[{"data":1,"prerenderedAt":1099},["ShallowReactive",2],{"method-liomapping2019":3},{"method":4,"reference":61,"equipment":81,"figures":113,"results":114},{"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":27,"sensors":34,"platform":37,"estimator":41,"association":42,"timeModel":43,"deskew":44,"loopClosure":45,"globalOptimization":46,"mapRepresentation":47,"prior":48,"outputGeometry":49,"compute":50,"codeUrl":51,"codeLicense":52,"relatedVersions":53},"liomapping2019","Ye et al., 2019","LIO-mapping (LIOM)","Tightly Coupled 3D Lidar Inertial Odometry and Mapping",2019,"recent","C04","odometry_with_local_mapping","LIO-mapping 在滑動視窗內以固定延遲平滑器（fixed-lag smoother）與邊緣化，將 IMU 預積分與 LiDAR 平面特徵的點到面殘差聯合最佳化，並同時線上估計 LiDAR-IMU 外參。里程計之後再做「旋轉約束」的全域地圖配準：利用里程計對 roll、pitch 的較佳估計修改最佳化，使地圖持續與重力方向對齊。論文指出此法需要足夠 IMU 激勵才能初始化。","LIO-mapping jointly optimizes preintegrated IMU and planar lidar residuals in a sliding window (with online extrinsics), then refines poses against the global map under rotational constraints to keep the map gravity-aligned.","full_text_reviewed","peer_reviewed_published","background","not_reported",[20,21,22],"controlled_experiment","independent_reference","public_benchmark",[24,25,26],"Motion-capture RMSE in six handheld sequences: LIO-mapping translation 0.0318-0.0874 m vs LOAM 0.0606-0.4469 m (Sec. VII-A, Table I)","Motion compensation and online extrinsic estimation both improve accuracy, especially under fast motion (Sec. VII-A1)","Works in lidar-degraded cases with sufficient IMU excitation (Sec. VIII)",[28,29,30,31,32,33],"Requires initialization with sufficient motion (Sec. VI-B, VIII)","Odometry-only LIO drifts when motion is slow because the local map is sparse (Sec. VII-A1)","To stay real time, sweeps are processed at 0.2 s (indoor) or 0.3 s (outdoor) intervals and some are skipped (Sec. VII-C)","Outdoor golf-cart and KAIST Urban results are only shown in a supplementary video, without quantitative evaluation (Sec. VII-B)","Follow-up work reports it ran at about 0.56-0.67x real time and failed to initialize on several datasets (liosam2020, Sec. II, IV)","Follow-up work reports more than 100 ms per scan for its LIO module (lins2020, Sec. I, Table II)",[35,36],"3D LiDAR (Velodyne VLP-16)","IMU (Xsens MTi-100, 400 Hz)",[38,39,40],"handheld","golf cart","vehicle (KAIST Urban, qualitative)","fixed-lag smoother over a sliding window with marginalization (MAP, Gauss-Newton via Ceres) jointly optimizing IMU states and lidar-IMU extrinsics; followed by rotation-constrained refinement against the global map (Sec. IV-E, V)","LOAM-style features; only planar features used in odometry; KNN plane fitting in a local map built in the pivot frame (relative lidar measurements) (Sec. IV-B, IV-C)","discrete states in a sliding window (Sec. IV-E)","IMU-propagated motion with a linear motion model interpolates each point to the sweep end (Sec. IV-B)","none","none (rotation-constrained scan-to-global-map refinement keeps map aligned with gravity) (Sec. V)","global feature point cloud map as by-product of refinement (Sec. V)","no prior map; for car-mounted configurations a prior term on the lidar-IMU extrinsic translation is added; initialization uses LOAM lidar odometry followed by VINS-Mono style IMU state and extrinsic initialization","global point cloud map and poses at IMU rate (Sec. V, VII-C)","Intel i7-7700K at 4.20 GHz, 16 GB RAM; mean odometry 128.7 ms (indoor) and 213.5 ms (outdoor), mapping 108.3 and 167.6 ms per input, IMU prediction about 0.01 ms; LiDAR sweeps processed at 0.2 s (indoor) or 0.3 s (outdoor) intervals, skipping some sweeps to keep real time","https:\u002F\u002Fgithub.com\u002Fhyye\u002Flio-mapping","GPL-3.0 (LICENSE file)",[54,58],{"relation":55,"title":56,"doi_or_url":57},"preprint","arXiv 1904.06993 (accepted by ICRA 2019)","https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.06993",{"relation":59,"title":60,"doi_or_url":51},"code_release","hyye\u002Flio-mapping",{"id":5,"kind":62,"shortName":7,"title":8,"authors":63,"year":9,"venue":67,"venueType":68,"publisher":69,"volumeIssuePages":70,"doi":71,"arxivId":72,"url":57,"firstPublicDate":73,"publicationStatus":16,"metadataStatus":74,"fulltextStatus":15,"era":10,"classicReason":75,"codeUrl":51,"cluster":11,"topics":76,"mdpi":77,"verification":78,"label":6,"fulltextRoute":79,"versionRead":80,"addedByCensus":77},"method",[64,65,66],"Haoyang