[{"data":1,"prerenderedAt":1065},["ShallowReactive",2],{"method-lins2020":3},{"method":4,"reference":58,"equipment":81,"figures":120,"results":121},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":22,"limitations":26,"sensors":32,"platform":35,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"lins2020","Qin et al., 2020","LINS","LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation",2020,"recent","C04","odometry_with_local_mapping","LINS 以機器人中心（robocentric）表述的迭代誤差狀態卡爾曼濾波器（iterated ESKF）緊耦合 6 軸 IMU 與 3D LiDAR：每次迭代都重新尋找點到邊、點到面的特徵對應，以降低錯誤匹配造成的線性化誤差。狀態以上一時刻的局部座標表示，再組合成全域位姿，以避免長時間運作時不確定性增長導致濾波發散。論文聚焦於里程計模組，建圖直接沿用 LeGO-LOAM 的建圖演算法。","LINS tightly couples a 6-axis IMU and a 3D lidar through a robocentric iterated ESKF that re-associates edge\u002Fplane features at each iteration, and reuses LeGO-LOAM mapping.","full_text_reviewed","peer_reviewed_published","background","not_reported",[20,21],"cross_site","independent_reference",[23,24,25],"About an order of magnitude faster than LIOM for the LIO module (18-25 ms vs 143-223 ms per scan, Table II)","Map-refined odometry drift 1.32-3.31% in four outdoor scenes (city, port, industrial park, forest) measured against GPS, and 1.08% in the indoor parking lot where no ground truth was available (Table I, Sec. IV-A)","Robust in feature-poor scenes (about 30 edge features per scan in the port's first turn) (Sec. IV-B1)",[27,28,29,30,31],"Only the previous scan is used for scan-to-scan matching, a sparser model than LIOM's local map (Sec. IV-C)","No ground truth in the indoor test; evaluated visually (Sec. IV-A)","Map-refined drift slightly higher than LIOM in the port (1.56% vs 1.40%) and city (1.79% vs 1.76%) tests, attributed by the authors to LIOM's rotation-constrained refinement (Sec. IV-B, Table I)","Paper covers only the odometry module; feature extraction and mapping are taken from LOAM and LeGO-LOAM (Sec. III-A, IV)","Follow-up work states the robocentric ESKF estimator drifts during long-duration navigation without other sensors (liosam2020, Sec. II, ref [16])",[33,34],"3D LiDAR (Velodyne VLP-16 on a car in the port test; RS-LiDAR-16 on a bus in the indoor parking lot and urban tests)","6-axis IMU (Xsens MTi-G-710 in the port test; the IMU placed inside the bus is not specified)",[36,37],"vehicle (car)","bus","robocentric iterated error-state Kalman filter (iterated ESKF), re-finding feature correspondences at each iteration (Sec. III-C)","LOAM\u002FLeGO-style edge and planar features; point-to-edge and point-to-plane residuals against the previous scan only (Sec. III-B, III-C, IV-C)","discrete lidar time-steps with IMU propagation (Sec. III-C)","raw features undistorted with the relative transformation estimated after the iterated update (Sec. III-C3)","none","none; map-refined odometry uses the LeGO-LOAM mapping module (Sec. IV)","global feature map from the LeGO-LOAM mapping algorithm (Sec. III-A, IV)","none (offline-calibrated extrinsics and accelerometer bias)","global 3D map (1 Hz) and fused odometry (400 Hz) (Fig. 2)","LIO module mean 18 to 25 ms per scan vs 143 to 223 ms for LIOM on a laptop with 2.4 GHz quad cores and 8 GiB memory (ROS, Ubuntu); global map output at 1 Hz and fused odometry at 400 Hz","https:\u002F\u002Fgithub.com\u002FChaoqinRobotics\u002FLINS---LiDAR-inertial-SLAM","not_verified",[51,55],{"relation":52,"title":53,"doi_or_url":54},"preprint","arXiv 1907.02233: v1 (2019-07-04) 'LINS: A Lidar-Inerital [sic] State Estimator for Robust and Fast Navigation'; v2 (2019-08-22) 'R-LINS: A Robocentric Lidar-Inertial State Estimator for Robust and