map.png","\u002Ffigure-files\u002Fclins2021\u002Ffig-6.webp",1186,705,"\u002Ffigure-files\u002Fclins2021\u002Ffig-6.thumb.webp",285,{"totalRows":166,"groupCount":167,"groups":168,"others":653},35,5,[169,315,517,589],{"slug":170,"group":171,"sourceId":5,"sourceLabel":6,"table":172,"selfRows":173,"metrics":174,"seqs":182,"entrants":197,"cells":208,"outcomes":309,"locators":310,"hardware":311,"wordings":312,"notes":313},"clins2021-table-ii","clins2021:Table II","Table II",12,[175,179],{"label":176,"unit":177,"statistic":178,"alignment":99},"Translation RMSE","m","RMSE",{"label":180,"unit":181,"statistic":178,"alignment":99},"Rotation RMSE","rad",[183,187,189,191,193,195],{"dataset":184,"sequence":185,"environment":186},"LIOM dataset (Ye et al., ICRA 2019)","fast1","room-scale scenes from the LIOM datasets (Sec. V-A), fast to slow motion",{"dataset":184,"sequence":188,"environment":186},"fast2",{"dataset":184,"sequence":190,"environment":186},"mid1",{"dataset":184,"sequence":192,"environment":186},"mid2",{"dataset":184,"sequence":194,"environment":186},"slow1",{"dataset":184,"sequence":196,"environment":186},"slow2",[198,201,204,207],{"name":199,"methodId":200,"linkable":87,"proposed":83,"self":83},"LOAM","loam2014",{"name":202,"methodId":203,"linkable":87,"proposed":83,"self":83},"LIO-SAM","liosam2020",{"name":205,"methodId":206,"linkable":87,"proposed":83,"self":83},"LIOM","liomapping2019",{"name":7,"methodId":5,"linkable":87,"proposed":87,"self":87},[209,213,216,219,222,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,304,305,307],[210,210,210,211,212,210,212,212,210],0,0.4469,-1,[210,210,214,215,212,210,212,212,210],1,0.2023,[210,210,217,218,212,210,212,212,210],2,0.174,[210,210,220,221,212,210,212,212,210],3,0.101,[210,210,223,224,212,210,212,212,210],4,0.0606,[210,210,167,226,212,210,212,212,210],0.0666,[214,210,210,228,212,210,212,212,210],0.1058,[214,210,214,230,212,210,212,212,210],0.1557,[214,210,217,232,212,210,212,212,210],0.1486,[214,210,220,234,212,210,212,212,210],0.0952,[214,210,223,236,212,210,212,212,210],0.0727,[214,210,167,238,212,210,212,212,210],0.0674,[217,210,210,240,212,210,212,212,210],0.0529,[217,210,214,242,212,210,212,212,210],0.0663,[217,210,217,244,212,210,212,212,210],0.0576,[217,210,220,246,212,210,212,212,210],0.0874,[217,210,223,248,212,210,212,212,210],0.0318,[217,210,167,250,212,210,212,212,210],0.0435,[220,210,210,252,212,210,212,212,210],0.0436,[220,210,214,254,212,210,212,212,210],0.0616,[220,210,217,256,212,210,212,212,210],0.0488,[220,210,220,258,212,210,212,212,210],0.0731,[220,210,223,260,212,210,212,212,210],0.0295,[220,210,167,262,212,210,212,212,210],0.0376,[210,214,210,264,212,210,212,212,210],0.1104,[210,214,214,266,212,210,212,212,210],0.0763,[210,214,217,268,212,210,212,212,210],0.0724,[210,214,220,270,212,210,212,212,210],0.0617,[210,214,223,272,212,210,212,212,210],0.0558,[210,214,167,274,212,210,212,212,210],0.0614,[214,214,210,276,212,210,212,212,210],0.1047,[214,214,214,278,212,210,212,212,210],0.1022,[214,214,217,280,212,210,212,212,210],0.102,[214,214,220,282,212,210,212,212,210],0.0789,[214,214,223,284,212,210,212,212,210],0.0698,[214,214,167,286,212,210,212,212,210],0.0715,[217,214,210,288,212,210,212,212,210],0.0537,[217,214,214,290,212,210,212,212,210],0.0574,[217,214,217,292,212,210,212,212,210],0.0523,[217,214,220,294,212,210,212,212,210],0.0567,[217,214,223,296,212,210,212,212,210],0.0496,[217,214,167,298,212,210,212,212,210],0.053,[220,214,210,300,212,210,212,212,210],0.0565,[220,214,214,302,212,210,212,212,210],0.0538,[220,214,217,294,212,210,212,212,210],[220,214,220,302,212,210,212,212,210],[220,214,223,306,212,210,212,212,210],0.0438,[220,214,167,308,212,210,212,212,210],0.057,[],[172],[],[],[314],"LIOM