[{"data":1,"prerenderedAt":1507},["ShallowReactive",2],{"method-fastlio2_2022":3},{"method":4,"reference":63,"equipment":86,"figures":163,"results":164},{"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":28,"sensors":36,"platform":39,"estimator":44,"association":45,"timeModel":46,"deskew":47,"loopClosure":48,"globalOptimization":49,"mapRepresentation":50,"prior":49,"outputGeometry":51,"compute":52,"codeUrl":53,"codeLicense":54,"relatedVersions":55},"fastlio2_2022","Xu et al., 2022","FAST-LIO2","FAST-LIO2: Fast Direct LiDAR-Inertial Odometry",2022,"recent","C05","odometry_with_local_mapping","FAST-LIO2 延續 FAST-LIO 的緊耦合迭代卡爾曼濾波，但取消手工特徵擷取，直接以原始點對地圖中局部平面做點到平面（point-to-plane）配準，使系統較不依賴特定 LiDAR 掃描樣式。地圖以作者提出的增量式 k-d 樹（ikd-Tree）維護，支援逐點插入、刪除、樹上降採樣與平行重建，因而可在里程計頻率同步更新稠密點雲地圖。地圖只保留一個邊長 L 的立方體區域內的點，該區域初始以起點為中心，並在 LiDAR 偵測範圍觸及邊界時移動；系統不含迴圈偵測或全域修正。","Direct (feature-free) tightly-coupled iterated-Kalman LIO that registers raw points point-to-plane against a dense map kept in an incremental k-d tree (ikd-Tree), supporting spinning and solid-state LiDARs in real time without loop closure.","full_text_reviewed","peer_reviewed_published","main_body","論文本身未在施工現場或以獨立幾何參考評估點雲；後續研究常以其為比較基準，例如 COIN-LIO 以其為基礎並在隧道等退化場景比較 [coinlio2024]，Voxel-SLAM 在動態起始條件下與之比較 [voxelslam2026]。",[20,21],"public_benchmark","controlled_experiment",[23,24,25,26,27],"Removing feature extraction makes the system adaptable to LiDARs with different scan patterns (abstract; Sec. VIII)","ikd-Tree gives the best overall kNN\u002Finsert\u002Fdelete performance among compared dynamic structures (octree, R-tree, nanoflann) in 18 sequences (Sec. VI-B)","Robust pose estimation in cluttered indoor scenes with rotation up to 1000 deg\u002Fs (abstract)","Authors report FAST-LIO2 or a variant best in 18 of 19 benchmark sequences, the exception being ulhk 4 where LILI-OM is slightly better (Sec. VI-C1; Table IV)","Total processing time about 8, 10 and 6 times lower than LILI-OM, LIO-SAM and LINS respectively (Sec. VI-D; Table VI)",[29,30,31,32,33,34,35],"Odometry only: no loop detection or correction (Sec. VI-C)","Only map points within a moving cube of side L are retained, so map revisits beyond this region are not re-associated (Sec. V-A) (inference)","In degenerate ENWIDE sequences FAST-LIO2 avoided divergence only where vegetation offered weak structure and still showed large drift (COIN-LIO, Sec. IV-C)","Diverged at start when initialized in strong motion because gravity is estimated from averaged acceleration (Voxel-SLAM arXiv v1, Sec. X-A)","Enlarging the map beyond 2000 m does not persistently improve accuracy because drift can cause false matches with old map points (Sec. VI-C1)","On the ARM board the per-scan time occasionally exceeds the 10 ms sampling period at 100 Hz (Sec. VII-B1)","Airborne mapping has no quantitative ground truth; GPS trajectories were unavailable and comparison was visual (Sec. VII-C)",[37,38],"3D LiDAR (solid-state Livox Horizon\u002FAvia and spinning Velodyne VLP-16\u002FHDL-32E in the tested datasets)","IMU",[40,41,42,43],"handheld","UAV","vehicle","wheeled UGV","tightly-coupled iterated Kalman filter on manifold (IKFOM toolbox) inherited from FAST-LIO, state includes LiDAR-IMU extrinsic (dimension 24)","direct: raw (downsampled) points registered without feature extraction; point-to-plane residual to a local plane fitted from 5 nearest map points found in ikd-Tree","discrete poses