Efficient Navigation' (cited as ref [16] by LIO-SAM); v3 (2020-05-06) final title","https:\u002F\u002Farxiv.org\u002Fabs\u002F1907.02233",{"relation":56,"title":57,"doi_or_url":48},"code_release","ChaoqinRobotics\u002FLINS---LiDAR-inertial-SLAM",{"id":5,"kind":59,"shortName":7,"title":8,"authors":60,"year":9,"venue":67,"venueType":68,"publisher":69,"volumeIssuePages":70,"doi":71,"arxivId":72,"url":54,"firstPublicDate":73,"publicationStatus":16,"metadataStatus":74,"fulltextStatus":15,"era":10,"classicReason":75,"codeUrl":48,"cluster":11,"topics":76,"mdpi":77,"verification":78,"label":6,"fulltextRoute":79,"versionRead":80,"addedByCensus":77},"method",[61,62,63,64,65,66],"Chao Qin","Haoyang Ye","Christian E. Pranata","Jun Han","Shuyang Zhang","Ming Liu","2020 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 8899-8906","10.1109\u002Ficra40945.2020.9197567","1907.02233","2019-07-04","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv 1907.02233v3 (2020-05-06), title matching the ICRA 2020 paper; not compared with the IEEE Xplore version of record",[82,89,93,97,100,106,111,114],{"category":83,"model":84,"canonical":84,"role":85,"dataset":86,"specs":87,"locator":88},"lidar","Velodyne VLP-16","method input",null,"fixed on top of a car with the IMU (port test)","Sec. IV-B1; Fig. 1",{"category":90,"model":91,"canonical":91,"role":85,"dataset":86,"specs":92,"locator":88},"imu","Xsens MTi-G-710","used as a 6-axis IMU; fixed on top of a car (port test)",{"category":83,"model":94,"canonical":94,"role":85,"dataset":86,"specs":95,"locator":96},"RS-LiDAR-16","mounted on top of a bus (indoor parking lot and urban tests)","Sec. IV-A; Sec. IV-B2; Fig. 4",{"category":90,"model":98,"canonical":98,"role":85,"dataset":86,"specs":99,"locator":96},"IMU (model not reported)","placed inside the bus (Fig. 4 caption: an IMU is stuck to the bus); used with the roof-mounted RS-LiDAR-16 in the indoor parking lot and urban tests",{"category":101,"model":102,"canonical":102,"role":103,"dataset":86,"specs":104,"locator":105},"gnss","GPS receiver","reference or ground truth","provides ground-truth positions in outdoor tests","Sec. IV-B",{"category":107,"model":108,"canonical":108,"role":85,"dataset":86,"specs":109,"locator":110},"platform","car","port test in Guangdong","Sec. IV-B1",{"category":107,"model":37,"canonical":37,"role":85,"dataset":86,"specs":112,"locator":113},"LiDAR on the roof, IMU inside the bus","Sec. IV-A; Fig. 4",{"category":115,"model":116,"canonical":116,"role":117,"dataset":86,"specs":118,"locator":119},"compute","laptop, 2.4 GHz quad cores","compute for runtime","8 GiB memory; ROS in Ubuntu","Sec. IV",[],{"totalRows":122,"groupCount":123,"groups":124,"others":986},130,16,[125,367,656,829],{"slug":126,"group":127,"sourceId":128,"sourceLabel":129,"table":130,"selfRows":131,"metrics":132,"seqs":149,"entrants":168,"cells":176,"outcomes":359,"locators":361,"hardware":362,"wordings":364,"notes":365},"locus2-2022-table-iii","locus2_2022:Table III","locus2_2022","Reinke et al., 2022","Table III",30,[133,137,141,144,146],{"label":134,"unit":135,"statistic":136,"alignment":18},"APE max [m]","m","max",{"label":138,"unit":139,"statistic":140,"alignment":18},"APE mean [%] (unit as printed)","% (as printed)","mean",{"label":142,"unit":143,"statistic":136,"alignment":75},"CPU [%] max (as printed)","% (100% = one core)",{"label":145,"unit":143,"statistic":140,"alignment":75},"CPU [%] mean (as printed)",{"label":147,"unit":148,"statistic":136,"alignment":75},"max memory [GB]","GB",[150,154,157,160,163,166],{"dataset":151,"sequence":152,"environment":153},"NeBula