Ye","Yuying Chen","Ming Liu","2019 International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 3144-3150","10.1109\u002Ficra.2019.8793511","1904.06993","2019-04-15","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv 1904.06993v1 (2019-04-15), the only arXiv version; not compared with the ICRA 2019 version of record",[82,89,94,99,104,107],{"category":83,"model":84,"canonical":84,"role":85,"dataset":86,"specs":87,"locator":88},"lidar","Velodyne VLP-16","method input",null,"16 lines, 10 Hz, mounted above the IMU in the handheld sensor pair","Sec. VII-A; Fig. 4a",{"category":90,"model":91,"canonical":91,"role":85,"dataset":86,"specs":92,"locator":93},"imu","Xsens MTi-100","400 Hz","Sec. VII-A",{"category":95,"model":96,"canonical":96,"role":97,"dataset":86,"specs":98,"locator":93},"other","motion capture system with reflective markers","reference or ground truth","provides ground-truth poses for the handheld sequences",{"category":100,"model":101,"canonical":101,"role":85,"dataset":86,"specs":102,"locator":103},"platform","handheld sensor pair","lidar and IMU close together; attached camera only records the scene","Sec. VI-A; Fig. 4a",{"category":100,"model":39,"canonical":39,"role":85,"dataset":86,"specs":105,"locator":106},"lidar at the front and IMU above the base link (sensor models not stated)","Sec. VI-A; Fig. 4b",{"category":108,"model":109,"canonical":109,"role":110,"dataset":86,"specs":111,"locator":112},"compute","Intel i7-7700K","compute for runtime","4.20 GHz, 16 GB RAM","Sec. VII-C",[],{"totalRows":115,"groupCount":116,"groups":117,"others":990},195,23,[118,292,534,734],{"slug":119,"group":120,"sourceId":5,"sourceLabel":6,"table":121,"selfRows":122,"metrics":123,"seqs":131,"entrants":146,"cells":159,"outcomes":286,"locators":287,"hardware":288,"wordings":289,"notes":290},"liomapping2019-table-i","liomapping2019:Table I","Table I",48,[124,128],{"label":125,"unit":126,"statistic":127,"alignment":18},"Translation RMSE w.r.t. ground truth","m","RMSE",{"label":129,"unit":130,"statistic":127,"alignment":18},"Rotation RMSE w.r.t. ground truth","rad",[132,136,138,140,142,144],{"dataset":133,"sequence":134,"environment":135},"Own handheld motion-capture sequences","fast 1","indoor motion-capture area",{"dataset":133,"sequence":137,"environment":135},"fast 2",{"dataset":133,"sequence":139,"environment":135},"med 1",{"dataset":133,"sequence":141,"environment":135},"med 2",{"dataset":133,"sequence":143,"environment":135},"slow 1",{"dataset":133,"sequence":145,"environment":135},"slow 2",[147,151,153,155,157],{"name":148,"methodId":149,"linkable":150,"proposed":77,"self":77},"LOAM","loam2014",true,{"name":152,"methodId":5,"linkable":150,"proposed":77,"self":150},"LIO-raw (no motion compensation)",{"name":154,"methodId":5,"linkable":150,"proposed":77,"self":150},"LIO-no-ex (no online extrinsic estimation)",{"name":156,"methodId":5,"linkable":150,"proposed":150,"self":150},"LIO",{"name":158,"methodId":5,"linkable":150,"proposed":150,"self":150},"LIO-mapping",[160,164,167,169,171,174,176,179,181,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,261,263,265,268,270,272,274,276,278,280,282,284],[161,161,161,162,163,161,163,163,161],0,0.4469,-1,[161,165,161,166,163,161,163,163,161],1,0.1104,[165,161,161,168,163,161,163,163,161],0.2464,[165,165,161,170,163,161,163,163,161],0.1123,[172,161,161,173,163,161,163,163,161],2,0.0957,[172,165,161,175,163,161,163,163,161],0.0547,[177,161,161,178,163,161,163,163,161],3,0.0949,[177,165,161,180,163,161,163,163,161],0.0545,[182,161,161,183,163,161,163,163,161],4,0.0529,[182,165,161,185,163,161,163,163,161],0.0537,[161,161,165,187,163,161,163,163,161],0.2023,[161,165,165,189,163,161,163,163,161],0.0763,[165,161,165,191,163,161,163,163,161],0.4346,[165,165,165,193,163,161,163,163,161],0.1063,[172,161,165,195,163,161,163,163,161],0.121,[172,165,165,197,163,161,163,163,161],0.0784,[177,161,165,199,163,161,163,163,161],0.0755,[177,165,165,201,163,161,163,163,161],0.0581,[182,161,165,203,163,161,163,163,161],0.0663,[182,165,165,205,163,161,163,163,161],0.0574,[161,161,172,207,163,161,163,163,161],0.174,[161,165,172,209,163,161,163