[{"data":1,"prerenderedAt":957},["ShallowReactive",2],{"method-dlio2023":3},{"method":4,"reference":57,"equipment":78,"figures":111,"results":112},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":22,"limitations":27,"sensors":33,"platform":36,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":43,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"dlio2023","Chen et al., 2023","DLIO","Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction",2023,"recent","C05","odometry_with_local_mapping","DLIO 以由粗到細的方式建構掃描內連續時間軌跡：先以 IMU 數值積分得到離散位姿，再以恆定急動度（jerk）與恆定角加速度的解析式為每個點求得去畸變轉換，可平行計算。去畸變同時產生 GICP 的初值，因此可省去掃描對掃描步驟而直接做掃描對地圖配準。狀態由具全域收斂性質的非線性幾何觀測器（geometric observer）更新，而非卡爾曼濾波或因子圖。","Coarse-to-fine continuous-time per-point deskewing (IMU integration refined with constant-jerk and angular-acceleration equations) combined with direct GICP scan-to-map registration and a nonlinear geometric observer.","full_text_reviewed","peer_reviewed_published","main_body","not_reported（測試為 Newer College 校園與 UCLA 校園手持資料）",[20,21],"public_benchmark","controlled_experiment",[23,24,25,26],"Ablation on Newer College shows full continuous-time correction reduces error versus none or discrete-only correction, especially in aggressive motion (Sec. IV-A; Table I)","Maps capture fine detail used for terrain cues (Sec. IV-B-2)","Lowest ATE on all five Newer College sequences and lowest end-to-end error and per-scan time on all four UCLA sequences among DLO, CT-ICP, LIO-SAM and FAST-LIO2 (Tables I-II)","Works with a low-cost 6-axis MPU-6050 IMU (about 10 USD) on the UCLA data (Sec. IV-B-2)",[28,29,30,31,32],"No loop closure; listed as future work (Sec. V)","Accuracy relies on scan matching returning an accurate solution for observer convergence (Sec. III-D) (author-stated condition)","RKO-LIO authors report DLIO had worse RPE than FAST-LIO2 and RKO-LIO on most Oxford Spires sequences (RKO-LIO, Sec. IV-B)","UCLA evaluation uses end-to-end translational error as a proxy because no ground truth was available (Sec. IV-B-2)","Baseline settings were modified: CT-ICP with larger voxelization and slowed playback, FAST-LIO2 first 100 poses excluded on some sequences (Sec. IV; Sec. IV-B-1)",[34,35],"3D mechanical LiDAR (tested: Ouster OS1 with 32 channels at 10 Hz on UCLA data and the Ouster LiDAR of Newer College; Velodyne is named only as an example input)","6-axis IMU (tested: InvenSense MPU-6050 on UCLA data; Ouster internal IMU at 100 Hz on Newer College)",[37],"handheld (Newer College; UCLA sequences recorded by hand-carrying the aerial platform, no flight experiments)","hierarchical nonlinear geometric observer (contraction-based) updated with GICP scan-to-map pose; IMU propagation between scans","GICP scan-to-map on dense, lightly filtered clouds (no feature extraction; scan-to-scan stage removed)","coarse discrete IMU integration refined by analytic continuous-time equations (constant jerk and constant angular acceleration) per point","point-wise continuous-time motion correction that also builds the GICP prior","none (adding loop closures listed as future work, Sec. V)","none","keyframe-based map with submap generation (from DLO)","odometry and keyframe point-cloud map; export format not_reported","CPU; all tests on a 16-core Intel i7-11800H; DLIO averaged 35.74 ms per scan across five Newer College sequences (Table I) and 8.37 to 10.96 ms per scan on the UCLA sequences (Table II)","https:\u002F\u002Fgithub.com\u002Fvectr-ucla\u002Fdirect_lidar_inertial_odometry","MIT (LICENSE file checked)",[50,54],{"relation":51,"title":52,"doi_or_url":53},"preprint","Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction (arXiv