[{"data":1,"prerenderedAt":832},["ShallowReactive",2],{"method-liliom2021":3},{"method":4,"reference":60,"equipment":80,"figures":133,"results":134},{"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":28,"sensors":33,"platform":36,"estimator":40,"association":41,"timeModel":42,"deskew":43,"loopClosure":44,"globalOptimization":45,"mapRepresentation":46,"prior":47,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"liliom2021","Li et al., 2021b","LiLi-OM","Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping",2021,"recent","C04","full_slam_with_global_correction","LiLi-OM 是同時支援固態（Livox Horizon）與機械式 LiDAR 的緊耦合 LiDAR 慣性里程計與建圖系統。前端以輕量的特徵式掃描配準（點到邊、點到面）快速估計運動並自適應挑選關鍵影格；後端以階層式、關鍵影格為基礎的滑動視窗最佳化直接融合 LiDAR 與 IMU 預積分，並以 ICP 驗證迴圈、GTSAM 最佳化全域位姿圖。針對 Horizon 不規則的掃描樣式，論文另提出兩階段的特徵擷取方法。","LiLi-OM tightly couples lidar features and preintegrated IMU in a hierarchical keyframe sliding window, with a dedicated feature extractor for the Livox Horizon and ICP-based loop closure in a global pose graph.","full_text_reviewed","peer_reviewed_published","supplementary","not_reported（背包平台與低成本固態 LiDAR 與工地巡檢式掃描情境相近（推論），但論文僅於城市、校園與樹叢場景測試）。",[20,21,22],"public_benchmark","cross_site","independent_reference",[24,25,26,27],"Lowest APE RMSE among the tested systems on UTBM, UrbanLoco and UrbanNav sequences (e.g., UL-1 1.59 m) (Sec. 5.2, Table 1)","Livox Horizon + Xsens suite (about 1700 EUR in Q1 2020) performed comparably to a HDL-64E setup on the FR-IOSB data (Sec. 5.3, Table 2)","Works with a six-axis IMU (Sec. 2, 5.2)","On the KA-Urban backpack sequences end-to-end errors were 0.08 to 1.28 m for LiLi-OM vs 0.40 to 109.62 m for Livox-Horizon-LOAM; disabling loop closure or removing IMU constraints increased the errors (Sec. 5.3.2, Table 3)",[29,30,31,32],"Suggests TSDF or geometric-primitive map representations would be needed for large-scale mapping with limited memory (Sec. 6)","End-to-end errors on own datasets rely on satellite-image-registered endpoints rather than full trajectory ground truth (Sec. 5.3.2)","Small FoV of solid-state LiDARs can still degrade odometry under fast motion or with insufficient features (Sec. 1)","Only ground and backpack platforms tested; UAV use under aggressive 6-DoF motion left for future work (Sec. 6)",[34,35],"solid-state LiDAR (Livox Horizon, 81.7 x 25.1 deg FoV) or spinning LiDAR (HDL-32E, HDL-64E)","IMU (6-axis sufficient; Xsens MTi-670 in own suite)",[37,38,39],"ground mobile robot platform provided by Fraunhofer IOSB (FR-IOSB data)","backpack (KA-Urban; sequence East recorded while cycling)","vehicle (public urban datasets UTBM, UrbanLoco, UrbanNav)","keyframe-based sliding-window optimization fusing LiDAR features and preintegrated IMU via marginalization (Ceres); in-between frames by local factor graph; global pose graph (GTSAM) at loop closure (Sec. 2, 4, 5.1)","point-to-edge and point-to-plane residuals against local feature maps (frontend about 20 recent frames, backend 30 recent keyframes), each residual weighted by agreement of edge direction or plane normal and by reflectance similarity of the five nearest features; two-stage time-domain extractor for the Livox Horizon (6 x 7-point patches, plane if lambda1\u002Flambda2 \u003C 0.3, else edge test lambda2\u002Flambda3 \u003C 0.25) because LOAM's per-scan-line smoothness cannot be applied to it; LOAM preprocessing only for spinning LiDARs (LiLi-OM*)","discrete keyframe and regular-frame states; a new keyframe is created when feature