,163,161],0.0724,[165,161,172,211,163,161,163,163,161],0.1413,[165,165,172,213,163,161,163,163,161],0.062,[172,161,172,215,163,161,163,163,161],0.1677,[172,165,172,217,163,161,163,163,161],0.0596,[177,161,172,219,163,161,163,163,161],0.1002,[177,165,172,221,163,161,163,163,161],0.057,[182,161,172,223,163,161,163,163,161],0.0576,[182,165,172,225,163,161,163,163,161],0.0523,[161,161,177,227,163,161,163,163,161],0.101,[161,165,177,229,163,161,163,163,161],0.0617,[165,161,177,231,163,161,163,163,161],0.246,[165,165,177,233,163,161,163,163,161],0.0886,[172,161,177,235,163,161,163,163,161],0.3032,[172,165,177,237,163,161,163,163,161],0.09,[177,161,177,239,163,161,163,163,161],0.1308,[177,165,177,241,163,161,163,163,161],0.0557,[182,161,177,243,163,161,163,163,161],0.0874,[182,165,177,245,163,161,163,163,161],0.0567,[161,161,182,247,163,161,163,163,161],0.0606,[161,165,182,249,163,161,163,163,161],0.0558,[165,161,182,251,163,161,163,163,161],0.1014,[165,165,182,253,163,161,163,163,161],0.0672,[172,161,182,255,163,161,163,163,161],0.0838,[172,165,182,257,163,161,163,163,161],0.0572,[177,161,182,259,163,161,163,163,161],0.0725,[177,165,182,201,163,161,163,163,161],[182,161,182,262,163,161,163,163,161],0.0318,[182,165,182,264,163,161,163,163,161],0.0496,[161,161,266,267,163,161,163,163,161],5,0.0666,[161,165,266,269,163,161,163,163,161],0.0614,[165,161,266,271,163,161,163,163,161],0.1016,[165,165,266,273,163,161,163,163,161],0.0548,[172,161,266,275,163,161,163,163,161],0.0868,[172,165,266,277,163,161,163,163,161],0.0551,[177,161,266,279,163,161,163,163,161],0.1024,[177,165,266,281,163,161,163,163,161],0.0533,[182,161,266,283,163,161,163,163,161],0.0435,[182,165,266,285,163,161,163,163,161],0.053,[],[121],[],[],[291],"Handheld VLP-16 + MTi-100 sequences with motion-capture ground truth; trajectories aligned with Umeyama's method (scale handling not stated); motion from fast to slow",{"slug":293,"group":294,"sourceId":295,"sourceLabel":296,"table":297,"selfRows":298,"metrics":299,"seqs":304,"entrants":345,"cells":360,"outcomes":527,"locators":529,"hardware":530,"wordings":531,"notes":532},"jiao2022fusionportable-table-iv","jiao2022fusionportable:Table IV","jiao2022fusionportable","Jiao et al., 2022","Table IV",17,[300],{"label":301,"unit":302,"statistic":303,"alignment":18},"mean absolute trajectory error (ATE) w.r.t. ground truth","m (not printed in Table IV; trajectories plotted in metres in Fig. 5)","mean",[305,309,311,314,316,318,320,322,325,327,329,332,334,336,338,340,342],{"dataset":306,"sequence":307,"environment":308},"FusionPortable","canteen night","handheld (Table III platform; gimbal use per sequence not stated); indoors (Table III)",{"dataset":306,"sequence":310,"environment":308},"canteen day",{"dataset":306,"sequence":312,"environment":313},"garden night","handheld (Table III platform; gimbal use per sequence not stated); indoors as listed in Table III",{"dataset":306,"sequence":315,"environment":313},"garden day",{"dataset":306,"sequence":317,"environment":308},"corridor day",{"dataset":306,"sequence":319,"environment":308},"escalator day",{"dataset":306,"sequence":321,"environment":308},"building day",{"dataset":306,"sequence":323,"environment":324},"MCR slow","handheld (Table III platform; gimbal use per sequence not stated); indoors, motion capture room (Table III); motion 6-DoF, jerky (Table III)",{"dataset":306,"sequence":326,"environment":324},"MCR normal",{"dataset":306,"sequence":328,"environment":324},"MCR fast",{"dataset":306,"sequence":330,"environment":331},"MCR slow 00","quadruped robot; indoors, motion capture room (Table III)",{"dataset":306,"sequence":333,"environment":331},"MCR slow 01",{"dataset":306,"sequence":335,"environment":331},"MCR normal 00",{"dataset":306,"sequence":337,"environment":331},"MCR normal 01",{"dataset":306,"sequence":339,"environment":331},"MCR fast 00",{"dataset":306,"sequence":341,"environment":331},"MCR fast 01",{"dataset":306,"sequence":343,"environment":344},"campus road day","Apollo autonomous vehicle; outdoors (Table III)",[346,349,352,354,357],{"name":347,"methodId":348,"linkable":150,"proposed":77,"self":77},"VINS-Fusion (LC)","vinsfusion2019",{"name":350,"methodId":351,"linkable":150,"proposed":77,"self":77},"A-LOAM","aloam_software",{"name":353,"methodId":5,"linkable":150,"proposed":77,"self":150},"LIO-Mapping",{"name":355,"methodId":356,"linkable":150,"proposed":77,"self":77},"LIO-SAM","liosam2020",{"name":358,"methodId":359,"linkable":150,"proposed":77,"self":77},"FAST-LIO2","fastlio2_2022",[361,363,365,367,369,371,373,374,376,377,378,380,382,384,386,388,390,392,393,395,397,399,401,403,405,407,409,411,413,415,417,420,422,424,426,428,430,432,434,435,437,440,441,443,445,446,448,449,451,453,454,457,459,461,463,465,468,470,472,473,475,478,480,482,483,485,488,490,492,494,495,498,500,502,503,505,508,510,512,514,516,519,521,523,525],[161,161,161,362,163,161,163,163,161],0.409,[165,161,161,364,163,161,163,163,161],0.067,[172,161,161,366,163,161,163,163,161],0.097,[177,161,161,368,163,161,163,163,161],0.063,[182,161,161,370,163,161,163,163,161],0.071,[161,161,165,372,163,161,163,163,161],0.691,[165,161,165,221,163,161,163,163,161],[172,161,165,375,163,161,163,163,161],0.088,[177,161,165,285,163,161,163,163,161],[182,161,165,221,163,161,163,163,161],[161,161,172,379,163,161,163,163,161],0.328,[165,161,172,381,163,161,163,163,161],0.567,[172,161,172,383,163,161,163,163,161],0.242,[177,161,172,385,163,161,163,163,161],0.254,[182,161,172,387,163,161,163,163,161],0.205,[161,161,177,389,163,161,163,163,161],0.518,[165,161,177,391,163,161,163,163,161],0.528,[172,161,177,366,163,161,163,163,161],[177,161,177,394,163,161,163,163,161],0.069,[182,161,177,396,163,161,163,163,161],0.068,[161,161,182,398,163,161,163,163,161],1.807,[165,161,182,400,163,161,163,163,161],0.416,[172,161,182,402,163,161,163,163,161],1.755,[177,161,182,404,163,161,163,163,161],0.594,[182,161,182,406,163,161,163,163,161],1.563,[161,161,266,408,163,161,163,163,161],2.127,[165,161,266,410,163,161,163,163,161],0.981,[172,161,266,412,163,161,163,163,161],0.346,[177,161,266,414,163,161,163,163,161],0.207,[182,161,266,416,163,161,163,163,161],4.193,[161,161,418,419,163,161,163,163,161],6,12.861,[165,161,418,421,163,161,163,163,161],1.58,[172,161,418,423,163,161,163,163,161],0.916,[177,161,418,425,163,161,163,163,161],0.222,[182,161,418,427,163,161,163,163,161],0.146,[161,161,429,86,161,161,163,163,161],7,[165,161,429,431,163,161,163,163,161],0.087,[172,161,429,433,163,161,163,163,161],0.042,[177,161,429,368,163,161,163,163,161],[182,161,429,436,163,161,163,163,161],0.114,[161,161,438,439,163,161,163,163,161],8,0.168,[165,161,438,379,163,161,163,163,161],[172,161,438,442,163,161,163,163,161],0.052,[177,161,438,444,163,161,163,163,161],0.082,[182,161,438,195,163,161,163,163,161],[161,161,447,86,161,161,163,163,161],9,[165,161,447,400,163,161,163,163,161],[172,161,447,450,163,161,163,163,161],0.099,[177,161,447,452,163,161,163,163,161],0.117,[182,161,447,86,161,161,163,163,161],[161,161,455,456,163,161,163,163,161],10,0.096,[165,161,455,458,163,161,163,163,161],0.12,[172,161,455,460,163,161,163,163,161],0.032,[177,161,455,462,163,161,163,163,161],0.023,[182,161,455,464,163,161,163,163,161],0.047,[161,161,466,467,163,161,163,163,161],11,0.081,[165,161,466,469,163,161,163,163,161],0.054,[172,161,466,471,163,161,163,163,161],0.03,[177,161,466,471,163,161,163,163,161],[182,161,466,474,163,161,163,163,161],0.051,[161,161,476,477,163,161,163,163,161],12,0.094,[165,161,476,479,163,161,163,163,161],0.492,[172,161,476,481,163,161,163,163,161],0.093,[177,161,476,433,163,161,163,163,161],[182,161,476,484,163,161,163,163,161],0.127,[161,161,486,487,163,161,163,163,161],13,0.086,[165,161,486,489,163,161,163,163,161],0.635,[172,161,486,491,163,161,163,163,161],0.39,[177,161,486,493,163,161,163,163,161],0.04,[182,161,486,396,163,161,163,163,161],[161,161,496,497,163,161,163,163,161],14,0.264,[165,161,496,499,163,161,163,163,161],4.601,[172,161,496,501,163,161,163,163,161],2.405,[177,161,496,442,163,161,163,163,161],[182,161,496,504,163,161,163,163,161],0.408,[161,161,506,507,163,161,163,163,161],15,0.13,[165,161,506,509,163,161,163,163,161],8.264,[172,161,506,511,163,161,163,163,161],2.21,[177,161,506,513,163,161,163,163,161],0.066,[182,161,506,515,163,161,163,163,161],1.495,[161,161,517,518,163,161,163,163,161],16,77.528,[165,161,517,520,163,161,163,163,161],5.707,[172,161,517,522,163,161,163,163,161],4.122,[177,161,517,524,163,161,163,163,161],7.364,[182,161,517,526,163,161,163,163,161],4.08,[528],"failed",[297],[],[],[533],"Mean