[{"data":1,"prerenderedAt":1317},["ShallowReactive",2],{"method-loam2014":3},{"method":4,"reference":66,"equipment":85,"figures":131,"results":132},{"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":24,"limitations":28,"sensors":36,"platform":40,"estimator":45,"association":46,"timeModel":47,"deskew":48,"loopClosure":49,"globalOptimization":50,"mapRepresentation":51,"prior":52,"outputGeometry":53,"compute":54,"codeUrl":55,"codeLicense":56,"relatedVersions":57},"loam2014","Zhang & Singh, 2014","LOAM","LOAM: Lidar Odometry and Mapping in Real-time",2014,"classic","C04","odometry_with_local_mapping","LOAM 將 3D LiDAR 的同時定位與建圖拆成兩個並行、頻率不同的演算法：高頻（約 10 Hz）里程計（odometry）以掃描對掃描配準估計速度並校正運動畸變（motion distortion），低頻（約 1 Hz）建圖（mapping）再把去畸變點雲精細配準到地圖。兩者都只使用依局部平滑度挑出的邊緣點（edge）與平面點（planar），分別以點到線、點到面距離作為殘差，並以 Levenberg-Marquardt 最佳化求解。系統沒有迴圈閉合（loop closure），IMU 只是選用的前處理先驗，因此長距離漂移無法做全域修正。","LOAM splits lidar SLAM into a fast scan-to-scan odometry that also removes motion distortion and a slower scan-to-map refinement, both using only smoothness-selected edge and planar points with point-to-line\u002Fplane residuals; it has no loop closure.","full_text_reviewed","peer_reviewed_published","main_body","原論文的室內測試在走廊與大廳（既有建築）中進行，並以固定站點掃描比對地圖匹配誤差，但未在營建工地驗證。Feng 等人 [feng2025_construction_lidar_eval] 在西安醫院門診大樓施工現場（主體結構已封頂、轉入機電安裝與室內裝修階段）與依施工圖建立的 Gazebo 模擬工地中測試「LOAM」，並稱所有演算法使用官方開源儲存庫的預設參數；但 LOAM 原始官方程式已無法取得，該文 Sec. 4.1.1 僅提及 A-LOAM 為開源實作，未明言實際執行的版本（推論：可能為 A-LOAM）。作者報告 LiDAR-only 方法在 z 軸累積漂移並造成地圖翹曲（Sec. 5.2-5.3）。論文未說明實際工地 APE 所用參考軌跡的來源（全文僅描述 Gazebo 模擬的真實軌跡外掛），故實際工地 APE 只能視為作者報告值，不能當作已驗證的幾何精度。",[20,21,22,23],"public_benchmark","controlled_experiment","completed_building","independent_reference",[25,26,27],"Real-time odometry and mapping without high-accuracy ranging or inertial measurements (abstract; Sec. VII)","KITTI odometry benchmark: authors report 0.88% average position error over 100-800 m segments and first rank at submission time (Sec. VII-C)","Indoor relative drift about 1% and outdoor about 2.5% in the authors' corridor\u002Forchard tests at 0.5 m\u002Fs (Sec. VII-A, Table I)",[29,30,31,32,33,34,35],"No loop closure, so accumulated drift is not corrected (Sec. I, VIII)","Assumes smooth, continuous velocity within a sweep; an IMU is needed for abrupt motion (Sec. III, VII-B)","Matching errors larger in natural outdoor scenes than in man-made indoor scenes (Sec. VII-A, Fig. 11)","KITTI results are not reported in the paper itself, only the benchmark rank and 0.88% average error (Sec. VII-C)","Own tests are short (27 to 67 m) at 0.5 m\u002Fs (Sec. VII-A, VII-B, Tables I and II)","Follow-up work states LOAM's global voxel map makes loop closure and absolute measurements (e.g., GPS) hard to add and that it drifts in large-scale tests (liosam2020, Sec. I)","Follow-up work reports that LOAM's feature extraction cannot keep up on an embedded Jetson TX2 and that noisy ground\u002Fvegetation features cause divergence on a small UGV (legoloam2018, Sec. I, IV)",[37,38,39],"3D LiDAR (custom rotating Hokuyo UTM-30LX 2D scanner)","IMU (optional, Xsens MTi-10)","3D LiDAR (360 deg Velodyne lidar via KITTI, 10 Hz; the model is not named in this paper)",[41,42,43,44],"cart (pushed, indoor)","ground vehicle","handheld","vehicle (KITTI)","Levenberg-Marquardt nonlinear least squares with bisquare robust weights; scan-to-scan odometry at about 10 Hz and scan-to-map mapping at about 1 Hz running in parallel (Sec. IV-B, V-C, VI)","edge and planar feature