v4)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.03749",{"relation":55,"title":56,"doi_or_url":47},"code_release","vectr-ucla\u002Fdirect_lidar_inertial_odometry",{"id":5,"kind":58,"shortName":7,"title":8,"authors":59,"year":9,"venue":63,"venueType":64,"publisher":65,"volumeIssuePages":66,"doi":67,"arxivId":68,"url":69,"firstPublicDate":70,"publicationStatus":16,"metadataStatus":71,"fulltextStatus":15,"era":10,"classicReason":72,"codeUrl":47,"cluster":11,"topics":73,"mdpi":74,"verification":75,"label":6,"fulltextRoute":76,"versionRead":77,"addedByCensus":74},"method",[60,61,62],"Kenny Chen","Ryan Nemiroff","Brett T. Lopez","2023 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 3983-3989","10.1109\u002Ficra48891.2023.10160508","2203.03749","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FICRA48891.2023.10160508","2022-03-07","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v4 (2023-03-07), ICRA 2023 accepted version; IEEE version of record not read",[79,86,90,95,101,104],{"category":80,"model":81,"canonical":81,"role":82,"dataset":83,"specs":84,"locator":85},"lidar","Ouster OS1","method input","UCLA Campus (self-collected)","10 Hz, 32 channels recorded with 512 horizontal resolution","Sec. IV-B-2",{"category":87,"model":88,"canonical":88,"role":82,"dataset":83,"specs":89,"locator":85},"imu","InvenSense MPU-6050","6-axis, about 10 USD, mounted about 0.1 m below the LiDAR",{"category":91,"model":92,"canonical":92,"role":82,"dataset":83,"specs":93,"locator":94},"platform","custom aerial vehicle (hand-carried for data collection, no flight)","not_reported","Fig. 1; Sec. IV-B-2",{"category":80,"model":96,"canonical":96,"role":97,"dataset":98,"specs":99,"locator":100},"Ouster LiDAR (model not stated in DLIO)","dataset sensor","Newer College Dataset","10 Hz","Sec. IV-B-1",{"category":87,"model":102,"canonical":102,"role":97,"dataset":98,"specs":103,"locator":100},"Ouster internal IMU","100 Hz",{"category":105,"model":106,"canonical":106,"role":107,"dataset":108,"specs":109,"locator":110},"compute","Intel i7-11800H","compute for runtime",null,"16-core CPU","Sec. IV",[],{"totalRows":113,"groupCount":114,"groups":115,"others":902},110,13,[116,265,529,778],{"slug":117,"group":118,"sourceId":5,"sourceLabel":6,"table":119,"selfRows":120,"metrics":121,"seqs":130,"entrants":145,"cells":165,"outcomes":258,"locators":259,"hardware":260,"wordings":262,"notes":263},"dlio2023-table-i","dlio2023:Table I","Table I",18,[122,126],{"label":123,"unit":124,"statistic":125,"alignment":93},"Absolute Trajectory Error (RMSE)","m","RMSE",{"label":127,"unit":128,"statistic":129,"alignment":72},"Avg Comp. 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Intel i7-11800H CPU",[],[264],"Original Newer College dataset, Ouster LiDAR 10 Hz with Ouster IMU 100 Hz, evaluated with evo; default parameters except extrinsics; LIO-SAM loop closures enabled; FAST-LIO2 online extrinsic estimation disabled and first 100 poses excluded on some sequences; CT-ICP voxelization increased and playback slowed to avoid failure",{"slug":266,"group":267,"sourceId":268,"sourceLabel":269,"table":270,"selfRows":271,"metrics":272,"seqs":278,"entrants":319,"cells":332,"outcomes":522,"locators":524,"hardware":525,"wordings":526,"notes":527},"iglio2024-table-iii","iglio2024:Table III","iglio2024","Chen et al., 2024","Table III",16,[273,276],{"label":274,"unit":124,"statistic":125,"alignment":275},"Absolute pose error (RMSE, meters)","SE3",{"label":274,"unit":124,"statistic":125,"alignment":277},"first-pose",[279,283,285,287,289,291,295,297,299,301,303,307,309,313,315,317],{"dataset":280,"sequence":281,"environment":282},"NCLT","nclt_1","campus (Velodyne 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('-': method did not participate)",[270],[],[],[528],"Absolute pose error (RMSE, m); identical iG-LIO parameters for all sequences; BG sequences evaluated with origin alignment, others with SE(3) alignment; '*' marks Livox avia sequences",{"slug":530,"group":531,"sourceId":532,"sourceLabel":533,"table":534,"selfRows":511,"metrics":535,"seqs":538,"entrants":572,"cells":594,"outcomes":770,"locators":772,"hardware":774,"wordings":775,"notes":776},"chen2025geode-table-5","chen2025geode:Table 5","chen2025geode","Chen et al., 2025b","Table 5",[536],{"label":537,"unit":124,"statistic":93,"alignment":93},"ATE (m)",[539,543,545,547,549,551,553,555,557,559,561,563,565,568,570],{"dataset":540,"sequence":541,"environment":542},"GEODE","Metro tunnels, alpha (Velodyne VLP-16), Tunneling 3","metro tunnel, mine tunnelling method (translational degeneracy along the axis)",{"dataset":540,"sequence":544,"environment":542},"Metro tunnels, alpha (Velodyne VLP-16), Tunneling 4",{"dataset":540,"sequence":546,"environment":542},"Metro tunnels, alpha (Velodyne VLP-16), Tunneling 5",{"dataset":540,"sequence":548,"environment":542},"Metro tunnels, beta (Ouster OS1-64), Tunneling 2",{"dataset":540,"sequence":550,"environment":542},"Metro tunnels, beta (Ouster OS1-64), Tunneling 3",{"dataset":540,"sequence":552,"environment":542},"Metro tunnels, beta (Ouster OS1-64), Tunneling 4",{"dataset":540,"sequence":554,"environment":542},"Metro tunnels, beta (Ouster OS1-64), Tunneling 5",{"dataset":540,"sequence":556,"environment":542},"Metro tunnels, gamma (Livox AVIA), Tunneling 1",{"dataset":540,"sequence":558,"environment":542},"Metro tunnels, gamma (Livox AVIA), Tunneling 2",{"dataset":540,"sequence":560,"environment":542},"Metro tunnels, gamma (Livox AVIA), Tunneling 3",{"dataset":540,"sequence":562,"environment":542},"Metro tunnels, gamma (Livox AVIA), Tunneling 4",{"dataset":540,"sequence":564,"environment":542},"Metro tunnels, gamma (Livox AVIA), Tunneling 5",{"dataset":540,"sequence":566,"environment":567},"Stairs, alpha (Velodyne VLP-16), Stairs","building stairs and corridors across multiple floors (loop from the seventh floor)",{"dataset":540,"sequence":569,"environment":567},"Stairs, beta (Ouster OS1-64), Stairs",{"dataset":540,"sequence":571,"environment":567},"Stairs, gamma (Livox AVIA), Stairs",[573,576,578,579,582,585,588,591],{"name":574,"methodId":575,"linkable":149,"proposed":74,"self":74},"COIN-LIO","coinlio2024",{"name":577,"methodId":158,"linkable":149,"proposed":74,"self":74},"FAST-LIO2",{"name":7,"methodId":5,"linkable":149,"proposed":74,"self":149},{"name":580,"methodId":581,"linkable":149,"proposed":74,"self":74},"FAST-LIVO","fastlivo2022",{"name":583,"methodId":584,"linkable":149,"proposed":74,"self":74},"Coco-LIC","cocolic2023",{"name":586,"methodId":587,"linkable":149,"proposed":74,"self":74},"R3LIVE","r3live2022",{"name":589,"methodId":590,"linkable":149,"proposed":74,"self":74},"LVI-SAM","lvisam2021",{"name":592,"methodId":593,"linkable":149,"proposed":74,"self":74},"VINS-Fusion","vinsfusion2019",[595,596,598,600,602,603,605,606,608,609,611,613,615,616,618,619,620,621,622,624,626,627,629,631,633,634,635,637,639,641,642,643,645,647,648,649,650,651,652,654,655,657,658,659,661,662,663,664,665,667,668,669,670,671,673,675,677,678,680,682,683,685,687,689,690,691,693,695,696,698,700,702,703,704,705,707,708,710,712,713,714,715,716,718,719,720,722,724,725,726,727,729,730,731,733,734,736,737,739,741,743,744,746,747,749,751,753,755,757,759,760,761,762,763,764,765,766,767,768,769],[167,167,167,108,167,167,169,169,167],[171,167,167,597,169,167,169,169,