overlap with the local map drops below 60% or after a set number (e.g. two) of regular frames; the sliding window usually spans three keyframes and regular-frame poses come from a local factor graph","rotational de-skew from gyroscope before feature extraction, translational de-skew from frame-to-model odometry estimate (Sec. 2)","radius search (e.g., 10 m) for spatially close but temporally distant keyframes, ICP fitting score for acceptance, then global pose-graph optimization (Sec. 4)","global pose graph optimized with GTSAM when a loop is confirmed (Sec. 4, 5.1)","keyframe feature maps attached to a global pose graph; frontend local map of about 20 recent frames and backend local map from 30 recent keyframes; features voxel-grid downsampled before building LiDAR constraints","none","keyframe feature map and trajectory (Sec. 4, Fig. 8)","Laptop with Intel Core i5-7300HQ, 8 GB RAM, all four cores; three ROS nodes run in parallel; mean per-frame runtime: preprocessing 9.99 to 30.14 ms, scan registration 16.71 to 27.15 ms, backend fusion 41.81 to 60.92 ms; real time at the 10 Hz LiDAR frame rate on all data sets","https:\u002F\u002Fgithub.com\u002FKIT-ISAS\u002Flili-om","GPL-3.0 (LICENSE file)",[53,57],{"relation":54,"title":55,"doi_or_url":56},"preprint","arXiv 2010.13150 (v1 2020-10-25, v3 2021-04-27)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.13150",{"relation":58,"title":59,"doi_or_url":50},"code_release","KIT-ISAS\u002Flili-om",{"id":5,"kind":61,"shortName":7,"title":8,"authors":62,"year":9,"venue":66,"venueType":67,"publisher":68,"volumeIssuePages":69,"doi":70,"arxivId":71,"url":56,"firstPublicDate":72,"publicationStatus":16,"metadataStatus":73,"fulltextStatus":15,"era":10,"classicReason":74,"codeUrl":50,"cluster":11,"topics":75,"mdpi":76,"verification":77,"label":6,"fulltextRoute":78,"versionRead":79,"addedByCensus":76},"method",[63,64,65],"Kailai Li","Meng Li","Uwe D. Hanebeck","IEEE Robotics and Automation Letters","journal","IEEE","6(3): 5167-5174","10.1109\u002Flra.2021.3070251","2010.13150","2020-10-25","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv 2010.13150v3 (2021-04-27), author version stating the RA-L 6(3) 5167-5174 citation; not compared with the IEEE Xplore version of record",[81,88,93,99,103,109,113,117,122,127],{"category":82,"model":83,"canonical":83,"role":84,"dataset":85,"specs":86,"locator":87},"lidar","Livox Horizon","method input",null,"solid-state, 81.7 x 25.1 deg FoV, 10 Hz; six vertically aligned laser diodes sweeping non-repetitively; 0.2 to 0.4 deg angular resolution over a 100 ms frame","Sec. 1; Sec. 3.1; Fig. 3",{"category":89,"model":90,"canonical":90,"role":84,"dataset":85,"specs":91,"locator":92},"imu","Xsens MTi-670","gyroscope and accelerometer readings, e.g. 200 Hz; Livox Horizon + Xsens suite cost about 1700 EUR (Q1 2020)","Sec. 2; Sec. 5.3",{"category":82,"model":94,"canonical":94,"role":95,"dataset":96,"specs":97,"locator":98},"Velodyne HDL-64E","compared device","FR-IOSB","high-end spinning LiDAR mounted on the same platform for comparison","Sec. 5.3.1; Fig. 7",{"category":89,"model":100,"canonical":100,"role":95,"dataset":96,"specs":101,"locator":102},"Xsens MTi-G-700","six-axis, 150 Hz, synchronized with the HDL-64E","Sec. 5.3.1",{"category":82,"model":104,"canonical":104,"role":105,"dataset":106,"specs":107,"locator":108},"Velodyne HDL-32E","dataset sensor","UTBM (EU long-term), UrbanLoco, UrbanNav","spinning LiDAR of the public data sets","Sec. 