ATE of open-source SLAM systems on FusionPortable sequences; x = failed to finish; VINS-Fusion run with loop closure (LC); ESVO omitted because it could not finish the sequences; unit not printed in the table",{"slug":535,"group":536,"sourceId":537,"sourceLabel":538,"table":539,"selfRows":496,"metrics":540,"seqs":546,"entrants":563,"cells":576,"outcomes":728,"locators":729,"hardware":730,"wordings":731,"notes":732},"licfusion2-2020-table-vi","licfusion2_2020:Table VI","licfusion2_2020","Zuo et al., 2020","Table VI",[541,544],{"label":542,"unit":543,"statistic":18,"alignment":18},"averaged ATE, orientation (deg)","deg",{"label":545,"unit":126,"statistic":18,"alignment":18},"averaged ATE, position (m)",[547,551,553,555,557,559,561],{"dataset":548,"sequence":549,"environment":550},"Vicon Room sequences (authors' data)","Seq 1 (42.62 m)","indoor Vicon motion-capture room (cluttered)",{"dataset":548,"sequence":552,"environment":550},"Seq 2 (84.16 m)",{"dataset":548,"sequence":554,"environment":550},"Seq 3 (33.92 m)",{"dataset":548,"sequence":556,"environment":550},"Seq 4 (53.14 m)",{"dataset":548,"sequence":558,"environment":550},"Seq 5 (49.74 m)",{"dataset":548,"sequence":560,"environment":550},"Seq 6 (87.87 m)",{"dataset":548,"sequence":562,"environment":550},"Average of Seq 1-6",[564,566,568,570,571,573],{"name":565,"methodId":537,"linkable":150,"proposed":150,"self":77},"LIC-Fusion 2.0",{"name":567,"methodId":86,"linkable":77,"proposed":77,"self":77},"OpenVINS-IC",{"name":569,"methodId":86,"linkable":77,"proposed":77,"self":77},"Proposed-LI",{"name":148,"methodId":149,"linkable":150,"proposed":77,"self":77},{"name":572,"methodId":5,"linkable":150,"proposed":77,"self":150},"LIO-MAP",{"name":574,"methodId":575,"linkable":150,"proposed":77,"self":77},"LIC-Fusion","licfusion2019",[577,579,580,582,584,586,587,589,591,593,595,597,599,601,602,604,605,607,609,611,613,615,617,619,621,623,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,685,686,688,690,691,692,693,694,696,698,700,702,704,705,707,708,710,712,714,715,716,717,719,721,723,725,727],[161,161,161,578,163,161,163,163,161],2.537,[161,165,161,366,163,161,163,163,161],[161,161,165,581,163,161,163,163,161],1.87,[161,165,165,583,163,161,163,163,161],0.145,[161,161,172,585,163,161,163,163,161],1.94,[161,165,172,227,163,161,163,163,161],[161,161,177,588,163,161,163,163,161],2.081,[161,165,177,590,163,161,163,163,161],0.116,[161,161,182,592,163,161,163,163,161],2.71,[161,165,182,594,163,161,163,163,161],0.104,[161,161,266,596,163,161,163,163,161],3.32,[161,165,266,598,163,161,163,163,161],0.113,[161,161,418,600,163,161,163,163,161],2.41,[161,165,418,598,163,161,163,163,161],[165,161,161,603,163,161,163,163,161],2.625,[165,165,161,477,163,161,163,163,161],[165,161,165,606,163,161,163,163,161],1.741,[165,165,165,608,163,161,163,163,161],0.177,[165,161,172,610,163,161,163,163,161],3.131,[165,165,172,612,163,161,163,163,161],0.273,[165,161,177,614,163,161,163,163,161],2.404,[165,165,177,616,163,161,163,163,161],0.115,[165,161,182,618,163,161,163,163,161],2.962,[165,165,182,620,163,161,163,163,161],0.129,[165,161,266,622,163,161,163,163,161],3.953,[165,165,266,620,163,161,163,163,161],[165,161,418,625,163,161,163,163,161],2.803,[165,165,418,627,163,161,163,163,161],0.153,[172,161,161,629,163,161,163,163,161],2.333,[172,165,161,631,163,161,163,163,161],0.199,[172,161,165,633,163,161,163,163,161],3.325,[172,165,165,635,163,161,163,163,161],0.444,[172,161,172,637,163,161,163,163,161],2.81,[172,165,172,639,163,161,163,163,161],0.306,[172,161,177,641,163,161,163,163,161],5.335,[172,165,177,643,163,161,163,163,161],0.272,[172,161,182,645,163,161,163,163,161],3.332,[172,165,182,647,163,161,163,163,161],0.44,[172,161,266,649,163,161,163,163,161],4.866,[172,165,266,651,163,161,163,163,161],0.412,[172,161,418,653,163,161,163,163,161],3.667,[172,165,418,655,163,161,163,163,161],0.345,[177,161,161,657,163,161,163,163,161],5.88,[177,165,161,659,163,161,163,163,161],0.156,[177,161,165,661,163,161,163,163,161],6.414,[177,165,165,663,163,161,163,163,161],0.134,[177,161,172,665,163,161,163,163,161],15.384,[177,165,172,667,163,161,163,163,161],0.333,[177,161,177,669,163,161,163,163,161],6.354,[177,165,177,671,163,161,163