167],0.21,[174,167,167,599,169,167,169,169,167],0.18,[177,167,167,601,169,167,169,169,167],0.2,[180,167,167,108,167,167,169,169,167],[183,167,167,604,169,167,169,169,167],0.59,[246,167,167,108,171,167,169,169,167],[417,167,167,607,169,167,169,169,167],47.66,[167,167,171,108,167,167,169,169,167],[171,167,171,610,169,167,169,169,167],0.24,[174,167,171,612,169,167,169,169,167],0.14,[177,167,171,614,169,167,169,169,167],0.19,[180,167,171,108,167,167,169,169,167],[183,167,171,617,169,167,169,169,167],0.25,[246,167,171,108,171,167,169,169,167],[417,167,171,108,171,167,169,169,167],[167,167,174,108,167,167,169,169,167],[171,167,174,614,169,167,169,169,167],[174,167,174,623,169,167,169,169,167],0.13,[177,167,174,625,169,167,169,169,167],0.17,[180,167,174,108,167,167,169,169,167],[183,167,174,628,169,167,169,169,167],11.44,[246,167,174,630,169,167,169,169,167],0.3,[417,167,174,632,169,167,169,169,167],0.69,[167,167,177,612,169,167,169,169,167],[171,167,177,612,169,167,169,169,167],[174,167,177,636,169,167,169,169,167],0.11,[177,167,177,638,169,167,169,169,167],0.12,[180,167,177,640,169,167,169,169,167],0.15,[183,167,177,612,169,167,169,169,167],[246,167,177,612,169,167,169,169,167],[417,167,177,644,169,167,169,169,167],2.31,[167,167,180,646,169,167,169,169,167],0.23,[171,167,180,625,169,167,169,169,167],[174,167,180,612,169,167,169,169,167],[177,167,180,640,169,167,169,169,167],[180,167,180,614,169,167,169,169,167],[183,167,180,601,169,167,169,169,167],[246,167,180,653,169,167,169,169,167],0.37,[417,167,180,108,171,167,169,169,167],[167,167,183,656,169,167,169,169,167],0.16,[171,167,183,625,169,167,169,169,167],[174,167,183,640,169,167,169,169,167],[177,167,183,660,169,167,169,169,167],0.27,[180,167,183,625,169,167,169,169,167],[183,167,183,617,169,167,169,169,167],[246,167,183,599,169,167,169,169,167],[417,167,183,108,171,167,169,169,167],[167,167,246,666,169,167,169,169,167],3.84,[171,167,246,638,169,167,169,169,167],[174,167,246,636,169,167,169,169,167],[177,167,246,623,169,167,169,169,167],[180,167,246,599,169,167,169,169,167],[183,167,246,672,169,167,169,169,167],0.26,[246,167,246,674,169,167,169,169,167],0.22,[417,167,246,676,169,167,169,169,167],0.36,[167,167,417,108,167,167,169,169,167],[171,167,417,679,169,167,169,169,167],1.16,[174,167,417,681,169,167,169,169,167],8.51,[177,167,417,676,169,167,169,169,167],[180,167,417,684,169,167,169,169,167],0.42,[183,167,417,686,169,167,169,169,167],2.83,[246,167,417,688,169,167,169,169,167],35.4,[417,167,417,108,171,167,169,169,167],[167,167,429,108,167,167,169,169,167],[171,167,429,692,169,167,169,169,167],1.88,[174,167,429,694,169,167,169,169,167],15.43,[177,167,429,360,169,167,169,169,167],[180,167,429,697,169,167,169,169,167],2.06,[183,167,429,699,169,167,169,169,167],86.51,[246,167,429,701,169,167,169,169,167],2.1,[417,167,429,108,171,167,169,169,167],[167,167,439,108,167,167,169,169,167],[171,167,439,614,169,167,169,169,167],[174,167,439,706,169,167,169,169,167],2.63,[177,167,439,601,169,167,169,169,167],[180,167,439,709,169,167,169,169,167],4.06,[183,167,439,711,169,167,169,169,167],63.05,[246,167,439,610,169,167,169,169,167],[417,167,439,108,171,167,169,169,167],[167,167,450,108,167,167,169,169,167],[171,167,450,640,169,167,169,169,167],[174,167,450,717,169,167,169,169,167],5.74,[177,167,450,623,169,167,169,169,167],[180,167,450,599,169,167,169,169,167],[183,167,450,721,169,167,169,169,167],57.46,[246,167,450,723,169,167,169,169,167],0.35,[417,167,450,108,171,167,169,169,167],[167,167,462,108,167,167,169,169,167],[171,167,462,660,169,167,169,169,167],[174,167,462,728,169,167,169,169,167],2.48,[177,167,462,638,169,167,169,169,167],[180,167,462,625,169,167