with per-point back-propagation","IMU forward\u002Fbackward propagation per point (inherited from FAST-LIO)","none (authors state FAST-LIO2 is an odometry without loop detection or correction, Sec. VI-C)","none","dense point map in an incremental k-d tree (ikd-Tree) with on-tree downsampling and box-wise deletion; the map region is a cube of side L initialized around the start position and moved when the LiDAR detection area reaches its border (default L = 1000 m)","odometry and registered dense point map inserted at odometry rate; export format not_reported in paper","CPU real time; benchmark on DJI Manifold 2-C (1.8 GHz quad-core Intel i7-8550U, 8 GB RAM) and Khadas VIM3 ARM board (2.2 GHz quad-core Cortex-A73, 4 GB RAM); 1.82 ms (Intel) and 5.23 ms (ARM) mean total per scan on a 100 Hz handheld sequence; benchmark per-scan totals 11.47 to 31.56 ms on Intel with the 1000 m map","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FFAST_LIO","GPL-2.0 (LICENSE file checked); ikd-Tree repository GPL-2.0",[56,60],{"relation":57,"title":58,"doi_or_url":59},"preprint","FAST-LIO2: Fast Direct LiDAR-inertial Odometry (arXiv v1)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.06829",{"relation":61,"title":62,"doi_or_url":53},"code_release","hku-mars\u002FFAST_LIO (FAST-LIO2) and hku-mars\u002Fikd-Tree",{"id":5,"kind":64,"shortName":7,"title":8,"authors":65,"year":9,"venue":71,"venueType":72,"publisher":73,"volumeIssuePages":74,"doi":75,"arxivId":76,"url":77,"firstPublicDate":78,"publicationStatus":16,"metadataStatus":79,"fulltextStatus":15,"era":10,"classicReason":80,"codeUrl":53,"cluster":11,"topics":81,"mdpi":82,"verification":83,"label":6,"fulltextRoute":84,"versionRead":85,"addedByCensus":82},"method",[66,67,68,69,70],"Wei Xu","Yixi Cai","Dongjiao He","Jiarong Lin","Fu Zhang","IEEE Transactions on Robotics","journal","IEEE","38(4):2053-2073","10.1109\u002Ftro.2022.3141876","2107.06829","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FTRO.2022.3141876","2021-07-14","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v1 (2021-07-14) preprint; IEEE T-RO 38(4):2053-2073 version of record not read, so table values may differ from the published version",[87,94,98,103,107,111,117,124,127,133,136,140,143,147,151,154,157,160],{"category":88,"model":89,"canonical":89,"role":90,"dataset":91,"specs":92,"locator":93},"lidar","Livox Avia","method input",null,"solid-state, 70.4 deg (H) x 77.2 deg (V) circular FoV, non-repetitive scan pattern, built-in IMU; scan rate 100 Hz unless stated (10 Hz in aerial test)","Sec. VII-A",{"category":95,"model":96,"canonical":96,"role":90,"dataset":91,"specs":97,"locator":93},"imu","BMI088 (built into Livox Avia)","built-in IMU of Livox Avia",{"category":99,"model":100,"canonical":100,"role":90,"dataset":91,"specs":101,"locator":102},"platform","280 mm wheelbase quadrotor UAV","forward-looking Livox Avia, indoor aggressive flight","Fig. 6(a); Sec. VII-A",{"category":99,"model":104,"canonical":104,"role":90,"dataset":91,"specs":105,"locator":106},"handheld platform","Livox Avia with DJI Manifold 2-C","Fig. 6(b); Sec. VII-A",{"category":99,"model":108,"canonical":108,"role":90,"dataset":91,"specs":109,"locator":110},"750 mm wheelbase quadrotor UAV (developed by Ambit-Geospatial)","down-facing Livox Avia, GPS-navigated waypoint flight","Fig. 6(c); Sec. VII-A; Sec. VII-C",{"category":112,"model":113,"canonical":113,"role":114,"dataset":91,"specs":115,"locator":116},"gnss","UAV