points selected by local smoothness per scan line; point-to-line and point-to-plane distances; mapping stage finds line\u002Fplane correspondences by eigen-analysis of local map point clusters (Sec. V-A, V-B, VI)","discrete sweep poses with constant angular and linear velocity (linear pose interpolation) inside a sweep (Sec. V-C)","points reprojected with the linearly interpolated odometry pose; optional IMU preprocessing removes orientation change and part of acceleration-induced distortion (Sec. V-C, VII-B)","none (stated in Sec. I and listed as future work in Sec. VIII)","none","registered point cloud map Q_k built from the undistorted sweeps, stored in 10 m cubes; cubes intersecting the new sweep are loaded into a KD-tree; matching uses ten times more feature points than odometry with edge or plane neighbourhoods found by eigen-analysis; the map is downsized with a 5 cm voxel grid","none (IMU optional)","motion-corrected registered point cloud map (5 cm voxel-grid downsampled) and 6-DoF pose at about 10 Hz (Sec. VI); dense raw-point export not described in paper","real time on a laptop with 2.5 GHz quad cores and 6 GiB memory; odometry and mapping each run on a separate core; ROS on Linux",null,"not_verified",[58,62],{"relation":59,"title":60,"doi_or_url":61},"journal_extension","Low-drift and real-time lidar odometry and mapping","10.1007\u002Fs10514-016-9548-2",{"relation":63,"title":64,"doi_or_url":65},"code_release","loam_back_and_forth \u002F loam_continuous (ROS wiki pages cited in paper; linked GitHub repositories return 404 on 2026-09-25)","http:\u002F\u002Fwiki.ros.org\u002Floam_back_and_forth",{"id":5,"kind":67,"shortName":7,"title":8,"authors":68,"year":9,"venue":71,"venueType":72,"publisher":73,"volumeIssuePages":74,"doi":75,"arxivId":55,"url":76,"firstPublicDate":77,"publicationStatus":16,"metadataStatus":78,"fulltextStatus":15,"era":10,"classicReason":79,"codeUrl":55,"cluster":11,"topics":80,"mdpi":81,"verification":82,"label":6,"fulltextRoute":83,"versionRead":84,"addedByCensus":81},"method",[69,70],"Ji Zhang","Sanjiv Singh","Robotics: Science and Systems X (RSS 2014)","conference","RSS Foundation","RSS X, paper p07 (proceedings URL rss10\u002Fp07.pdf)","10.15607\u002Frss.2014.x.007","https:\u002F\u002Fwww.roboticsproceedings.org\u002Frss10\u002Fp07.pdf","2014-07-12","metadata_verified","principle reused \u002F reproducible baseline: edge-planar feature selection by local smoothness, point-to-line and point-to-plane residuals, and the split into high-rate odometry and low-rate mapping are explicitly reused by LeGO-LOAM, F-LOAM, Loam_livox, LIO-SAM and LiLi-OM, and LOAM is the baseline in most later papers of this cluster.",[11],false,"corrected","publisher OA","version of record, RSS X (2014) online proceedings PDF p07",[86,93,98,104,108,112,114,116,122,128],{"category":87,"model":88,"canonical":89,"role":90,"dataset":55,"specs":91,"locator":92},"lidar","Hokuyo UTM-30LX (custom rotating 3D lidar)","Hokuyo UTM-30LX","method input","180 deg FoV, 0.25 deg resolution, 40 lines\u002Fs; motor rotates at 180 deg\u002Fs between -90 and 90 deg (1 s sweep); encoder resolution 0.25 deg","Sec. IV-A; Fig. 2",{"category":94,"model":95,"canonical":95,"role":90,"dataset":55,"specs":96,"locator":97},"imu","Xsens MTi-10","optional; orientation from a Kalman filter on gyro and accelerometer used to preprocess the point cloud","Sec. VII-B",{"category":99,"model":100,"canonical":100,"role":101,"dataset":55,"specs":102,"locator":103},"gnss","high