,169,169,167],[183,167,462,732,169,167,169,169,167],1.3,[246,167,462,625,169,167,169,169,167],[417,167,462,735,169,167,169,169,167],22.03,[167,167,475,108,167,167,169,169,167],[171,167,475,738,169,167,169,169,167],4.69,[174,167,475,740,169,167,169,169,167],4.89,[177,167,475,742,169,167,169,169,167],3.28,[180,167,475,108,167,167,169,169,167],[183,167,475,745,169,167,169,169,167],4.54,[246,167,475,108,171,167,169,169,167],[417,167,475,748,169,167,169,169,167],3.66,[167,167,114,750,169,167,169,169,167],0.45,[171,167,114,752,169,167,169,169,167],0.38,[174,167,114,754,169,167,169,169,167],0.41,[177,167,114,756,169,167,169,169,167],0.4,[180,167,114,758,169,167,169,169,167],6.84,[183,167,114,108,171,167,169,169,167],[246,167,114,108,171,167,169,169,167],[417,167,114,604,169,167,169,169,167],[167,167,499,108,167,167,169,169,167],[171,167,499,108,171,167,169,169,167],[174,167,499,108,171,167,169,169,167],[177,167,499,108,171,167,169,169,167],[180,167,499,108,171,167,169,169,167],[183,167,499,108,171,167,169,169,167],[246,167,499,108,171,167,169,169,167],[417,167,499,108,171,167,169,169,167],[72,771],"failed",[773],"Table 5 (VoR)",[],[],[777],"ATE (m) per sequence, average of five runs, parameters not tuned per sequence; X = breakdown or error > 100 m; - = algorithm not adapted to this data. Only Metro tunnels and Stairs blocks extracted; shield-tunnel sequences are absent from Table 5 because all methods failed (Sec. 5.2). Stairs GT from PALoc (inlier RMSE 0.07 m alpha, 0.08 m beta); gamma stairs GT not accurate.",{"slug":779,"group":780,"sourceId":781,"sourceLabel":782,"table":119,"selfRows":450,"metrics":783,"seqs":789,"entrants":802,"cells":812,"outcomes":895,"locators":896,"hardware":897,"wordings":898,"notes":899},"rkolio2026-table-i","rkolio2026:Table I","rkolio2026","Malladi et al., 2026",[784,786],{"label":785,"unit":124,"statistic":93,"alignment":93},"ATE (m), averaged over sequences of each scene",{"label":787,"unit":788,"statistic":129,"alignment":93},"RPE (%), intervals 1, 2, 5, 10, 20, 50, 100 m","%",[790,794,796,798,800],{"dataset":791,"sequence":792,"environment":793},"Oxford Spires","Blenheim","university campus and college buildings, outdoor and indoor, backpack",{"dataset":791,"sequence":795,"environment":793},"Bodleian",{"dataset":791,"sequence":797,"environment":793},"Christ",{"dataset":791,"sequence":799,"environment":793},"Keble",{"dataset":791,"sequence":801,"environment":793},"Radcliffe",[803,806,807,808,810],{"name":804,"methodId":805,"linkable":149,"proposed":74,"self":74},"KISS-ICP","kissicp2023",{"name":7,"methodId":5,"linkable":149,"proposed":74,"self":149},{"name":577,"methodId":158,"linkable":149,"proposed":74,"self":74},{"name":809,"methodId":781,"linkable":149,"proposed":149,"self":74},"Ours (RKO-LIO)",{"name":811,"methodId":108,"linkable":74,"proposed":74,"self":74},"VILENS-SLAM (SLAM reference, results from Tao et al.)",[813,814,816,818,820,822,824,825,826,827,829,831,832,834,836,837,838,840,841,843,844,846,848,849,850,852,854,856,858,860,862,864,865,867,869,871,872,873,875,877,879,881,883,884,885,887,889,891,892,894],[167,167,167,679,169,167,169,169,167],[167,167,171,815,169,167,169,169,167],2.15,[167,167,174,817,169,167,169,169,167],0.96,[167,167,177,819,169,167,169,169,167],9.48,[167,167,180,821,169,167,169,169,167],0.46,[171,167,167,823,169,167,169,169,167],11.35,[171,167,171,756,169,167,169,169,167],[171,167,174,625,169,167,169,169,167],[171,167,177,676,169,167,169,169,167],[171,167,180,828,169,167,169,169,167],0.92,[174,167,167,830,169,167,169,169,167],0.94,[174,167,171,672,169,167,169,169,167],[174,167,174,833,169,167,169,169,167],0.72,[174,167,177,835,169,167,169