onboard GPS\u002FIMU navigation (model not reported)","reference or ground truth","used only for UAV navigation, not by FAST-LIO2; trajectories compared visually, GPS trajectories not available for quantitative evaluation","Sec. VII-C",{"category":118,"model":119,"canonical":120,"role":121,"dataset":91,"specs":122,"locator":123},"compute","DJI Manifold 2-C","DJI Manifold 2C","compute for runtime","1.8 GHz quad-core Intel i7-8550U CPU, 8 GB RAM","Sec. VI-A",{"category":118,"model":125,"canonical":125,"role":121,"dataset":91,"specs":126,"locator":123},"Khadas VIM3","2.2 GHz quad-core Cortex-A73 CPU, 4 GB RAM",{"category":88,"model":128,"canonical":128,"role":129,"dataset":130,"specs":131,"locator":132},"Livox Horizon","dataset sensor","LiLi-OM dataset (lili)","solid-state, non-repetitive, 81.7 deg (H) x 25.1 deg (V) FoV, 10 Hz","Sec. VI; Table II",{"category":95,"model":134,"canonical":134,"role":129,"dataset":130,"specs":135,"locator":132},"Xsens MTi-670","6-axis, 200 Hz",{"category":88,"model":137,"canonical":137,"role":129,"dataset":138,"specs":139,"locator":132},"Velodyne VLP-16","LIO-SAM dataset (liosam)","16 lines, 10 Hz",{"category":95,"model":141,"canonical":141,"role":129,"dataset":138,"specs":142,"locator":132},"MicroStrain 3DM-GX5-25","9-axis, 1000 Hz",{"category":88,"model":144,"canonical":144,"role":129,"dataset":145,"specs":146,"locator":132},"Velodyne HDL-32E","UTBM robocar dataset (utbm)","two units at 10 Hz on a human-driven robocar (max 50 km\u002Fh); only the left LiDAR used",{"category":95,"model":148,"canonical":149,"role":129,"dataset":145,"specs":150,"locator":132},"Xsens MTi-28A53G25","Xsens MTi-28","6-axis, 100 Hz",{"category":88,"model":144,"canonical":144,"role":129,"dataset":152,"specs":153,"locator":132},"UrbanLoco HK (ulhk)","10 Hz, human-driven vehicle",{"category":95,"model":155,"canonical":155,"role":129,"dataset":152,"specs":156,"locator":132},"Xsens MTi-10","9-axis, 100 Hz",{"category":88,"model":144,"canonical":144,"role":129,"dataset":158,"specs":159,"locator":132},"NCLT","10 Hz, UGV",{"category":95,"model":161,"canonical":161,"role":129,"dataset":158,"specs":162,"locator":132},"Microstrain MS25","9-axis, 50 Hz (interpolated to 100 Hz for LIO-SAM)",[],{"totalRows":165,"groupCount":166,"groups":167,"others":968},907,99,[168,398,553,733],{"slug":169,"group":170,"sourceId":171,"sourceLabel":172,"table":173,"selfRows":174,"metrics":175,"seqs":195,"entrants":212,"cells":218,"outcomes":392,"locators":393,"hardware":394,"wordings":395,"notes":396},"hu2025mapeval-table-v","hu2025mapeval:Table V","hu2025mapeval","Hu et al., 2025","Table V",42,[176,180,184,186,189,191,193],{"label":177,"unit":178,"statistic":179,"alignment":179},"ATE (computed with evo [5]; statistic not stated)","cm","not_reported",{"label":181,"unit":178,"statistic":182,"alignment":183},"AC (mean point-to-point error of correspondences within tau = 0.2 m)","mean","SE3",{"label":185,"unit":178,"statistic":182,"alignment":183},"CD (sum of the two directed mean closest-point distances, Eq. 4)",{"label":187,"unit":188,"statistic":179,"alignment":183},"COM (|C_tau| \u002F N_g)","unitless as printed (0 to 100 scale)",{"label":190,"unit":49,"statistic":182,"alignment":49},"MME (mean map entropy, search radius 0.1 m; lower is better)",{"label":192,"unit":178,"statistic":182,"alignment":183},"AWD (voxelized average Wasserstein