room-scale sequences with ground truth (fast\u002Fmid\u002Fslow motion); translation and rotation RMSE; LOAM and LIOM values copied from the LIOM paper; CLINS knot spacing 0.05 s and poses queried at 100 Hz",{"slug":316,"group":317,"sourceId":318,"sourceLabel":319,"table":320,"selfRows":321,"metrics":322,"seqs":325,"entrants":346,"cells":368,"outcomes":510,"locators":511,"hardware":513,"wordings":514,"notes":515},"clic2023-table-iii","clic2023:Table III","clic2023","Lv et al., 2023","Table III",9,[323],{"label":324,"unit":177,"statistic":178,"alignment":99},"APE (RMSE, meter)",[326,330,332,334,336,338,340,342,344],{"dataset":327,"sequence":328,"environment":329},"NTU VIRAL","eee_01 (237 m)","campus indoor and outdoor MAV flights",{"dataset":327,"sequence":331,"environment":329},"eee_02 (171 m)",{"dataset":327,"sequence":333,"environment":329},"eee_03 (128 m)",{"dataset":327,"sequence":335,"environment":329},"nya_01 (160 m)",{"dataset":327,"sequence":337,"environment":329},"nya_02 (249 m)",{"dataset":327,"sequence":339,"environment":329},"nya_03 (315 m)",{"dataset":327,"sequence":341,"environment":329},"sbs_01 (202 m)",{"dataset":327,"sequence":343,"environment":329},"sbs_02 (184 m)",{"dataset":327,"sequence":345,"environment":329},"sbs_03 (199 m)",[347,349,352,354,356,358,360,362,364,366],{"name":348,"methodId":203,"linkable":87,"proposed":83,"self":83},"LIO-SAM(2) [L, I]",{"name":350,"methodId":351,"linkable":83,"proposed":83,"self":83},"MILIOM (horz. LiDAR)(2) [L, I]",null,{"name":353,"methodId":351,"linkable":83,"proposed":83,"self":83},"VIRAL (horz. LiDAR)(2) [L, I]",{"name":355,"methodId":5,"linkable":87,"proposed":83,"self":87},"CLINS (w\u002Fo loop) [L, I]",{"name":357,"methodId":318,"linkable":87,"proposed":87,"self":83},"CLIO (w\u002Fo loop) [L, I]",{"name":359,"methodId":318,"linkable":87,"proposed":87,"self":83},"CLIC (w\u002Fo loop) [L, I, C]",{"name":361,"methodId":351,"linkable":83,"proposed":83,"self":83},"MILIOM (2 LiDARs)(2) [L2, I]",{"name":363,"methodId":351,"linkable":83,"proposed":83,"self":83},"VIRAL (2 LiDARs)(2) [L2, I, C]",{"name":365,"methodId":318,"linkable":87,"proposed":87,"self":83},"CLIO2 (w\u002Fo loop) [L2, I]",{"name":367,"methodId":318,"linkable":87,"proposed":87,"self":83},"CLIC2 (w\u002Fo loop) [L2, I, C]",[369,371,373,374,376,378,380,383,386,389,391,393,395,396,398,400,401,403,405,407,409,411,412,414,416,417,419,420,422,424,426,428,430,431,432,434,436,437,439,441,442,443,444,445,447,448,449,450,451,452,454,456,457,458,459,461,463,465,466,467,468,469,471,473,474,475,477,478,480,481,483,485,486,487,489,490,491,492,493,494,495,497,499,501,502,503,505,507,508,509],[210,210,210,370,212,210,212,212,210],0.075,[210,210,214,372,212,210,212,212,210],0.069,[210,210,217,221,212,210,212,212,210],[210,210,220,375,212,210,212,212,210],0.076,[210,210,223,377,212,210,212,212,210],0.09,[210,210,167,379,212,210,212,212,210],0.137,[210,210,381,382,212,210,212,212,210],6,0.089,[210,210,384,385,212,210,212,212,210],7,0.083,[210,210,387,388,212,210,212,212,210],8,0.14,[214,210,210,390,212,210,212,212,210],0.104,[214,210,214,392,212,210,212,212,210],0.065,[214,210,217,394,212,210,212,212,210],0.063,[214,210,220,385,212,210,212,212,210],[214,210,223,397,212,210,212,212,210],0.072,[214,210,167,399,212,210,212,212,210],0.058,[214,210,381,375,212,210,212,212,210],[214,210,384,402,212,210,212,212,210],0.081,[214,210,387,404,212,210,212,212,210],0.088,[217,210,210,406,212,210,212,212,210],0.064,[217,210,214,408,212,210,212,212,210],0.051,[217,210,217,410,212,210,212,212,210],0.06,[217,210,220,394,212,210,212,212,210],[217,210,223,413,212,210,212,212,210],0.042,[217,210,167,415,212,210,212,212,210],0.039