accuracy GPS\u002FINS","reference or ground truth","on the ground vehicle in the orchard drift test","Sec. VII-A",{"category":105,"model":106,"canonical":106,"role":101,"dataset":55,"specs":107,"locator":97},"other","tape ruler","manual ground-truth measurement for the IMU comparison tests",{"category":109,"model":110,"canonical":110,"role":90,"dataset":55,"specs":111,"locator":103},"platform","cart pushed by a person","carries lidar, battery and laptop; indoor tests at 0.5 m\u002Fs",{"category":109,"model":42,"canonical":42,"role":90,"dataset":55,"specs":113,"locator":103},"lidar mounted at the front; outdoor tests at 0.5 m\u002Fs",{"category":109,"model":43,"canonical":43,"role":90,"dataset":55,"specs":115,"locator":97},"person walks at 0.5 m\u002Fs moving the lidar up and down about 0.5 m; staircase test",{"category":117,"model":118,"canonical":118,"role":119,"dataset":55,"specs":120,"locator":121},"compute","laptop, 2.5 GHz quad cores","compute for runtime","6 GiB memory; two cores used","Sec. VII",{"category":87,"model":123,"canonical":123,"role":124,"dataset":125,"specs":126,"locator":127},"Velodyne lidar (360 deg, model not named)","dataset sensor","KITTI odometry benchmark","logged at 10 Hz","Sec. VII-C; Fig. 13",{"category":99,"model":100,"canonical":100,"role":101,"dataset":125,"specs":129,"locator":130},"ground truth of the KITTI benchmark","Sec. VII-C",[],{"totalRows":133,"groupCount":134,"groups":135,"others":1072},311,49,[136,275,576,782],{"slug":137,"group":138,"sourceId":139,"sourceLabel":140,"table":141,"selfRows":142,"metrics":143,"seqs":155,"entrants":166,"cells":171,"outcomes":266,"locators":268,"hardware":269,"wordings":272,"notes":273},"legoloam2018-table-iv","legoloam2018:Table IV","legoloam2018","Shan & Englot, 2018","Table IV",24,[144,149,151,153],{"label":145,"unit":146,"statistic":147,"alignment":148},"runtime of segmentation module per scan","ms","mean","not_reported",{"label":150,"unit":146,"statistic":147,"alignment":148},"runtime of feature extraction module per scan",{"label":152,"unit":146,"statistic":147,"alignment":148},"runtime of lidar odometry module per scan",{"label":154,"unit":146,"statistic":147,"alignment":148},"runtime of lidar mapping module per scan",[156,160,163],{"dataset":157,"sequence":158,"environment":159},"Own Jackal UGV datasets","Experiment 1","Stevens campus, smooth roads, 1.09 km, 11 m elevation change",{"dataset":157,"sequence":161,"environment":162},"Experiment 2","Stevens campus incl. sidewalk bordered by grass and trees, 1.24 km, 11 m elevation change",{"dataset":157,"sequence":164,"environment":165},"Experiment 3","forested hiking trail (dirt, asphalt, grass), 2.71 km, 19 m elevation change",[167,169],{"name":7,"methodId":5,"linkable":168,"proposed":81,"self":168},true,{"name":170,"methodId":139,"linkable":168,"proposed":168,"self":81},"LeGO-LOAM",[172,175,178,180,182,185,187,190,192,193,195,197,199,201,203,205,207,208,210,212,214,216,218,220,222,223,225,227,229,231,233,235,237,238,240,242,244,246,248,250,252,253,255,257,259,261,262,264],[173,173,173,55,173,173,173,174,173],0,-1,[176,173,173,177,174,173,173,174,173],1,29.3,[173,176,173,179,174,173,173,174,173],105.1,[176,176,173,181,174,173,173,174,173],9.1,[173,183,173,184,174,173,173,174,173],2,133.4,[176,183,173,186,174,173,173,174,173],19.3,[173,188,173,189,174,173,173,174,173],3,702.3,[176,188,173,191,174,173,173,174,173],266.7,[173,173,176,55,173,173,173,174,173],[176,173,