distance, voxel 3.0 m; proposed metric)",{"label":194,"unit":49,"statistic":182,"alignment":183},"SCS (spatial consistency score, voxel 3.0 m; proposed metric; lower is better)",[196,200,202,204,206,208],{"dataset":197,"sequence":198,"environment":199},"FusionPortable","S5 (MCR slow)","Room",{"dataset":197,"sequence":201,"environment":199},"S7 (MCR slow 00)",{"dataset":197,"sequence":203,"environment":199},"S8 (MCR slow 01)",{"dataset":197,"sequence":205,"environment":199},"S9 (MCR normal 00)",{"dataset":197,"sequence":207,"environment":199},"S10 (MCR normal 01)",{"dataset":209,"sequence":210,"environment":211},"MS-dataset (authors, self-collected)","S14 (PK1)","Parking lot (outdoor)",[213,216],{"name":214,"methodId":5,"linkable":215,"proposed":82,"self":215},"FAST-LIO2 (FL2) [2]",true,{"name":217,"methodId":91,"linkable":82,"proposed":82,"self":82},"PALoc [10] (loop closure and prior-map constraints)",[219,223,226,229,232,235,238,240,242,244,246,248,250,252,254,256,258,260,262,264,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,318,320,322,324,326,328,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,370,372,374,376,378,380,382,384,386,388,390],[220,220,220,221,222,220,222,222,220],0,14.24,-1,[220,220,224,225,222,220,222,222,220],1,3.66,[220,220,227,228,222,220,222,222,220],2,5.66,[220,220,230,231,222,220,222,222,220],3,9.33,[220,220,233,234,222,220,222,222,220],4,5.53,[220,220,236,237,222,220,222,222,220],5,33.36,[224,220,220,239,222,220,222,222,220],13.03,[224,220,224,241,222,220,222,222,220],3.83,[224,220,227,243,222,220,222,222,220],6.15,[224,220,230,245,222,220,222,222,220],8.89,[224,220,233,247,222,220,222,222,220],5.51,[224,220,236,249,222,220,222,222,220],28.56,[220,224,220,251,222,220,222,222,220],5.77,[220,224,224,253,222,220,222,222,220],3.11,[220,224,227,255,222,220,222,222,220],3.29,[220,224,230,257,222,220,222,222,220],5.05,[220,224,233,259,222,220,222,222,220],3.12,[220,224,236,261,222,220,222,222,220],8.12,[224,224,220,263,222,220,222,222,220],5.86,[224,224,224,253,222,220,222,222,220],[224,224,227,266,222,220,222,222,220],3.24,[224,224,230,268,222,220,222,222,220],4.83,[224,224,233,270,222,220,222,222,220],3.1,[224,224,236,272,222,220,222,222,220],5.02,[220,227,220,274,222,220,222,222,220],10.28,[220,227,224,276,222,220,222,222,220],16.65,[220,227,227,278,222,220,222,222,220],14.14,[220,227,230,280,222,220,222,222,220],13.73,[220,227,233,282,222,220,222,222,220],12.23,[220,227,236,284,222,220,222,222,220],97.3,[224,227,220,286,222,220,222,222,220],10.02,[224,227,224,288,222,220,222,222,220],16.6,[224,227,227,290,222,220,222,222,220],14.18,[224,227,230,292,222,220,222,222,220],13.88,[224,227,233,294,222,220,222,222,220],12.31,[224,227,236,296,222,220,222,222,220],97.15,[220,230,220,298,222,220,222,222,220],90.46,[220,230,224,300,222,220,222,222,220],97.25,[220,230,227,302,222,220,222,222,220],97.34,[220,230,230,304,222,220,222,222,220],96.17,[220,230,233,306,222,220,222,222,220],97.29,[220,230,236,308,222,220,222,222,220],75,[224,230,220,310,222,220,222,222,220],91.12,[224,230,224,312,222,220,222,222,220],97.26,[224,230,227,314,222,220,222,222,220],97.37,[224,230,230,316,222,220,222,222,220],96.47,[224,230,233,306,222,220,222,222,220],[224,230,236,319,222,220,222,222,220],91.25,[220,233,220,321,222,220,222,222,220],-8.08,[220,233,224,323,222,220,222,222,220],-8.87,[220,233,227,325,222,220,222,222,220],-8.66,[220,233,230,327,222,220,222