odometry dataset (DARPA SubT, Team CoSTAR)","A: power plant, Elma WA (urban), Husky, 631.53 m","feature-poor corridors, large open spaces",{"dataset":151,"sequence":155,"environment":156},"C: power plant, Elma WA (urban), Husky, 757.40 m","feature-poor corridors, large and narrow spaces",{"dataset":151,"sequence":158,"environment":159},"F: Bruceton Mine, Pittsburgh PA (tunnel), Husky, 1569.73 m","self-similar repetitive geometry",{"dataset":151,"sequence":161,"environment":162},"H: Subway Station, Los Angeles CA (urban), Spot, 1777.45 m","3-level, multiple stairs, feature-poor corridors",{"dataset":151,"sequence":164,"environment":165},"I: Kentucky Underground Limestone Mine KY (cave), Spot, 768.82 m","large area, degraded lighting",{"dataset":151,"sequence":167,"environment":165},"J: Kentucky Underground Limestone Mine KY (cave), Husky, 2339.81 m",[169,172,175],{"name":170,"methodId":128,"linkable":171,"proposed":171,"self":77},"LOCUS 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(authors state only LOCUS 2.0 does not fail in tunnel dataset F)",[130],[363],"not_reported (the computer used for Table III is not stated in the paper)",[],[366],"Underground datasets A, C, F, H, I, J (Table I); LOCUS 2.0 versus FAST-LIO and LINS; column labels reproduced as printed (APE max [m], APE mean [%], CPU [%] max and mean, max memory [GB]); many printed 'max' values are below 'mean' values; ground truth from LOCUS 1.0 against survey-grade maps",{"slug":368,"group":369,"sourceId":370,"sourceLabel":371,"table":372,"selfRows":373,"metrics":374,"seqs":379,"entrants":426,"cells":450,"outcomes":648,"locators":650,"hardware":652,"wordings":653,"notes":654},"voxelslam2026-table-2-odometry-without-lc","voxelslam2026:Table 2 (odometry without LC)","voxelslam2026","Liu et al., 2026","Table 2 (odometry without LC)",13,[375],{"label":376,"unit":377,"statistic":378,"alignment":18},"absolute trajectory error (RMSE, 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exp15-upper-gallery (name per Table C1)","hilti07","upper gallery",{"dataset":407,"sequence":408,"environment":409},"Hilti handheld sequence exp21-outside (name per Table C1)","hilti08","outside",{"dataset":411,"sequence":412,"environment":413},"Hilti handheld sequence site1-handheld-1 (name per Table C1)","hilti09","construction site (same site for hilti09 to hilti13, Sec. 10.3.1)",{"dataset":415,"sequence":416,"environment":413},"Hilti handheld sequence site1-handheld-2 (name per Table C1)","hilti10",{"dataset":418,"sequence":419,"environment":413},"Hilti handheld sequence site1-handheld-3 (name per Table C1)","hilti11",{"dataset":421,"sequence":422,"environment":413},"Hilti handheld sequence site1-handheld-4 (name per Table C1)","hilti12",{"dataset":424,"sequence":425,"environment":413},"Hilti handheld sequence site1-handheld-5 (name per Table C1)","hilti13",[427,430,433,434,437,440,443,446,448],{"name":428,"methodId":429,"linkable":171,"proposed":77,"self":77},"LeGO-LOAM","legoloam2018",{"name":431,"methodId":432,"linkable":171,"proposed":77,"self":77},"LiLi-OM","liliom2021",{"name":7,"methodId":5,"linkable":171,"proposed":77,"self":171},{"name":435,"methodId":436,"linkable":171,"proposed":77,"self":77},"LIO-SAM","liosam2020",{"name":438,"methodId":439,"linkable":171,"proposed":77,"self":77},"FAST-LIO2","fastlio2_2022",{"name":441,"methodId":442,"linkable":171,"proposed":77,"self":77},"Faster-LIO","fasterlio2022",{"name":444,"methodId":445,"linkable":171,"proposed":77,"self":77},"Point-LIO","pointlio2023",{"name":447,"methodId":370,"linkable":171,"proposed":171,"self":77},"Our (Odom)",{"name":449,"methodId":370,"linkable":171,"proposed":171,"self":77},"Our (Odom+LM)",[451,453,455,456,458,459,461,463,465,468,471,474,477,479,481,483,484,486,487,489,490,492,494,496,498,499,500,502,504,505,507,508,510,511,513,515,517,518,520,521,523,525,526,528,529,531,532,534,536,538,540,542,544,545,547,549,551,553,555,557,559,560,562,564,566,567,569,571,573,574,576,578,580,581,583,585,587,588,590,591,592,594,596,598,600,602,603,605,607,609,610,612,613,615,617,618,619,620,622,623,624,625,626,627,629,630,631,632,634,636,637,639,641,643,644,645,646],[178,178,178,452,180,178,180,180,178],9.1,[178,178,182,454,180,178,180,180,178],47,[178,178,185,86,178,178,180,180,178],[178,178,188,457,180,178,180,180,178],25.3,[178,178,191,86,178,178,180,180,178],[178,178,330,460,180,178,180,180,178],67,[178,178,462,86,178,178,180,180,178],6,[178,178,464,457,180,178,180,180,178],7,[178,178,466,467,180,178,180,180,178],8,12.7,[178,178,469,470,180,178,180,180,178],9,14.3,[178,178,472,473,180,178,180,180,178],10,27.1,[178,178,475,476,180,178,180,180,178],11,19.7,[178,178,478,86,178,178,180,180,178],12,[182,178,178,480,180,178,180,180,178],6.2,[182,178,182,482,180,178,180,180,178],22.2,[182,178,185,86,178,178,180,180,178],[182,178,188,485,180,178,180,180,178],31,[182,178,191,86,178,178,180,180,178],[182,178,330,488,180,178,180,180,178],28.9,[182,178,462,86,178,178,180,180,178],[182,178,464,491,180,178,180,180,178],20.3,[182,178,466,493,180,178,180,180,178],6.9,[182,178,469,495,180,178,180,180,178],8.5,[182,178,472,497,180,178,180,180,178],19.9,[182,178,475,457,180,178,180,180,178],[182,178,478,86,178,178,180,180,178],[185,178,178,501,180,178,180,180,178],6.5,[185,178,182,503,180,178,180,180,178],18.8,[185,178,185,86,178,178,180,180,178],[185,178,188,506,180,178,180,180,178],20.7,[185,178,191,86,178,178,180,180,178],[185,178,330,509,180,178,180,180,178],23.1,[185,178,462,86,178,178,180,180,178],[185,178,464,512,180,178,180,180,178],17.8,[185,178,466,514,180,178,180,180,178],7.5,[185,178,469,516,180,178,180,180,178],9.9,[185,178,472,267,180,178,180,180,178],[185,178,475,519,180,178,180,180,178],20,[185,178,478,86,178,178,180,180,178],[188,178,178,522,180,178,180,180,178],7.4,[188,178,182,524,180,178,180,180,178],15.2,[188,178,185,86,178,178,180,180,178],[188,178,188,527,180,178,180,180,178],23.4,[188,178,191,86,178,178,180,180,178],[188,178,330,530,180,178,180,180,178],17.4,[188,178,462,86,178,178,180,180,178],[188,178,464,533,180,178,180,180,178],22.4,[188,178,466,535,180,178,180,180,178],6.6,[188,178,469,537,180,178,180,180,178],6.8,[188,178,472,539,180,178,180,180,178],17.6,[188,178,475,541,180,178,180,180,178],16.8,[188,178,478,543,180,178,180,180,178],74,[191,178,178,222,180,178,180,180,178],[191,178,182,546,180,178,180,180,178],2.8,[191,178,185,548,180,178,180,180,178],32,[191,178,188,550,180,178,180,180,178],6.7,[191,178,191,552,180,178,180,180,178],55,[191,178,330,554,180,178,180,180,178],2.4,[191,178,462,556,180,178,180,180,178],72,[191,178,464,558,180,178,180,180,178],1.7,[191,178,466,554,180,178,180,180,178],[191,178,469,561,180,178,180,180,178],1.8,[191,178,472,563,180,178,180,180,178],4.2,[191,178,475,565,180,178,180,180,178],3.5,[191,178,478,123,180,178,180,180,178],[330,178,178,568,180,178,180,180,178],1.1,[330,178,182,570,180,178,180,180,178],2.1,[330,178,185,572,180,178,180,180,178],37,[330,178,188,330,180,178,180,180,178],[330,178,191,575,180,178,180,180,178],73,[330,178,330,577,180,178,180,180,178],1.4,[330,178,462,579,180,178,180,180,178],61,[330,178,464,554,180,178,180,180,178],[330,178,466,582,180,178,180,180,178