,[217,210,381,408,212,210,212,212,210],[217,210,384,418,212,210,212,212,210],0.056,[217,210,387,410,212,210,212,212,210],[220,210,210,421,212,210,212,212,210],0.059,[220,210,214,423,212,210,212,212,210],0.03,[220,210,217,425,212,210,212,212,210],0.029,[220,210,220,427,212,210,212,212,210],0.034,[220,210,223,429,212,210,212,212,210],0.04,[220,210,167,415,212,210,212,212,210],[220,210,381,425,212,210,212,212,210],[220,210,384,433,212,210,212,212,210],0.031,[220,210,387,435,212,210,212,212,210],0.033,[223,210,210,423,212,210,212,212,210],[223,210,214,438,212,210,212,212,210],0.023,[223,210,217,440,212,210,212,212,210],0.028,[223,210,220,413,212,210,212,212,210],[223,210,223,298,212,210,212,212,210],[223,210,167,413,212,210,212,212,210],[223,210,381,440,212,210,212,212,210],[223,210,384,446,212,210,212,212,210],0.032,[223,210,387,423,212,210,212,212,210],[167,210,210,423,212,210,212,212,210],[167,210,214,425,212,210,212,212,210],[167,210,217,440,212,210,212,212,210],[167,210,220,429,212,210,212,212,210],[167,210,223,453,212,210,212,212,210],0.054,[167,210,167,455,212,210,212,212,210],0.041,[167,210,381,425,212,210,212,212,210],[167,210,384,433,212,210,212,212,210],[167,210,387,435,212,210,212,212,210],[381,210,210,460,212,210,212,212,210],0.067,[381,210,214,462,212,210,212,212,210],0.066,[381,210,217,464,212,210,212,212,210],0.052,[381,210,220,308,212,210,212,212,210],[381,210,223,460,212,210,212,212,210],[381,210,167,413,212,210,212,212,210],[381,210,381,462,212,210,212,212,210],[381,210,384,470,212,210,212,212,210],0.082,[381,210,387,472,212,210,212,212,210],0.093,[384,210,210,410,212,210,212,212,210],[384,210,214,399,212,210,212,212,210],[384,210,217,476,212,210,212,212,210],0.037,[384,210,220,408,212,210,212,212,210],[384,210,223,479,212,210,212,212,210],0.043,[384,210,167,446,212,210,212,212,210],[384,210,381,482,212,210,212,212,210],0.048,[384,210,384,484,212,210,212,212,210],0.062,[384,210,387,453,212,210,212,212,210],[387,210,210,429,212,210,212,212,210],[387,210,214,488,212,210,212,212,210],0.021,[387,210,217,433,212,210,212,212,210],[387,210,220,423,212,210,212,212,210],[387,210,223,476,212,210,212,212,210],[387,210,167,427,212,210,212,212,210],[387,210,381,435,212,210,212,212,210],[387,210,384,476,212,210,212,212,210],[387,210,387,496,212,210,212,212,210],0.044,[321,210,210,498,212,210,212,212,210],0.038,[321,210,214,500,212,210,212,212,210],0.025,[321,210,217,423,212,210,212,212,210],[321,210,220,425,212,210,212,212,210],[321,210,223,504,212,210,212,212,210],0.036,[321,210,167,506,212,210,212,212,210],0.035,[321,210,381,427,212,210,212,212,210],[321,210,384,506,212,210,212,212,210],[321,210,387,479,212,210,212,212,210],[],[512],"Table III; Sec. VI",[],[],[516],"NTU VIRAL dataset (MAV, indoor and outdoor); APE RMSE in metres; sensors L = LiDAR, I = IMU, C = camera, L2 = two LiDARs; rows marked (2) are results quoted from [51] (VIRAL SLAM preprint) and their loop-closure setting is not stated; CLINS, CLIO and CLIC variants run without loop closure",{"slug":518,"group":519,"sourceId":5,"sourceLabel":6,"table":520,"selfRows":387,"metrics":521,"seqs":524,"entrants":534,"cells":542,"outcomes":583,"locators":584,"hardware":585,"wordings":586,"notes":587},"clins2021-table-iv","clins2021:Table IV","Table IV",[522],{"label":523,"unit":177,"statistic":178,"alignment":99},"RMSE of APE",[525,528,530,532],{"dataset":93,"sequence":526,"environment":527},"YQ-01 (3.26 km)","outdoor campus and urban roads, vehicle",{"dataset":93,"sequence":529,"environment":527},"YQ-02 (0.95 km)",{"dataset":114,"sequence":531,"environment":527},"Kaist-Urban-07 (2.54 km)",{"dataset":114,"sequence":533,"environment":527},"Kaist-Urban-08 (1.56 