,169,167],6.06,[174,167,180,821,169,167,169,169,167],[177,167,167,601,169,167,169,169,167],[177,167,171,839,169,167,169,169,167],0.86,[177,167,174,617,169,167,169,169,167],[177,167,177,842,169,167,169,169,167],0.08,[177,167,180,640,169,167,169,169,167],[180,167,167,845,169,167,169,169,167],0.56,[180,167,171,847,169,167,169,169,167],1.11,[180,167,174,636,169,167,169,169,167],[180,167,177,638,169,167,169,169,167],[180,167,180,851,169,167,169,169,167],0.07,[167,171,167,853,169,167,169,169,171],46.99,[167,171,171,855,169,167,169,169,171],19.1,[167,171,174,857,169,167,169,169,171],11.01,[167,171,177,859,169,167,169,169,171],38.48,[167,171,180,861,169,167,169,169,171],3.58,[171,171,167,863,169,167,169,169,171],46.87,[171,171,171,847,169,167,169,169,171],[171,171,174,866,169,167,169,169,171],4.17,[171,171,177,868,169,167,169,169,171],4.16,[171,171,180,870,169,167,169,169,171],4.28,[174,171,167,847,169,167,169,169,171],[174,171,171,672,169,167,169,169,171],[174,171,174,874,169,167,169,169,171],23.64,[174,171,177,876,169,167,169,169,171],5.42,[174,171,180,878,169,167,169,169,171],1.4,[177,171,167,880,169,167,169,169,171],0.95,[177,171,171,882,169,167,169,169,171],0.74,[177,171,174,390,169,167,169,169,171],[177,171,177,817,169,167,169,169,171],[177,171,180,886,169,167,169,169,171],0.7,[180,171,167,888,169,167,169,169,171],1.13,[180,171,171,890,169,167,169,169,171],1.68,[180,171,174,752,169,167,169,169,171],[180,171,177,893,169,167,169,169,171],0.39,[180,171,180,893,169,167,169,169,171],[],[119],[],[],[900,901],"Oxford Spires backpack (Hesai QT64); ground truth by registering undistorted scans to a TLS map; averages over all sequences of each scene; odometry without loop closure except the VILENS-SLAM reference; initialization disabled because sequences start in motion","Oxford Spires backpack (Hesai QT64); ground truth by registering undistorted scans to a TLS map; averages over all sequences of each scene; odometry without loop closure except the VILENS-SLAM reference; initialization disabled because sequences start in motion; RPE over 1 to 100 m intervals",[903,910,915,923,928,934,939,944,952],{"group":904,"slug":905,"sourceLabel":906,"table":907,"selfRows":450,"datasets":908},"vegatorres2024slam2ref:Table 1","vegatorres2024slam2ref-table-1","Vega-Torres et al., 2024","Table 1",[909],"ConSLAM",{"group":911,"slug":912,"sourceLabel":6,"table":913,"selfRows":429,"datasets":914},"dlio2023:Table II","dlio2023-table-ii","Table II",[83],{"group":916,"slug":917,"sourceLabel":269,"table":913,"selfRows":246,"datasets":918},"iglio2024:Table II","iglio2024-table-ii",[919,920,921,280,922],"AVIA","BG","NCD","ULHK",{"group":924,"slug":925,"sourceLabel":782,"table":270,"selfRows":246,"datasets":926},"rkolio2026:Table III","rkolio2026-table-iii",[927],"Leg-KILO dataset",{"group":929,"slug":930,"sourceLabel":782,"table":931,"selfRows":246,"datasets":932},"rkolio2026:Table IV","rkolio2026-table-iv","Table IV",[933],"DigiForests",{"group":935,"slug":936,"sourceLabel":937,"table":913,"selfRows":246,"datasets":938},"steamlio2025:Table II","steamlio2025-table-ii","Burnett et al., 2025",[98],{"group":940,"slug":941,"sourceLabel":782,"table":913,"selfRows":183,"datasets":942},"rkolio2026:Table II","rkolio2026-table-ii",[943],"own car dataset",{"group":945,"slug":946,"sourceLabel":947,"table":948,"selfRows":177,"datasets":949},"lee2024lidarodom_survey:Table 4","lee2024lidarodom-survey-table-4","Lee et al., 2024b","Table 4",[909,950,951],"HeLiPR","NTU VIRAL",{"group":953,"slug":954,"sourceLabel":937,"table":955,"selfRows":171,"datasets":956},"steamlio2025:Text Sec.V-B","steamlio2025-text-sec-v-b","Text Sec.V-B",[98],1790510653875]