],1.9,[330,178,469,584,180,178,180,180,178],2.3,[330,178,472,586,180,178,180,180,178],2.7,[330,178,475,342,180,178,180,180,178],[330,178,478,589,180,178,180,180,178],11.4,[462,178,178,568,180,178,180,180,178],[462,178,182,188,180,178,180,180,178],[462,178,185,593,180,178,180,180,178],23,[462,178,188,595,180,178,180,180,178],3.7,[462,178,191,597,180,178,180,180,178],44,[462,178,330,599,180,178,180,180,178],0.9,[462,178,462,601,180,178,180,180,178],45,[462,178,464,342,180,178,180,180,178],[462,178,466,604,180,178,180,180,178],3.2,[462,178,469,606,180,178,180,180,178],1.6,[462,178,472,608,180,178,180,180,178],3.6,[462,178,475,191,180,178,180,180,178],[462,178,478,611,180,178,180,180,178],9.2,[464,178,178,222,180,178,180,180,178],[464,178,182,614,180,178,180,180,178],2.5,[464,178,185,616,180,178,180,180,178],9.3,[464,178,188,563,180,178,180,180,178],[464,178,191,593,180,178,180,180,178],[464,178,330,606,180,178,180,180,178],[464,178,462,621,180,178,180,180,178],15.7,[464,178,464,561,180,178,180,180,178],[464,178,466,606,180,178,180,180,178],[464,178,469,185,180,178,180,180,178],[464,178,472,546,180,178,180,180,178],[464,178,475,554,180,178,180,180,178],[464,178,478,628,180,178,180,180,178],4.3,[466,178,178,319,180,178,180,180,178],[466,178,182,561,180,178,180,180,178],[466,178,185,188,180,178,180,180,178],[466,178,188,633,180,178,180,180,178],3.4,[466,178,191,635,180,178,180,180,178],15.9,[466,178,330,599,180,178,180,180,178],[466,178,462,638,180,178,180,180,178],9.8,[466,178,464,640,180,178,180,180,178],1.2,[466,178,466,642,180,178,180,180,178],1.25,[466,178,469,577,180,178,180,180,178],[466,178,472,554,180,178,180,180,178],[466,178,475,577,180,178,180,180,178],[466,178,478,647,180,178,180,180,178],1.26,[649],"failed (dash; text states LeGO-LOAM, LiLi-OM, LINS and LIO-SAM failed in these sequences)",[651],"Table 2",[],[],[655],"Hilti handheld sequences (Hesai XT-32, BMI085 400 Hz); ATE exported from the Hilti evaluation website; odometry without loop closure; all methods with default parameters",{"slug":657,"group":658,"sourceId":439,"sourceLabel":659,"table":660,"selfRows":478,"metrics":661,"seqs":664,"entrants":697,"cells":706,"outcomes":821,"locators":824,"hardware":825,"wordings":826,"notes":827},"fastlio2-2022-table-iv","fastlio2_2022:Table IV","Xu et al., 2022","Table IV",[662],{"label":663,"unit":135,"statistic":378,"alignment":18},"Absolute translational error (RMSE)",[665,669,671,673,677,681,683,685,687,689,691,693],{"dataset":666,"sequence":667,"environment":668},"UTBM robocar dataset","utbm 8","human-driven robocar, urban, up to 50 km\u002Fh",{"dataset":666,"sequence":670,"environment":668},"utbm 9",{"dataset":666,"sequence":672,"environment":668},"utbm 10",{"dataset":674,"sequence":675,"environment":676},"UrbanLoco HK (ulhk)","ulhk 4","human-driven vehicle, urban with moving vehicles",{"dataset":678,"sequence":679,"environment":680},"NCLT","nclt 4","UGV, University of Michigan North Campus",{"dataset":678,"sequence":682,"environment":680},"nclt 5",{"dataset":678,"sequence":684,"environment":680},"nclt 6",{"dataset":678,"sequence":686,"environment":680},"nclt 7",{"dataset":678,"sequence":688,"environment":680},"nclt 8",{"dataset":678,"sequence":690,"environment":680},"nclt 9",{"dataset":678,"sequence":692,"environment":680},"nclt 10",{"dataset":694,"sequence":695,"environment":696},"LIO-SAM dataset","liosam 1","MIT campus (LIO-SAM data)",[698,700,702,704,705],{"name":699,"methodId":439,"linkable":171,"proposed":171,"self":77},"FAST-LIO2 (1000m), default local map size",{"name":701,"methodId":439,"linkable":171,"proposed":77,"self":77},"FAST-LIO2 (Feature), feature-based variant",{"name":703,"methodId":432,"linkable":171,"proposed":77,"self":77},"LILI-OM",{"name":435,"methodId":436,"linkable":171,"proposed":77,"self":77},{"name":7,"methodId":5,"linkable":171,"proposed":77,"self":171},[707,709,711,712,714,716,718,720,722,724,726,728,730,732,734,736,738,739,741,743,745,747,749,751,753,755,757,759,761,763,765,767,769,771,773,775,777,778,779,780,782,784,786,787,789,791,793,795,797,799,801,803,805,807,809,811,813,815,817,819],[178,178,178,708,180,178,180,180,178],27.29,[178,178,182,710,180,178,180,180,178],51.6,[178,178,185,541,180,178,180,180,178],[178,178,188,713,180,178,180,180,178],2.57,[178,178,191,715,180,178,180,180,178],8.71,[178,178,330,717,180,178,180,180,178],6.68,[178,178,462,719,180,178,180,180,178],20.96,[178,178,464,721,180,178,180,180,178],6.58,[178,178,466,723,180,178,180,180,178],30.08,[178,178,469,725,180,178,180,180,178],5.56,[178,178,472,727,180,178,180,180,178],16.29,[178,178,475,729,180,178,180,180,178],4.58,[182,178,178,731,180,178,180,180,178],27.21,[182,178,182,733,180,178,180,180,178],53.81,[182,178,185,735,180,178,180,180,178],22.59,[182,178,188,737,180,178,180,180,178],2.61,[182,178,191,495,180,178,180,180,178],[182,178,330,740,180,178,180,180,178],7.82,[182,178,462,742,180,178,180,180,178],20.57,[182,178,464,744,180,178,180,180,178],6.77,[182,178,466,746,180,178,180,180,178],31.17,[182,178,469,748,180,178,180,180,178],6.09,[182,178,472,750,180,178,180,180,178],16.61,[182,178,475,752,180,178,180,180,178],7.85,[185,178,178,754,180,178,180,180,178],59.48,[185,178,182,756,180,178,180,180,178],782.11,[185,178,185,758,180,178,180,180,178],17.59,[185,178,188,760,180,178,180,180,178],2.29,[185,178,191,762,180,178,180,180,178],317.77,[185,178,330,764,180,178,180,180,178],12.42,[185,178,462,766,180,178,180,180,178],260.76,[185,178,464,768,180,178,180,180,178],12.17,[185,178,466,770,180,178,180,180,178],276.74,[185,178,469,772,180,178,180,180,178],7.39,[185,178,472,774,180,178,180,180,178],328.87,[185,178,475,776,180,178,180,180,178],18.78,[188,178,178,86,178,178,180,180,178],[188,178,182,86,178,178,180,180,178],[188,178,185,86,178,178,180,180,178],[188,178,188,781,180,178,180,180,178],3.52,[188,178,191,783,180,178,180,180,178],9461,[188,178,330,785,180,178,180,180,178],7.15,[188,178,462,86,182,178,180,180,178],[188,178,464,788,180,178,180,180,178],22.26,[188,178,466,790,180,178,180,180,178],44.83,[188,178,469,792,180,178,180,180,178],7.43,[188,178,472,794,180,178,180,180,178],1077.5,[188,178,475,796,180,178,180,180,178],4.75,[191,178,178,798,180,178,180,180,178],48.17,[191,178,182,800,180,178,180,180,178],54.35,[191,178,185,802,180,178,180,180,178],60.48,[191,178,188,804,180,178,180,180,178],3.11,[191,178,191,806,180,178,180,180,178],65.95,[191,178,330,808,180,178,180,180,178],1051,[191,178,462,810,180,178,180,180,178],243.87,[191,178,464,812,180,178,180,180,178],378.99,[191,178,466,814,180,178,180,180,178],106.03,[191,178,469,816,180,178,180,180,178],11.13,[191,178,472,818,180,178,180,180,178],2995.9,[191,178,475,820,180,178,180,180,178],880.92,[822,823],"not_run: utbm lacks the attitude quaternion LIO-SAM needs","failed (system totally failed, marked x)",[660],[],[],[828],"Absolute translational error RMSE (m) in sequences with good ground truth; loop closure of LILI-OM and LIO-SAM deactivated; all on Manifold 2-C; map-size variants 2000\u002F800\u002F600 m omitted for row cap",{"slug":830,"group":831,"sourceId":832,"sourceLabel":833,"table":834,"selfRows":478,"metrics":835,"seqs":843,"entrants":852,"cells":876,"outcomes":979,"locators":981,"hardware":982,"wordings":983,"notes":984},"lvisam2021-table-ii","lvisam2021:Table