,163,161],0.15,[177,161,182,673,163,161,163,163,161],5.542,[177,165,182,675,163,161,163,163,161],0.14,[177,161,266,677,163,161,163,163,161],7.095,[177,165,266,679,163,161,163,163,161],0.188,[177,161,418,681,163,161,163,163,161],7.778,[177,165,418,683,163,161,163,163,161],0.183,[182,161,161,86,161,161,163,163,161],[182,165,161,86,161,161,163,163,161],[182,161,165,687,163,161,163,163,161],5.608,[182,165,165,689,163,161,163,163,161],0.214,[182,161,172,86,161,161,163,163,161],[182,165,172,86,161,161,163,163,161],[182,161,177,86,161,161,163,163,161],[182,165,177,86,161,161,163,163,161],[182,161,182,695,163,161,163,163,161],4.89,[182,165,182,697,163,161,163,163,161],0.17,[182,161,266,699,163,161,163,163,161],12.862,[182,165,266,701,163,161,163,163,161],0.238,[182,161,418,703,163,161,163,163,161],7.786,[182,165,418,414,163,161,163,163,161],[266,161,161,706,163,161,163,163,161],2.345,[266,165,161,366,163,161,163,163,161],[266,161,165,709,163,161,163,163,161],1.879,[266,165,165,711,163,161,163,163,161],0.173,[266,161,172,713,163,161,163,163,161],1.973,[266,165,172,594,163,161,163,163,161],[266,161,177,86,161,161,163,163,161],[266,165,177,86,161,161,163,163,161],[266,161,182,718,163,161,163,163,161],2.743,[266,165,182,720,163,161,163,163,161],0.1,[266,161,266,722,163,161,163,163,161],3.788,[266,165,266,724,163,161,163,163,161],0.131,[266,161,418,726,163,161,163,163,161],2.546,[266,165,418,195,163,161,163,163,161],[528],[539],[],[],[733],"Averaged 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":735,"group":736,"sourceId":737,"sourceLabel":738,"table":739,"selfRows":496,"metrics":740,"seqs":756,"entrants":766,"cells":783,"outcomes":980,"locators":984,"hardware":985,"wordings":987,"notes":988},"locus2021-table-ii","locus2021:Table II","locus2021","Palieri et al., 2021","Table II",[741,744,746,749,752,755],{"label":742,"unit":126,"statistic":743,"alignment":18},"APE max","max",{"label":745,"unit":126,"statistic":303,"alignment":18},"APE mean",{"label":747,"unit":126,"statistic":748,"alignment":18},"APE std","std",{"label":750,"unit":126,"statistic":127,"alignment":751},"ME (map error) RMSE","SE3",{"label":753,"unit":754,"statistic":743,"alignment":18},"CPU load (number of cores)","cores",{"label":753,"unit":754,"statistic":303,"alignment":18},[757,761,763],{"dataset":758,"sequence":759,"environment":760},"DARPA SubT Husky datasets (CoSTAR)","Urban Alpha course","decommissioned power plant, Satsop (Elma, WA): long feature-poor corridors and large open spaces",{"dataset":758,"sequence":762,"environment":760},"Urban Beta course",{"dataset":758,"sequence":764,"environment":765},"Tunnel Safety Research course","Bruceton Research Mine, Pittsburgh: self-similar and self-repetitive tunnels",[767,769,771,773,775,778,781,782],{"name":768,"methodId":737,"linkable":150,"proposed":150,"self":77},"LOCUS",{"name":770,"methodId":737,"linkable":150,"proposed":150,"self":77},"LOCUS FGA",{"name":772,"methodId":86,"linkable":77,"proposed":77,"self":77},"BLAM",{"name":774,"methodId":351,"linkable":150,"proposed":77,"self":77},"ALOAM",{"name":776,"methodId":777,"linkable":150,"proposed":77,"self":77},"FLOAM","floam2021",{"name":779,"methodId":780,"linkable":150,"proposed":77,"self":77},"Cartographer","cartographer2016",{"name":353,"methodId":5,"linkable":150,"proposed":77,"self":150},{"name":355,"methodId":356,"linkable":150,"proposed":77,"self":77},[784,786,788,790,792,794,796,798,800,802,804,806,808,809,811,812,814,816,818,820,822,823,825,826,827,828,829,830,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,867,869,871,872,874,876,878,880,882,884,886,888,890,892,894,896,898,900,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,932,934,936,938,940,941,943,944,945,947,948,950,952,954,956,958,960,961,963,965,966,967,968,969,970,971,972,973,975,977,978],[161,161,161,785,163,161,163,163,161],1.69,[161,165,161,787,163,161,163,163,161],0.62,[161,172,161,789,163,161,163,163,161],0.57,[161,177,161,791,163,161,163,163,161],0.29,[161,161,165,793,163,161,163,163,161],1.51,[161,165,165,795,163,161,163,163,161],0.88,[161,172,165,797,163,161,163,163,161],0.51,[161,177,165,799,163,161,163,163,161],0.69,[161,161,172,801,163,161,163,163,161],3.39,[161,165,172,803,163,161,163,163,161],1.67,[161,172,172,805,163,161,163,163,161],