5.2",{"category":89,"model":110,"canonical":110,"role":105,"dataset":111,"specs":112,"locator":108},"six-axis IMU (model not stated)","UTBM (EU long-term)","100 Hz",{"category":89,"model":114,"canonical":114,"role":105,"dataset":115,"specs":116,"locator":108},"Xsens MTi-10","UrbanLoco, UrbanNav","nine-axis, 100 Hz",{"category":118,"model":119,"canonical":119,"role":84,"dataset":96,"specs":120,"locator":121},"platform","mobile robot platform (Fraunhofer IOSB)","carries the Livox-Xsens suite and the HDL-64E with MTi-G-700","Sec. 5.3.1; Fig. 7; Acknowledgment",{"category":118,"model":123,"canonical":123,"role":84,"dataset":124,"specs":125,"locator":126},"backpack","KA-Urban","carries the Livox-Xsens suite; KA-Urban sequences 0.20 to 3.70 km","Sec. 5.3.2; Fig. 9",{"category":128,"model":129,"canonical":129,"role":130,"dataset":85,"specs":131,"locator":132},"compute","laptop with Intel Core i5-7300HQ","compute for runtime","8 GB RAM, all four CPU cores used","Sec. 5.4",[],{"totalRows":135,"groupCount":136,"groups":137,"others":735},162,21,[138,367,444,523],{"slug":139,"group":140,"sourceId":141,"sourceLabel":142,"table":143,"selfRows":144,"metrics":145,"seqs":153,"entrants":180,"cells":193,"outcomes":359,"locators":362,"hardware":363,"wordings":364,"notes":365},"fasterlio2022-table-ii","fasterlio2022:Table II","fasterlio2022","Bai et al., 2022","Table II",18,[146,150],{"label":147,"unit":148,"statistic":149,"alignment":149},"APE (m)","m","not_reported",{"label":151,"unit":152,"statistic":149,"alignment":47},"RPE (%) translational per 100 m","%",[154,158,160,164,166,168,170,174,176],{"dataset":155,"sequence":156,"environment":157},"NCLT","nclt_2 (0.26 km)","NCLT public dataset (scene and platform not described in the paper)",{"dataset":155,"sequence":159,"environment":157},"nclt_4 (1.86 km)",{"dataset":161,"sequence":162,"environment":163},"UTBM robocar dataset","utbm_2 (5.03 km)","UTBM robocar public dataset (scene not described in the paper)",{"dataset":161,"sequence":165,"environment":163},"utbm_3 (4.99 km)",{"dataset":161,"sequence":167,"environment":163},"utbm_4 (4.99 km)",{"dataset":161,"sequence":169,"environment":163},"utbm_5 (5.00 km)",{"dataset":171,"sequence":172,"environment":173},"ULHK (UrbanLoco)","ulhk_1 (0.60 km)","ULHK public dataset (scene not described in the paper)",{"dataset":171,"sequence":175,"environment":173},"ulhk_2 (0.62 km)",{"dataset":177,"sequence":178,"environment":179},"LIO-SAM dataset","liosam_1 (1.44 km)","liosam_1 sequence (scene not described in the paper)",[181,184,186,189,192],{"name":182,"methodId":141,"linkable":183,"proposed":183,"self":76},"Faster-LIO",true,{"name":185,"methodId":141,"linkable":183,"proposed":183,"self":76},"Faster-LIO 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LIO-SAM needs 9-axis IMU input, UTBM skipped (Sec. V-C)","'-' in table (Table I footnote: failed due to large drift or lack of necessary input)",[143],[],[],[366],"Accuracy in APE (m) over whole trajectories and translational RPE (%) per 100 m; loop closure of LIO-SAM and LiLi-OM disabled; parameters of LIO-SAM and LiLi-OM not adjusted; reference mostly RTK",{"slug":368,"group":369,"sourceId":5,"sourceLabel":6,"table":370,"selfRows":144,"metrics":371,"seqs":380,"entrants":399,"cells":401,"outcomes":437,"locators":438,"hardware":439,"wordings":441,"notes":442},"liliom2021-table-4","liliom2021:Table 4","Table 4",[372,376,378],{"label":373,"unit":374,"statistic":375,"alignment":149},"runtime