176,194,174,173,173,174,173],29.9,[173,176,176,196,174,173,173,174,173],106.7,[176,176,176,198,174,173,173,174,173],9.9,[173,183,176,200,174,173,173,174,173],124.5,[176,183,176,202,174,173,173,174,173],18.6,[173,188,176,204,174,173,173,174,173],793.6,[176,188,176,206,174,173,173,174,173],278.2,[173,173,183,55,173,173,173,174,173],[176,173,183,209,174,173,173,174,173],36.8,[173,176,183,211,174,173,173,174,173],104.6,[176,176,183,213,174,173,173,174,173],6.1,[173,183,183,215,174,173,173,174,173],122.1,[176,183,183,217,174,173,173,174,173],18.1,[173,188,183,219,174,173,173,174,173],850.9,[176,188,183,221,174,173,173,174,173],253.3,[173,173,173,55,173,173,176,174,173],[176,173,173,224,174,173,176,174,173],16.7,[173,176,173,226,174,173,176,174,173],50.4,[176,176,173,228,174,173,176,174,173],4,[173,183,173,230,174,173,176,174,173],69.8,[176,183,173,232,174,173,176,174,173],6.8,[173,188,173,234,174,173,176,174,173],289.4,[176,188,173,236,174,173,176,174,173],108.2,[173,173,176,55,173,173,176,174,173],[176,173,176,239,174,173,176,174,173],17,[173,176,176,241,174,173,176,174,173],49.3,[176,176,176,243,174,173,176,174,173],4.4,[173,183,176,245,174,173,176,174,173],66.5,[176,183,176,247,174,173,176,174,173],6.5,[173,188,176,249,174,173,176,174,173],330.5,[176,188,176,251,174,173,176,174,173],116.7,[173,173,183,55,173,173,176,174,173],[176,173,183,254,174,173,176,174,173],20,[173,176,183,256,174,173,176,174,173],48.5,[176,176,183,258,174,173,176,174,173],2.3,[173,183,183,260,174,173,176,174,173],63,[176,183,183,213,174,173,176,174,173],[173,188,183,263,174,173,176,174,173],344.9,[176,188,183,265,174,173,176,174,173],101.7,[267],"not_applicable (N\u002FA in table)",[141],[270,271],"Nvidia Jetson TX2 (ARM Cortex-A57), CPU only","laptop, 2.5 GHz Intel i7-4710MQ, CPU only",[],[274],"Runtime of each module for processing one scan, averaged over 10 real-time trials; LOAM has no segmentation module",{"slug":276,"group":277,"sourceId":278,"sourceLabel":279,"table":280,"selfRows":281,"metrics":282,"seqs":292,"entrants":326,"cells":361,"outcomes":568,"locators":569,"hardware":570,"wordings":573,"notes":574},"mulls2021-table-ii","mulls2021:Table II","mulls2021","Pan et al., 2021","Table II",15,[283,286,289],{"label":284,"unit":285,"statistic":147,"alignment":148},"ATE [%] (average translation error)","%",{"label":287,"unit":288,"statistic":147,"alignment":148},"ARE [deg\u002F100m] (average rotation error)","deg\u002F100m",{"label":290,"unit":291,"statistic":148,"alignment":50},"time (s)\u002Fframe","s",[293,297,300,303,305,307,309,311,313,315,317,319,322,324],{"dataset":294,"sequence":295,"environment":296},"KITTI odometry","00","urban",{"dataset":294,"sequence":298,"environment":299},"01","highway",{"dataset":294,"sequence":301,"environment":302},"02","country",{"dataset":294,"sequence":304,"environment":302},"03",{"dataset":294,"sequence":306,"environment":302},"04",{"dataset":294,"sequence":308,"environment":302},"05",{"dataset":294,"sequence":310,"environment":296},"06",{"dataset":294,"sequence":312,"environment":296},"07",{"dataset":294,"sequence":314,"environment":296},"08",{"dataset":294,"sequence":316,"environment":302},"09",{"dataset":294,"sequence":318,"environment":302},"10",{"dataset":294,"sequence":320,"environment":321},"00-10 mean","urban, highway, country",{"dataset":294,"sequence":323,"environment":321},"11-21 mean (online test set)",{"dataset":294,"sequence":325,"environment":321},"per