0.76,[161,177,172,807,163,161,163,163,161],0.63,[161,182,165,801,163,161,161,163,161],[161,266,165,810,163,161,161,163,161],2.72,[165,161,161,807,163,161,163,163,161],[165,165,161,813,163,161,163,163,161],0.26,[165,172,161,815,163,161,163,163,161],0.18,[165,177,161,817,163,161,163,163,161],0.28,[165,161,165,819,163,161,163,163,161],1.2,[165,165,165,821,163,161,163,163,161],0.58,[165,172,165,491,163,161,163,163,161],[165,177,165,824,163,161,163,163,161],0.48,[165,161,172,86,161,161,163,163,161],[165,165,172,86,161,161,163,163,161],[165,172,172,86,161,161,163,163,161],[165,177,172,86,161,161,163,163,161],[165,182,165,801,163,161,161,163,161],[165,266,165,810,163,161,161,163,161],[172,161,161,832,163,161,163,163,161],3.44,[172,165,161,834,163,161,163,163,161],1.01,[172,172,161,836,163,161,163,163,161],0.94,[172,177,161,838,163,161,163,163,161],0.43,[172,161,165,840,163,161,163,163,161],3.89,[172,165,165,842,163,161,163,163,161],2.27,[172,172,165,844,163,161,163,163,161],0.89,[172,177,165,846,163,161,163,163,161],1.27,[172,161,172,848,163,161,163,163,161],171.34,[172,165,172,850,163,161,163,163,161],35.45,[172,172,172,852,163,161,163,163,161],51.91,[172,177,172,854,163,161,163,163,161],5.37,[172,182,165,856,163,161,161,163,161],1.14,[172,266,165,858,163,161,161,163,161],0.93,[177,161,161,860,163,161,163,163,161],4.33,[177,165,161,862,163,161,163,163,161],1.38,[177,172,161,864,163,161,163,163,161],1.19,[177,177,161,866,163,161,163,163,161],0.6,[177,161,165,868,163,161,163,163,161],2.58,[177,165,165,870,163,161,163,163,161],2.11,[177,172,165,647,163,161,163,163,161],[177,177,165,873,163,161,163,163,161],0.99,[177,161,172,875,163,161,163,163,161],18.61,[177,165,172,877,163,161,163,163,161],10.01,[177,172,172,879,163,161,163,163,161],6.01,[177,177,172,881,163,161,163,163,161],6.11,[177,182,165,883,163,161,161,163,161],1.65,[177,266,165,885,163,161,161,163,161],1.41,[182,161,161,887,163,161,163,163,161],29.49,[182,165,161,889,163,161,163,163,161],9.19,[182,172,161,891,163,161,163,163,161],8.96,[182,177,161,893,165,161,163,163,161],1.73,[182,161,165,895,163,161,163,163,161],40.64,[182,165,165,897,163,161,163,163,161],3.94,[182,172,165,899,163,161,163,163,161],8.42,[182,177,165,901,165,161,163,163,161],3.73,[182,161,172,903,163,161,163,163,161],85.31,[182,165,172,905,163,161,163,163,161],32.49,[182,172,172,907,163,161,163,163,161],25.73,[182,177,172,909,163,161,163,163,161],20.16,[182,182,165,911,163,161,161,163,161],1.76,[182,266,165,913,163,161,161,163,161],1.44,[266,161,161,915,163,161,163,163,161],5.84,[266,165,161,917,163,161,163,163,161],2.91,[266,172,161,919,163,161,163,163,161],1.6,[266,177,161,921,163,161,163,163,161],1.05,[266,161,165,923,163,161,163,163,161],2.64,[266,165,165,925,163,161,163,163,161],1.37,[266,172,165,927,163,161,163,163,161],0.67,[266,177,165,929,163,161,163,163,161],0.31,[266,161,172,931,163,161,163,163,161],50.05,[266,165,172,933,163,161,163,163,161],14.31,[266,172,172,935,163,161,163,163,161],13.45,[266,177,172,937,163,161,163,163,161],14.25,[266,182,165,939,163,161,161,163,161],1.75,[266,266,165,795,163,161,161,163,161],[418,161,161,942,163,161,163,163,161],2.12,[418,165,161,873,163,161,163,163,161],[418,172,161,797,163,161,163,163,161],[418,177,161,946,163,161,163,163,161],0.45,[418,161,165,919,163,161,163,163,161],[418,165,165,949,163,161,163,163,161],1.18,[418,172,165,951,163,161,163,163,161],0.22,[418,177,165,953,163,161,163,163,161],0.61,[418,161,172,955,163,161,163,163,161],3.31,[418,165,172,957,163,161,163,163,161],1.99,[418,172,172,959,163,161,163,163,161],0.55,[418,177,172,805,163,161,163,163,161],[418,182,165,962,163,161,161,163,161],1.8,[418,266,165,964,163,161,161,163,161],1.53,[429,161,161,86,172,161,163,163,161],[429,165,161,86,172,161,163,163,161],[429,172,161,86,172,161,163,163,161],[429,177,161,86,172,161,163,163,161],[429,161,165,86,172,161,163,163,161],[429,165,165,86,172,161,163,163,161],[429,172,165,86,172,161,163,163,161],[429,177,165,86,172,161,163,163,161],[429,161,172,974,163,161,163,163,161],2.45,[429,165,172,976,163,161,163,163,161],1.26,[429,172,172,821,163,161,163,163,161],[429,177,172,979,163,161,163,163,161],0.52,[981,982,983],"not_run (FGA variant not reported on the Tunnel dataset)","value marked * in Table II: failure leads to a low