of preprocessing node per frame","ms","mean",{"label":377,"unit":374,"statistic":375,"alignment":149},"runtime of scan registration node per frame",{"label":379,"unit":374,"statistic":375,"alignment":149},"runtime of backend fusion node per frame",[381,384,387,391,394,397],{"dataset":111,"sequence":382,"environment":383},"UTBM-2","Velodyne HDL-32E 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(own)","Schloss-2",{"dataset":395,"sequence":398,"environment":393},"East",[400],{"name":7,"methodId":5,"linkable":183,"proposed":183,"self":183},[402,404,406,408,409,411,413,415,417,419,421,423,425,427,429,431,433,435],[195,195,195,403,197,195,195,197,195],12.72,[195,195,199,405,197,195,195,197,195],13.31,[195,195,206,407,197,195,195,197,195],30.14,[195,195,211,350,197,195,195,197,195],[195,195,216,410,197,195,195,197,195],10.21,[195,195,221,412,197,195,195,197,195],11.76,[195,199,195,414,197,195,195,197,195],22.29,[195,199,199,416,197,195,195,197,195],23.71,[195,199,206,418,197,195,195,197,195],16.71,[195,199,211,420,197,195,195,197,195],22.69,[195,199,216,422,197,195,195,197,195],27.15,[195,199,221,424,197,195,195,197,195],25.3,[195,206,195,426,197,195,195,197,195],50.27,[195,206,199,428,197,195,195,197,195],57.62,[195,206,206,430,197,195,195,197,195],60.92,[195,206,211,432,197,195,195,197,195],58.86,[195,206,216,434,197,195,195,197,195],54.56,[195,206,221,436,197,195,195,197,195],41.81,[],[370],[440],"laptop, Intel Core i5-7300HQ, 8 GB RAM, four cores",[],[443],"Average runtime per frame of the three parallel ROS nodes; Velodyne HDL columns use the spinning-LiDAR preprocessing (LiLi-OM*), Livox columns the proposed extractor",{"slug":445,"group":446,"sourceId":5,"sourceLabel":6,"table":447,"selfRows":448,"metrics":449,"seqs":452,"entrants":466,"cells":476,"outcomes":517,"locators":518,"hardware":519,"wordings":520,"notes":521},"liliom2021-table-3","liliom2021:Table 3","Table 3",16,[450],{"label":451,"unit":148,"statistic":149,"alignment":149},"end-to-end position error",[453,456,459,462,464],{"dataset":395,"sequence":454,"environment":455},"Campus-1","Karlsruhe urban, backpack, 0.50 km, 1.43 m\u002Fs",{"dataset":395,"sequence":457,"environment":458},"Campus-2","Karlsruhe urban, backpack, 0.20 km, 1.57 m\u002Fs",{"dataset":395,"sequence":460,"environment":461},"Schloss-1","Karlsruhe urban, backpack, 0.65 km, 1.03 m\u002Fs",{"dataset":395,"sequence":396,"environment":463},"Karlsruhe urban, backpack, 1.10 km, 1.49 m\u002Fs",{"dataset":395,"sequence":398,"environment":465},"Karlsruhe urban, backpack, 3.70 km, 3.11 m\u002Fs, cycling",[467,469,471,472,474],{"name":468,"methodId":85,"linkable":76,"proposed":76,"self":76},"LiHo, Livox-Horizon-LOAM",{"name":470,"methodId":5,"linkable":183,"proposed":76,"self":183},"LiLi-OM-O (no loop closure)",{"name":7,"methodId":5,"linkable":183,"proposed":183,"self":183},{"name":473,"methodId":5,"linkable":183,"proposed":76,"self":183},"LiLi-OM-O, IMU removed",{"name":475,"methodId":5,"linkable":183,"proposed":76,"self":183},"LiLi-OM, IMU