frame",[327,329,331,333,335,337,340,342,345,347,349,351,353,355,357,359],{"name":328,"methodId":5,"linkable":168,"proposed":81,"self":168},"LOAM [10]",{"name":330,"methodId":55,"linkable":81,"proposed":81,"self":81},"IMLS-SLAM [11]",{"name":332,"methodId":55,"linkable":81,"proposed":81,"self":81},"MC2SLAM [13]",{"name":334,"methodId":55,"linkable":81,"proposed":81,"self":81},"S4-SLAM [26]*",{"name":336,"methodId":55,"linkable":81,"proposed":81,"self":81},"PSF-LO [27]",{"name":338,"methodId":339,"linkable":168,"proposed":81,"self":81},"SUMA++ [16]*","sumapp2019",{"name":341,"methodId":55,"linkable":81,"proposed":81,"self":81},"LiTAMIN2 [51]*",{"name":343,"methodId":344,"linkable":168,"proposed":81,"self":81},"LO-Net [18]","lonet2019",{"name":346,"methodId":55,"linkable":81,"proposed":81,"self":81},"FALO [25]",{"name":348,"methodId":55,"linkable":81,"proposed":81,"self":81},"LoDoNet [28]",{"name":350,"methodId":278,"linkable":168,"proposed":168,"self":81},"MULLS-LO(mc)",{"name":352,"methodId":278,"linkable":168,"proposed":168,"self":81},"MULLS-SLAM(mc)*",{"name":354,"methodId":278,"linkable":168,"proposed":168,"self":81},"MULLS-LO(s1)",{"name":356,"methodId":278,"linkable":168,"proposed":168,"self":81},"MULLS-SLAM(m1)*",{"name":358,"methodId":278,"linkable":168,"proposed":168,"self":81},"MULLS-SLAM(m5)*",{"name":360,"methodId":278,"linkable":168,"proposed":168,"self":81},"MULLS-SLAM(s5m5)*",[362,364,366,368,370,372,375,378,381,384,387,390,393,396,398,401,403,405,407,409,411,413,414,415,417,418,419,421,423,425,427,429,430,432,433,435,437,439,441,442,444,445,446,447,449,450,451,453,455,457,459,460,461,462,464,466,467,469,471,473,474,475,477,479,481,483,485,486,487,489,491,492,494,496,498,500,502,503,504,506,507,508,510,512,513,514,515,516,518,519,520,521,523,525,527,529,531,532,534,535,536,537,539,541,542,543,544,545,546,547,548,549,550,551,552,553,554,556,558,559,561,564,566,567],[173,173,173,363,174,173,174,174,173],0.78,[173,173,176,365,174,173,174,174,173],1.43,[173,173,183,367,174,173,174,174,173],0.92,[173,173,188,369,174,173,174,174,173],0.86,[173,173,228,371,174,173,174,174,173],0.71,[173,173,373,374,174,173,174,174,173],5,0.57,[173,173,376,377,174,173,174,174,173],6,0.65,[173,173,379,380,174,173,174,174,173],7,0.63,[173,173,382,383,174,173,174,174,173],8,1.12,[173,173,385,386,174,173,174,174,173],9,0.77,[173,173,388,389,174,173,174,174,173],10,0.79,[173,173,391,392,174,173,174,174,173],11,0.84,[173,173,394,395,174,173,174,174,173],12,0.55,[173,176,394,397,174,173,174,174,173],0.13,[173,183,399,400,174,173,173,174,173],13,0.1,[176,173,173,402,174,173,174,174,173],0.5,[176,173,176,404,174,173,174,174,173],0.82,[176,173,183,406,174,173,174,174,173],0.53,[176,173,188,408,174,173,174,174,173],0.68,[176,173,228,410,174,173,174,174,173],0.33,[176,173,373,412,174,173,174,174,173],0.32,[176,173,376,410,174,173,174,174,173],[176,173,379,410,174,173,174,174,173],[176,173,382,416,174,173,174,174,173],0.8,[176,173,385,395,174,173,174,174,173],[176,173,388,406,174,173,174,174,173],[176,173,391,420,174,173,174,174,173],0.52,[176,173,394,422,174,173,174,174,173],0.69,[176,176,394,424,174,173,174,174,173],0.18,[176,183,399,426,174,173,173,174,173],1.25,[183,173,173,428,174,173,174,174,173],0.51,[183,173,176,389,174,173,174,174,173],[183,173,183,431,174,173,174,174,173],0.54,[183,173,188,377,174,173,174,174,173],[183,173,228,434,174,173,174,174,173],0.44,[183,173,373,436,174,173,174,174,173],0.27,[183,173,376,438,174,173,174,174,173],0.31,[183,173,379,440,174,173