II","lvisam2021","Shan et al., 2021","Table II",[836,838,840],{"label":837,"unit":135,"statistic":378,"alignment":18},"RMSE w.r.t. GPS (m)",{"label":839,"unit":135,"statistic":18,"alignment":42},"Translation (m), end-to-end",{"label":841,"unit":842,"statistic":18,"alignment":42},"Rotation (degree), end-to-end","deg",[844,848],{"dataset":845,"sequence":846,"environment":847},"Jackal (authors' data)","Jackal","outdoor area with structures, vegetation and various road surfaces (Clearpath Jackal UGV, manually driven)",{"dataset":849,"sequence":850,"environment":851},"Handheld (authors' data)","Handheld","open fields including an open baseball field with grass and a ground plane (handheld)",[853,856,858,861,864,866,868,870,872,874],{"name":854,"methodId":855,"linkable":171,"proposed":77,"self":77},"VINS (w\u002Fo loop)","vinsmono2018",{"name":857,"methodId":855,"linkable":171,"proposed":77,"self":77},"VINS (w\u002F loop)",{"name":859,"methodId":860,"linkable":171,"proposed":77,"self":77},"LOAM","loam2017_auro",{"name":862,"methodId":863,"linkable":171,"proposed":77,"self":77},"LIO-mapping","liomapping2019",{"name":865,"methodId":5,"linkable":171,"proposed":77,"self":171},"LINS (w\u002Fo loop)",{"name":867,"methodId":5,"linkable":171,"proposed":77,"self":171},"LINS (w\u002F loop)",{"name":869,"methodId":436,"linkable":171,"proposed":77,"self":77},"LIO-SAM (w\u002Fo loop)",{"name":871,"methodId":436,"linkable":171,"proposed":77,"self":77},"LIO-SAM (w\u002F loop)",{"name":873,"methodId":832,"linkable":171,"proposed":171,"self":77},"LVI-SAM (w\u002Fo loop)",{"name":875,"methodId":832,"linkable":171,"proposed":171,"self":77},"LVI-SAM (w\u002F loop)",[877,879,881,883,885,887,889,891,893,895,896,898,900,902,904,906,907,909,911,913,915,917,919,921,923,925,927,929,931,933,934,936,938,939,940,941,942,944,945,947,949,951,953,954,955,956,957,959,960,962,964,966,968,969,970,971,972,974,975,977],[178,178,178,878,180,178,180,180,178],8.58,[182,178,178,880,180,178,180,180,178],4.49,[185,178,178,882,180,178,180,180,178],44.92,[188,178,178,884,180,178,180,180,178],127.05,[191,178,178,886,180,178,180,180,178],3.95,[330,178,178,888,180,178,180,180,178],0.77,[462,178,178,890,180,178,180,180,178],3.54,[464,178,178,892,180,178,180,180,178],1.52,[466,178,178,894,180,178,180,180,178],4.05,[469,178,178,243,180,178,180,180,178],[178,182,178,897,180,178,180,180,178],10.82,[182,182,178,899,180,178,180,180,178],11.86,[185,182,178,901,180,178,180,180,178],61.73,[188,182,178,903,180,178,180,180,178],123.22,[191,182,178,905,180,178,180,180,178],7.37,[330,182,178,183,180,178,180,180,178],[462,182,178,908,180,178,180,180,178],5.48,[464,182,178,910,180,178,180,180,178],0.12,[466,182,178,912,180,178,180,180,178],4.69,[469,182,178,914,180,178,180,180,178],0.11,[178,185,178,916,180,178,180,180,178],9.98,[182,185,178,918,180,178,180,180,178],12.79,[185,185,178,920,180,178,180,180,178],61.41,[188,185,178,922,180,178,180,180,178],139.23,[191,185,178,924,180,178,180,180,178],4.8,[330,185,178,926,180,178,180,180,178],2.07,[462,185,178,928,180,178,180,180,178],2.18,[464,185,178,930,180,178,180,180,178],2.64,[466,185,178,932,180,178,180,180,178],2.28,[469,185,178,892,180,178,180,180,178],[178,178,182,935,180,178,180,180,178],87.53,[182,178,182,937,180,178,180,180,178],73.07,[185,178,182,86,178,178,180,180,178],[188,178,182,86,178,178,180,180,178],[191,178,182,86,178,178,180,180,178],[330,178,182,86,178,178,180,180,178],[462,178,182,943,180,178,180,180,178],53.62,[464,178,182