km)",[535,537,539,540,541],{"name":536,"methodId":203,"linkable":87,"proposed":83,"self":83},"LIO-SAM (odom)",{"name":538,"methodId":5,"linkable":87,"proposed":87,"self":87},"CLINS(odom)",{"name":205,"methodId":206,"linkable":87,"proposed":83,"self":83},{"name":202,"methodId":203,"linkable":87,"proposed":83,"self":83},{"name":7,"methodId":5,"linkable":87,"proposed":87,"self":87},[543,545,547,549,551,553,555,557,559,561,563,565,567,569,571,573,575,577,579,581],[210,210,210,544,212,210,212,212,210],8.857,[210,210,214,546,212,210,212,212,210],3.215,[210,210,217,548,212,210,212,212,210],1.288,[210,210,220,550,212,210,212,212,210],3.524,[214,210,210,552,212,210,212,212,210],5.917,[214,210,214,554,212,210,212,212,210],3.15,[214,210,217,556,212,210,212,212,210],1.383,[214,210,220,558,212,210,212,212,210],3.907,[217,210,210,560,212,210,212,212,210],3.931,[217,210,214,562,212,210,212,212,210],0.881,[217,210,217,564,212,210,212,212,210],1.515,[217,210,220,566,212,210,212,212,210],16.277,[220,210,210,568,212,210,212,212,210],2.22,[220,210,214,570,212,210,212,212,210],2.487,[220,210,217,572,212,210,212,212,210],1.972,[220,210,220,574,212,210,212,212,210],3.951,[223,210,210,576,212,210,212,212,210],2.311,[223,210,214,578,212,210,212,212,210],1.509,[223,210,217,580,212,210,212,212,210],0.562,[223,210,220,582,212,210,212,212,210],1.133,[],[520],[],[],[588],"Large-scale vehicle sequences; APE RMSE (evo) against provided ground truth (RTK-GPS for YQ, dataset ground truth for KAIST); (odom) = without loop correction; knot spacing 0.1 s",{"slug":590,"group":591,"sourceId":318,"sourceLabel":319,"table":592,"selfRows":167,"metrics":593,"seqs":605,"entrants":609,"cells":615,"outcomes":645,"locators":646,"hardware":648,"wordings":650,"notes":651},"clic2023-table-viii","clic2023:Table VIII","Table VIII",[594,597,599,601,603],{"label":595,"unit":596,"statistic":81,"alignment":81},"Update Local Map time over the sequence","s",{"label":598,"unit":596,"statistic":81,"alignment":81},"Update Trajectory time over the sequence",{"label":600,"unit":596,"statistic":81,"alignment":81},"Update Prior time over the sequence",{"label":602,"unit":596,"statistic":81,"alignment":81},"Others time over the sequence",{"label":604,"unit":596,"statistic":81,"alignment":81},"Total time over the sequence",[606],{"dataset":327,"sequence":607,"environment":608},"eee_01 (397 s)","campus MAV flight",[610,611,613],{"name":7,"methodId":5,"linkable":87,"proposed":83,"self":87},{"name":612,"methodId":318,"linkable":87,"proposed":87,"self":83},"CLIO",{"name":614,"methodId":318,"linkable":87,"proposed":87,"self":83},"CLIC",[616,618,620,622,624,626,628,629,631,633,635,637,639,641,643],[210,210,210,617,212,210,210,212,210],17.39,[214,210,210,619,212,210,210,212,210],11.56,[217,210,210,621,212,210,210,212,210],11.52,[210,214,210,623,212,210,210,212,210],1184.34,[214,214,210,625,212,210,210,212,210],58.55,[217,214,210,627,212,210,210,212,210],80.64,[210,217,210,210,212,210,210,212,210],[214,217,210,630,212,210,210,212,210],8.45,[217,217,210,632,212,210,210,212,210],8.44,[210,220,210,634,212,210,210,212,210],400.13,[214,220,210,636,212,210,210,212,210],139.27,[217,220,210,638,212,210,210,212,210],193.97,[210,223,210,640,212,210,210,212,210],1601.86,[214,223,210,642,212,210,210,212,210],217.82,[217,223,210,644,212,210,210,212,210],294.57,[],[647],"Table VIII; Sec. VI-E",[649],"desktop PC, Intel i7-7700K, 32 GB RAM",[],[652],"Time consumption (seconds) of main modules over the whole eee_01 sequence (397 s) of NTU VIRAL on an Intel i7-7700K desktop with 32 GB RAM",[654],{"group":655,"slug":656,"sourceLabel":6,"table":657,"selfRows":214,"datasets":658},"clins2021:Text Sec.V-D","clins2021-text-sec-v-d","Text Sec.V-D",[99],1790510657704]