,174,174,173],0.34,[183,173,382,392,174,173,174,174,173],[183,173,385,443,174,173,174,174,173],0.46,[183,173,388,420,174,173,174,174,173],[183,173,391,420,174,173,174,174,173],[183,173,394,422,174,173,174,174,173],[183,176,394,448,174,173,174,174,173],0.16,[183,183,399,400,174,173,173,174,173],[188,173,391,367,174,173,174,174,173],[188,173,394,452,174,173,174,174,173],0.93,[188,176,394,454,174,173,174,174,173],0.38,[188,183,399,456,174,173,173,174,173],0.2,[228,173,391,458,174,173,174,174,173],0.74,[228,173,394,404,174,173,174,174,173],[228,176,394,412,174,173,174,174,173],[228,183,399,456,174,173,173,174,173],[373,173,173,463,174,173,174,174,173],0.64,[373,173,176,465,174,173,174,174,173],1.6,[373,173,183,176,174,173,174,174,173],[373,173,188,468,174,173,174,174,173],0.67,[373,173,228,470,174,173,174,174,173],0.37,[373,173,373,472,174,173,174,174,173],0.4,[373,173,376,443,174,173,174,174,173],[373,173,379,440,174,173,174,174,173],[373,173,382,476,174,173,174,174,173],1.1,[373,173,385,478,174,173,174,174,173],0.47,[373,173,388,480,174,173,174,174,173],0.66,[373,173,391,482,174,173,174,174,173],0.7,[373,173,394,484,174,173,174,174,173],1.06,[373,176,394,440,174,173,174,174,173],[373,183,399,400,174,173,173,174,173],[376,173,391,488,174,173,174,174,173],0.85,[376,183,399,490,174,173,173,174,173],0.01,[379,173,173,363,174,173,174,174,173],[379,173,176,493,174,173,174,174,173],1.42,[379,173,183,495,174,173,174,174,173],1.01,[379,173,188,497,174,173,174,174,173],0.73,[379,173,228,499,174,173,174,174,173],0.56,[379,173,373,501,174,173,174,174,173],0.62,[379,173,376,395,174,173,174,174,173],[379,173,379,499,174,173,174,174,173],[379,173,382,505,174,173,174,174,173],1.08,[379,173,385,386,174,173,174,174,173],[379,173,388,367,174,173,174,174,173],[379,173,391,509,174,173,174,174,173],0.83,[379,173,394,511,174,173,174,174,173],1.75,[379,176,394,389,174,173,174,174,173],[379,183,399,400,174,173,173,174,173],[382,173,391,176,174,173,174,174,173],[382,183,399,400,174,173,173,174,173],[385,173,391,517,174,173,174,174,173],1.27,[388,173,173,428,174,173,174,174,173],[388,173,176,501,174,173,174,174,173],[388,173,183,395,174,173,174,174,173],[388,173,188,522,174,173,174,174,173],0.61,[388,173,228,524,174,173,174,174,173],0.35,[388,173,373,526,174,173,174,174,173],0.28,[388,173,376,528,174,173,174,174,173],0.24,[388,173,379,530,174,173,174,174,173],0.29,[388,173,382,416,174,173,174,174,173],[388,173,385,533,174,173,174,174,173],0.49,[388,173,388,522,174,173,174,174,173],[388,173,391,533,174,173,174,174,173],[388,173,394,377,174,173,174,174,173],[388,176,394,538,174,173,174,174,173],0.19,[388,183,399,540,174,173,176,174,173],0.08,[391,173,173,431,174,173,174,174,173],[391,173,176,501,174,173,174,174,173],[391,173,183,422,174,173,174,174,173],[391,173,188,522,174,173,174,174,173],[391,173,228,524,174,173,174,174,173],[391,173,373,530,174,173,174,174,173],[391,173,376,530,174,173,174,174,173],[391,173,379,436,174,173,174,174,173],[391,173,382,509,174,173,174,174,173],[391,173,385,428,174,173,174,174,173],[391,173,388,522,174,173,174,174,173],[391,173,391,420,174,173,174,174,173],[391,183,399,400,174,173,176,174,173],[394,173,391,555,174,173,174,174,173],2.57,[394,183,399,557,174,173,176,174,173],0.03,[399,173,391,386,174,173,174,174,173],[399,183,399,560,174,173,176,174,173],0.05,[562,173,391,563,174,173,174,174,173],14,0.6,[562,183,399,565,174,173,176,174,173],0.07,[281,173,391,522,174,173,174,174,173],[281,183,399,540,174,173,176,174,173],[],[280],[571,572],"not_reported (value taken from original paper or KITTI leaderboard)","Intel Core i7-7700HQ @2.80GHz",[],[575],"KITTI odometry ATE [%] and ARE [deg\u002F100m], averaged over 100-800 m segments; baseline values from original papers and KITTI leaderboard; * = with loop closure; time in s per frame",{"slug":577,"group":578,"sourceId":579,"sourceLabel":580,"table":581,"selfRows":562,"metrics":582,"seqs":589,"entrants":606,"cells":620,"outcomes":775,"locators":777,"hardware":778,"wordings":779,"notes":780},"licfusion2-2020-table-vi","licfusion2_2020:Table VI","licfusion2_2020","Zuo et al., 2020","Table VI",[583,586],{"label":584,"unit":585,"statistic":148,"alignment":148},"averaged ATE, orientation (deg)","deg",{"label":587,"unit":588,"statistic":148,"alignment":148},"averaged ATE, position (m)","m",[590,594,596,598,600,602,604],{"dataset":591,"sequence":592,"environment":593},"Vicon Room sequences (authors' data)","Seq 1 (42.62 m)","indoor Vicon motion-capture room 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ATE of 5 runs on 6 Vicon-room sequences (cluttered room, Vicon ground truth), orientation (deg) and position (m); ATE computed following Zhang and Scaramuzza [23]; '-' = translational error above 20 m. The Average column is printed by the authors (for LIO-MAP and LIC-Fusion it averages only the sequences that did not fail).",{"slug":783,"group":784,"sourceId":785,"sourceLabel":786,"table":787,"selfRows":399,"metrics":788,"seqs":797,"entrants":823,"cells":848,"outcomes":1066,"locators":1067,"hardware":1068,"wordings":1069,"notes":1070},"pwclonet2021-table-1","pwclonet2021:Table 1","pwclonet2021","Wang et al., 2021c","Table 1",[789,793,795],{"label":790,"unit":285,"statistic":791,"alignment":792},"trel (average translational RMSE, %)","RMSE","not_applicable",{"label":794,"unit":285,"statistic":791,"alignment":792},"Mean on 07-10, trel",{"label":796,"unit":288,"statistic":791,"alignment":792},"Mean on 07-10, rrel (deg\u002F100m)",[798,801,803,805,807,809,811,813,815,817,819,821],{"dataset":294,"sequence":799,"environment":800},"00* (training)","vehicle, road",{"dataset":294,"sequence":802,"environment":800},"01* (training)",{"dataset":294,"sequence":804,"environment":800},"02* (training)",{"dataset":294,"sequence":806,"environment":800},"03* (training)",{"dataset":294,"sequence":808,"environment":800},"04* (training)",{"dataset":294,"sequence":810,"environment":800},"05* (training)",{"dataset":294,"sequence":812,"environment":800},"06* (training)",{"dataset":294,"sequence":814,"environment":800},"07 (test)",{"dataset":294,"sequence":816,"environment":800},"08 (test)",{"dataset":294,"sequence":818,"environment":800},"09 (test)",{"dataset":294,"sequence":820,"environment":800},"10 (test)",{"dataset":294,"sequence":822,"environment":800},"mean on 07-10 (test)",[824,827,830,833,836,838,840,842,844,846],{"name":825,"methodId":826,"linkable":168,"proposed":81,"self":81},"Full LOAM [31]","loam2017_auro",{"name":828,"methodId":829,"linkable":168,"proposed":81,"self":81},"ICP-po2po","besl1992icp",{"name":831,"methodId":832,"linkable":168,"proposed":81,"self":81},"ICP-po2pl","chen1992pointtoplane",{"name":834,"methodId":835,"linkable":168,"proposed":81,"self":81},"GICP [19]","segal2009gicp",{"name":837,"methodId":55,"linkable":81,"proposed":81,"self":81},"CLS [21]",{"name":839,"methodId":55,"linkable":81,"proposed":81,"self":81},"Velas et al. 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