,86,178,178,180,180,178],[466,178,182,946,180,178,180,180,178],7.87,[469,178,182,948,180,178,180,180,178],0.83,[178,182,182,950,180,178,180,180,178],40.65,[182,182,182,952,180,178,180,180,178],1.87,[185,182,182,86,178,178,180,180,178],[188,182,182,86,178,178,180,180,178],[191,182,182,86,178,178,180,180,178],[330,182,182,86,178,178,180,180,178],[462,182,182,958,180,178,180,180,178],58.91,[464,182,182,86,178,178,180,180,178],[466,182,182,961,180,178,180,180,178],7.57,[469,182,182,963,180,178,180,180,178],0.27,[178,185,182,965,180,178,180,180,178],53.48,[182,185,182,967,180,178,180,180,178],52.09,[185,185,182,86,178,178,180,180,178],[188,185,182,86,178,178,180,180,178],[191,185,182,86,178,178,180,180,178],[330,185,182,86,178,178,180,180,178],[462,185,182,973,180,178,180,180,178],20.17,[464,185,182,86,178,178,180,180,178],[466,185,182,976,180,178,180,180,178],24.82,[469,185,182,978,180,178,180,180,178],3.82,[980],"failed",[834],[],[],[985],"Comparison on the Jackal and Handheld datasets (start and end at the same position); RMSE computed against GPS positions treated as ground truth (Reach RS+, available only in some regions); Translation and Rotation are end-to-end errors; 'Fail' = no meaningful result",[987,994,1000,1005,1014,1020,1029,1033,1039,1045,1051,1058],{"group":988,"slug":989,"sourceLabel":990,"table":991,"selfRows":472,"datasets":992},"glim2024:Table V","glim2024-table-v","Koide et al., 2024","Table V",[993],"Multi-Camera Newer College",{"group":995,"slug":996,"sourceLabel":6,"table":997,"selfRows":472,"datasets":998},"lins2020:Table I","lins2020-table-i","Table I",[999],"Own LINS datasets",{"group":1001,"slug":1002,"sourceLabel":659,"table":991,"selfRows":464,"datasets":1003},"fastlio2_2022:Table V","fastlio2-2022-table-v",[694,1004,674],"LiLi-OM dataset (lili)",{"group":1006,"slug":1007,"sourceLabel":1008,"table":1009,"selfRows":464,"datasets":1010},"pointlio2023:Table 6","pointlio2023-table-6","He et al., 2023a","Table 6",[1011,1012,1013],"lili","liosam","ulhk",{"group":1015,"slug":1016,"sourceLabel":1017,"table":997,"selfRows":464,"datasets":1018},"voxelmappp2024:Table I","voxelmappp2024-table-i","Wu et al., 2024b",[1019],"M2DGR",{"group":1021,"slug":1022,"sourceLabel":1023,"table":1024,"selfRows":462,"datasets":1025},"liliom2021:Table 1","liliom2021-table-1","Li et al., 2021b","Table 1",[1026,1027,1028],"UTBM (EU long-term)","UrbanLoco","UrbanNav",{"group":1030,"slug":1031,"sourceLabel":6,"table":834,"selfRows":330,"datasets":1032},"lins2020:Table II","lins2020-table-ii",[999],{"group":1034,"slug":1035,"sourceLabel":1008,"table":1036,"selfRows":330,"datasets":1037},"pointlio2023:Table 5","pointlio2023-table-5","Table 5",[1012,1013,1038],"utbm",{"group":1040,"slug":1041,"sourceLabel":1042,"table":130,"selfRows":185,"datasets":1043},"he2023ikfom:Table III","he2023ikfom-table-iii","He et al., 2023b",[1044],"LIO-SAM open sequences",{"group":1046,"slug":1047,"sourceLabel":1008,"table":1048,"selfRows":185,"datasets":1049},"pointlio2023:Table 7","pointlio2023-table-7","Table 7",[1050],"12 public sequences (utbm, ulhk, liosam, lili)",{"group":1052,"slug":1053,"sourceLabel":1054,"table":1055,"selfRows":182,"datasets":1056},"fastlio2021:Text Sec.IV-D (LINS)","fastlio2021-text-sec-iv-d-lins","Xu & Zhang, 2021","Text Sec.IV-D (LINS)",[1057],"LINS dataset",{"group":1059,"slug":1060,"sourceLabel":1061,"table":1062,"selfRows":182,"datasets":1063},"ghadimzadeh2025slamnde:Table 3","ghadimzadeh2025slamnde-table-3","Ghadimzadeh Alamdari et al., 2025","Table 3",[1064],"Luleå SubT tunnel dataset (Koval et al. 2022)",1790510662977]