map error","failed (authors could not get LIO-SAM working on the Urban datasets, likely because the 50 Hz IMU rate is below the recommended 200 Hz)",[739],[986],"Intel Hades Canyon NUC8i7HVKVA (4 x 1.9 GHz, 32 GB RAM, Ubuntu 18.04)",[],[989],"Husky field datasets from the SubT Urban (Alpha, Beta courses at the Satsop power plant) and Tunnel (Safety Research course, Bruceton mine) circuits; APE via evo against a reference from LOCUS scan matching on the DARPA ground-truth map; ME = RMSE of cloud-to-cloud error after ICP alignment of the map to the DARPA ground-truth map; loop closures disabled; FLOAM and LIO-Mapping ran with one LiDAR in Urban Alpha, LIO-SAM with one LiDAR; CPU loads from Urban Beta (LIO-SAM from Tunnel)",[991,997,1004,1010,1016,1022,1031,1036,1043,1047,1053,1059,1064,1068,1075,1079,1083,1090,1094],{"group":992,"slug":993,"sourceLabel":994,"table":739,"selfRows":476,"datasets":995},"clins2021:Table II","clins2021-table-ii","Lv et al., 2021",[996],"LIOM dataset (Ye et al., ICRA 2019)",{"group":998,"slug":999,"sourceLabel":1000,"table":1001,"selfRows":455,"datasets":1002},"dliom2023:Table III","dliom2023-table-iii","Wang et al., 2023b","Table III",[1003],"NTU VIRAL",{"group":1005,"slug":1006,"sourceLabel":1007,"table":1001,"selfRows":455,"datasets":1008},"dlo2022:Table III","dlo2022-table-iii","Chen et al., 2022a",[1009],"DARPA SubT Urban Circuit (Alpha and Beta courses)",{"group":1011,"slug":1012,"sourceLabel":1013,"table":121,"selfRows":455,"datasets":1014},"lins2020:Table I","lins2020-table-i","Qin et al., 2020",[1015],"Own LINS datasets",{"group":1017,"slug":1018,"sourceLabel":538,"table":1019,"selfRows":429,"datasets":1020},"licfusion2_2020:Table V","licfusion2-2020-table-v","Table V",[1021],"Teaching Building sequences (authors' data)",{"group":1023,"slug":1024,"sourceLabel":1025,"table":1026,"selfRows":418,"datasets":1027},"liliom2021:Table 1","liliom2021-table-1","Li et al., 2021b","Table 1",[1028,1029,1030],"UTBM (EU long-term)","UrbanLoco","UrbanNav",{"group":1032,"slug":1033,"sourceLabel":6,"table":739,"selfRows":418,"datasets":1034},"liomapping2019:Table II","liomapping2019-table-ii",[1035],"Own sequences (16-line lidar)",{"group":1037,"slug":1038,"sourceLabel":1039,"table":739,"selfRows":418,"datasets":1040},"lvisam2021:Table II","lvisam2021-table-ii","Shan et al., 2021",[1041,1042],"Handheld (authors' data)","Jackal (authors' data)",{"group":1044,"slug":1045,"sourceLabel":1013,"table":739,"selfRows":266,"datasets":1046},"lins2020:Table II","lins2020-table-ii",[1015],{"group":1048,"slug":1049,"sourceLabel":1050,"table":297,"selfRows":266,"datasets":1051},"liosam2020:Table IV","liosam2020-table-iv","Shan et al., 2020",[1052],"Own LIO-SAM datasets",{"group":1054,"slug":1055,"sourceLabel":994,"table":297,"selfRows":182,"datasets":1056},"clins2021:Table IV","clins2021-table-iv",[1057,1058],"KAIST Urban (Complex Urban dataset)","YQ (authors' campus sequences)",{"group":1060,"slug":1061,"sourceLabel":1000,"table":297,"selfRows":182,"datasets":1062},"dliom2023:Table IV","dliom2023-table-iv",[1063],"TONGJI dataset",{"group":1065,"slug":1066,"sourceLabel":1000,"table":1019,"selfRows":182,"datasets":1067},"dliom2023:Table V","dliom2023-table-v",[18],{"group":1069,"slug":1070,"sourceLabel":1071,"table":1072,"selfRows":182,"datasets":1073},"yan2026tunnel:Table 2","yan2026tunnel-table-2","Yan et al., 2026a","Table 2",[1074],"WHU-Helmet (WHUH)",{"group":1076,"slug":1077,"sourceLabel":1050,"table":739,"selfRows":177,"datasets":1078},"liosam2020:Table II","liosam2020-table-ii",[1052],{"group":1080,"slug":1081,"sourceLabel":738,"table":1001,"selfRows":177,"datasets":1082},"locus2021:Table III","locus2021-table-iii",[758],{"group":1084,"slug":1085,"sourceLabel":1086,"table":1087,"selfRows":165,"datasets":1088},"ghadimzadeh2025slamnde:Table 3","ghadimzadeh2025slamnde-table-3","Ghadimzadeh Alamdari et al., 2025","Table 3",[1089],"Luleå SubT tunnel dataset (Koval et al. 2022)",{"group":1091,"slug":1092,"sourceLabel":1050,"table":1001,"selfRows":165,"datasets":1093},"liosam2020:Table III","liosam2020-table-iii",[1052],{"group":1095,"slug":1096,"sourceLabel":1071,"table":1097,"selfRows":165,"datasets":1098},"yan2026tunnel:Table 4","yan2026tunnel-table-4","Table 4",[1074],1790510658614]