removed",[477,479,480,482,484,486,488,490,492,494,495,497,499,501,503,505,507,509,511,513,515],[195,195,195,478,197,195,197,197,195],1.47,[199,195,195,298,197,195,197,197,195],[206,195,195,481,197,195,197,197,195],0.13,[195,195,199,483,197,195,197,197,195],0.4,[199,195,199,485,197,195,197,197,195],0.21,[206,195,199,487,197,195,197,197,195],0.19,[195,195,206,489,197,195,197,197,195],1.55,[199,195,206,491,197,195,197,197,195],0.95,[206,195,206,493,197,195,197,197,195],0.15,[211,195,206,294,197,195,197,197,195],[216,195,206,496,197,195,197,197,195],0.24,[195,195,211,498,197,195,197,197,195],8.34,[199,195,211,500,197,195,197,197,195],4.41,[206,195,211,502,197,195,197,197,195],0.08,[211,195,211,504,197,195,197,197,195],5.58,[216,195,211,506,197,195,197,197,195],0.12,[195,195,216,508,197,195,197,197,195],109.62,[199,195,216,510,197,195,197,197,195],15.66,[206,195,216,512,197,195,197,197,195],1.28,[211,195,216,514,197,195,197,197,195],19.28,[216,195,216,516,197,195,197,197,195],3.43,[],[447],[],[],[522],"End-to-end position error on KA-Urban backpack sequences, end points registered from satellite images; LiLi-OM-O has loop closure disabled; 'IMU removed' variants drop all IMU constraints incl. de-skewing",{"slug":524,"group":525,"sourceId":526,"sourceLabel":527,"table":528,"selfRows":529,"metrics":530,"seqs":535,"entrants":582,"cells":595,"outcomes":727,"locators":729,"hardware":731,"wordings":732,"notes":733},"voxelslam2026-table-2-full-slam-with-lc","voxelslam2026:Table 2 (full SLAM with LC)","voxelslam2026","Liu et al., 2026","Table 2 (full SLAM with LC)",13,[531],{"label":532,"unit":533,"statistic":534,"alignment":149},"absolute trajectory error (RMSE, centimeters)","cm","RMSE",[536,540,543,546,550,554,558,562,566,570,573,576,579],{"dataset":537,"sequence":538,"environment":539},"Hilti handheld sequence exp01-construction (name per Table C1)","hilti01","construction environment (sequence named construction)",{"dataset":541,"sequence":542,"environment":539},"Hilti handheld sequence exp02-construction (name per Table C1)","hilti02",{"dataset":544,"sequence":545,"environment":539},"Hilti handheld sequence exp03-construction (name per Table C1)","hilti03",{"dataset":547,"sequence":548,"environment":549},"Hilti handheld sequence exp07-long-corridor (name per Table C1)","hilti04","long corridor",{"dataset":551,"sequence":552,"environment":553},"Hilti handheld sequence exp09-cupola (name per Table C1)","hilti05","cupola",{"dataset":555,"sequence":556,"environment":557},"Hilti handheld sequence exp11-lower-gallery (name per Table C1)","hilti06","lower gallery",{"dataset":559,"sequence":560,"environment":561},"Hilti handheld sequence exp15-upper-gallery (name per Table C1)","hilti07","upper gallery",{"dataset":563,"sequence":564,"environment":565},"Hilti handheld sequence exp21-outside (name per Table C1)","hilti08","outside",{"dataset":567,"sequence":568,"environment":569},"Hilti handheld sequence site1-handheld-1 (name per Table C1)","hilti09","construction site (same site for hilti09 to hilti13, Sec. 10.3.1)",{"dataset":571,"sequence":572,"environment":569},"Hilti handheld sequence site1-handheld-2 (name per Table C1)","hilti10",{"dataset":574,"sequence":575,"environment":569},"Hilti handheld sequence site1-handheld-3 (name per Table C1)","hilti11",{"dataset":577,"sequence":578,"environment":569},"Hilti handheld sequence site1-handheld-4 (name per Table C1)","hilti12",{"dataset":580,"sequence":581,"environment":569},"Hilti handheld sequence site1-handheld-5 (name per Table C1)","hilti13",[583,586,587,588,591,593],{"name":584,"methodId":585,"linkable":183,"proposed":76,"self":76},"LeGO-LOAM","legoloam2018",{"name":7,"methodId":5,"linkable":183,"proposed":76,"self":183},{"name":190,"methodId":191,"linkable":183,"proposed":76,"self":76},{"name":589,"methodId":590,"linkable":183,"proposed":76,"self":76},"LTA-OM","ltaom2024",{"name":592,"methodId":526,"linkable":183,"proposed":183,"self":76},"Our (Odom+LM+LC)",{"name":594,"methodId":526,"linkable":183,"proposed":183,"self":76},"Our (Full)",[596,598,600,601,602,603,605,606,608,610,613,616,619,621,623,624,625,627,628,629,630,632,634,635,637,639,640,642,644,645,647,648,650,651,653,655,657,659,661,663,665,667,669,671,673,674,676,677,679,680,682,684,685,687,688,690,692,693,695,697,698,700,702,703,704,706,708,709,710,712,714,716,718,719,720,721,723,725],[195,195,195,597,197,195,197,197,195],8.8,[195,195,199,599,197,195,197,197,195],39,[195,195,206,85,195,195,197,197,195],[195,195,211,424,197,195,197,197,195],[195,195,216,85,195,195,197,197,195],[195,195,221,604,197,195,197,197,195],67,[195,195,226,85,195,195,197,197,195],[195,195,231,607,197,195,197,197,195],22.7,[195,195,236,609,197,195,197,197,195],12.6,[195,195,611,612,197,195,197,197,195],9,12.9,[195,195,614,615,197,195,197,197,195],10,27.1,[195,195,617,618,197,195,197,197,195],11,16.2,[195,195,620,85,195,195,197,197,195],12,[199,195,195,622,197,195,197,197,195],6.2,[199,195,199,248,197,195,197,197,195],[199,195,206,85,195,195,197,197,195],[199,195,211,626,197,195,197,197,195],31,[199,195,216,85,195,195,197,197,195],[199,195,221,615,197,195,197,197,195],[199,195,226,85,195,195,197,197,195],[199,195,231,631,197,195,197,197,195],18.6,[199,195,236,633,197,195,197,197,195],6.9,[199,195,611,236,197,195,197,197,195],[199,195,614,636,197,195,197,197,195],19.9,[199,195,617,638,197,195,197,197,195],22.8,[199,195,620,85,195,195,197,197,195],[206,195,195,641,197,195,197,197,195],6.1,[206,195,199,643,197,195,197,197,195],10.1,[206,195,206,85,195,195,197,197,195],[206,195,211,646,197,195,197,197,195],23.4,[206,195,216,85,195,195,197,197,195],[206,195,221,649,197,195,197,197,195],13.4,[206,195,226,85,195,195,197,197,195],[206,195,231,652,197,195,197,197,195],17.2,[206,195,236,654,197,195,197,197,195],6.6,[206,195,611,656,197,195,197,197,195],5.5,[206,195,614,658,197,195,197,197,195],17.6,[206,195,617,660,197,195,197,197,195],12.5,[206,195,620,662,197,195,197,197,195],74,[211,195,195,664,197,195,197,197,195],1.27,[211,195,199,666,197,195,197,197,195],2.5,[211,195,206,668,197,195,197,197,195],33,[211,195,211,670,197,195,197,197,195],6.7,[211,195,216,672,197,195,197,197,195],40,[211,195,221,206,197,195,197,197,195],[211,195,226,675,197,195,197,197,195],65,[211,195,231,341,197,195,197,197,195],[211,195,236,678,197,195,197,197,195],2.4,[211,195,611,237,197,195,197,197,195],[211,195,614,681,197,195,197,197,195],4.1,[211,195,617,683,197,195,197,197,195],2.9,[211,195,620,448,197,195,197,197,195],[216,195,195,686,197,195,197,197,195],0.78,[216,195,199,292,197,195,197,197,195],[216,195,206,689,197,195,197,197,195],2.8,[216,195,211,691,197,195,197,197,195],3.4,[216,195,216,248,197,195,197,197,195],[216,195,221,694,197,195,197,197,195],0.9,[216,195,226,696,197,195,197,197,195],9.2,[216,195,231,694,197,195,197,197,195],[216,195,236,699,197,195,197,197,195],1.25,[216,195,611,701,197,195,197,197,195],1.4,[216,195,614,678,197,195,197,197,195],[216,195,617,701,197,195,197,197,195],[216,195,620,705,197,195,197,197,195],1.26,[221,195,195,707,197,195,197,197,195],0.62,[221,195,199,701,197,195,197,197,195],[221,195,206,683,197,195,197,197,195],[221,195,211,711,197,195,197,197,195],3.3,[221,195,216,713,197,195,197,197,195],13.8,[221,195,221,715,197,195,197,197,195],0.7,[221,195,226,717,197,195,197,197,195],7.8,[221,195,231,276,197,195,197,197,195],[221,195,236,199,197,195,197,197,195],[221,195,611,232,197,195,197,197,195],[221,195,614,722,197,195,197,197,195],1.3,[221,195,617,724,197,195,197,197,195],1.22,[221,195,620,726,197,195,197,197,195],0.8,[728],"failed (dash; text states LeGO-LOAM, LiLi-OM, LINS and LIO-SAM failed in these sequences)",[730],"Table 2",[],[],[734],"Hilti handheld sequences; ATE exported from the Hilti evaluation website; full SLAM with loop closure (Our (Full) adds global mapping)",[736,741,748,754,758,764,773,780,784,790,794,800,806,812,818,824,828],{"group":737,"slug":738,"sourceLabel":527,"table":739,"selfRows":529,"datasets":740},"voxelslam2026:Table 2 (odometry without LC)","voxelslam2026-table-2-odometry-without-lc","Table 2 (odometry without LC)",[537,541,544,547,551,555,559,563,567,571,574,577,580],{"group":742,"slug":743,"sourceLabel":744,"table":745,"selfRows":620,"datasets":746},"fastlio2_2022:Table IV","fastlio2-2022-table-iv","Xu et al., 2022","Table IV",[177,155,161,747],"UrbanLoco HK (ulhk)",{"group":749,"slug":750,"sourceLabel":751,"table":730,"selfRows":614,"datasets":752},"ltaom2024:Table 2","ltaom2024-table-2","Zou et al., 2024",[753],"MulRan",{"group":755,"slug":756,"sourceLabel":751,"table":447,"selfRows":614,"datasets":757},"ltaom2024:Table 3","ltaom2024-table-3",[155],{"group":759,"slug":760,"sourceLabel":744,"table":761,"selfRows":231,"datasets":762},"fastlio2_2022:Table V","fastlio2-2022-table-v","Table V",[177,763,747],"LiLi-OM dataset (lili)",{"group":765,"slug":766,"sourceLabel":767,"table":768,"selfRows":231,"datasets":769},"pointlio2023:Table 6","pointlio2023-table-6","He et al., 2023a","Table 6",[770,771,772],"lili","liosam","ulhk",{"group":774,"slug":775,"sourceLabel":776,"table":777,"selfRows":226,"datasets":778},"glio2024:Table I","glio2024-table-i","Liu et al., 2024","Table I",[779],"UrbanNav (Hong Kong)",{"group":781,"slug":782,"sourceLabel":776,"table":143,"selfRows":226,"datasets":783},"glio2024:Table II","glio2024-table-ii",[779],{"group":785,"slug":786,"sourceLabel":6,"table":787,"selfRows":226,"datasets":788},"liliom2021:Table 1","liliom2021-table-1","Table 1",[111,385,789],"UrbanNav",{"group":791,"slug":792,"sourceLabel":6,"table":730,"selfRows":226,"datasets":793},"liliom2021:Table 2","liliom2021-table-2",[388],{"group":795,"slug":796,"sourceLabel":767,"table":797,"selfRows":221,"datasets":798},"pointlio2023:Table 5","pointlio2023-table-5","Table 5",[771,772,799],"utbm",{"group":801,"slug":802,"sourceLabel":803,"table":777,"selfRows":211,"datasets":804},"fflins2023:Table I","fflins2023-table-i","Tang et al., 2023",[805],"LiLi-OM dataset",{"group":807,"slug":808,"sourceLabel":767,"table":809,"selfRows":206,"datasets":810},"pointlio2023:Table 7","pointlio2023-table-7","Table 7",[811],"12 public sequences (utbm, ulhk, liosam, lili)",{"group":813,"slug":814,"sourceLabel":744,"table":815,"selfRows":199,"datasets":816},"fastlio2_2022:Text Sec.VII-B1","fastlio2-2022-text-sec-vii-b1","Text Sec.VII-B1",[817],"private handheld dataset (Livox Avia)",{"group":819,"slug":820,"sourceLabel":821,"table":447,"selfRows":199,"datasets":822},"ghadimzadeh2025slamnde:Table 3","ghadimzadeh2025slamnde-table-3","Ghadimzadeh Alamdari et al., 2025",[823],"Luleå SubT tunnel dataset (Koval et al. 2022)",{"group":825,"slug":826,"sourceLabel":751,"table":797,"selfRows":199,"datasets":827},"ltaom2024:Table 5","ltaom2024-table-5",[753],{"group":829,"slug":830,"sourceLabel":751,"table":768,"selfRows":199,"datasets":831},"ltaom2024:Table 6","ltaom2024-table-6",[155],1790510653732]