,222,220],-8.24,[220,233,233,323,222,220,222,222,220],[220,233,236,330,222,220,222,222,220],-8.81,[224,233,220,332,222,220,222,222,220],-8.07,[224,233,224,334,222,220,222,222,220],-8.86,[224,233,227,336,222,220,222,222,220],-8.67,[224,233,230,338,222,220,222,222,220],-8.26,[224,233,233,340,222,220,222,222,220],-8.89,[224,233,236,342,222,220,222,222,220],-8.71,[220,236,220,344,222,220,222,222,220],48.26,[220,236,224,346,222,220,222,222,220],50.2,[220,236,227,348,222,220,222,222,220],47.18,[220,236,230,350,222,220,222,222,220],48.27,[220,236,233,352,222,220,222,222,220],46.57,[220,236,236,354,222,220,222,222,220],40.2,[224,236,220,356,222,220,222,222,220],48.14,[224,236,224,358,222,220,222,222,220],50.26,[224,236,227,360,222,220,222,222,220],47.38,[224,236,230,362,222,220,222,222,220],48.12,[224,236,233,364,222,220,222,222,220],46.46,[224,236,236,366,222,220,222,222,220],31.42,[220,368,220,369,222,220,222,222,220],6,69.23,[220,368,224,371,222,220,222,222,220],78.94,[220,368,227,373,222,220,222,222,220],69.58,[220,368,230,375,222,220,222,222,220],75.92,[220,368,233,377,222,220,222,222,220],71.67,[220,368,236,379,222,220,222,222,220],72.34,[224,368,220,381,222,220,222,222,220],69.39,[224,368,224,383,222,220,222,222,220],78.75,[224,368,227,385,222,220,222,222,220],69.45,[224,368,230,387,222,220,222,222,220],75.6,[224,368,233,389,222,220,222,222,220],71.49,[224,368,236,391,222,220,222,222,220],87.82,[],[173],[],[],[397],"Map metrics and ATE for FAST-LIO2 and PALoc maps against TLS or high-precision ground-truth maps (FusionPortable MCR room sequences and MS-dataset parking lot); estimated map registered to the GT map by point-to-plane ICP (SE(3)), tau = 0.2 m, voxel 3.0 m, MME radius 0.1 m; values identical in arXiv v2 and the RA-L version of record",{"slug":399,"group":400,"sourceId":5,"sourceLabel":6,"table":401,"selfRows":402,"metrics":403,"seqs":407,"entrants":453,"cells":458,"outcomes":545,"locators":546,"hardware":547,"wordings":550,"notes":551},"fastlio2-2022-table-vi","fastlio2_2022:Table VI","Table VI",38,[404],{"label":405,"unit":406,"statistic":182,"alignment":80},"average processing time per scan, Total","ms",[408,411,413,415,419,421,423,426,428,430,433,435,437,439,441,443,445,449,451],{"dataset":130,"sequence":409,"environment":410},"lili 6","campus and urban streets (Livox Horizon)",{"dataset":130,"sequence":412,"environment":410},"lili 7",{"dataset":130,"sequence":414,"environment":410},"lili 8",{"dataset":416,"sequence":417,"environment":418},"UTBM robocar dataset","utbm 8","human-driven robocar, urban, up to 50 km\u002Fh",{"dataset":416,"sequence":420,"environment":418},"utbm 9",{"dataset":416,"sequence":422,"environment":418},"utbm 10",{"dataset":152,"sequence":424,"environment":425},"ulhk 4","human-driven vehicle, urban with moving vehicles",{"dataset":152,"sequence":427,"environment":425},"ulhk 5",{"dataset":152,"sequence":429,"environment":425},"ulhk 6",{"dataset":158,"sequence":431,"environment":432},"nclt 4","UGV, University of Michigan North Campus",{"dataset":158,"sequence":434,"environment":432},"nclt 5",{"dataset":158,"sequence":436,"environment":432},"nclt 6",{"dataset":158,"sequence":438,"environment":432},"nclt 7",{"dataset":158,"sequence":440,"environment":432},"nclt 8",{"dataset":158,"sequence":442,"environment":432},"nclt 9",{"dataset":158,"sequence":444,"environment":432},"nclt 10",{"dataset":446,"sequence":447,"environment":448},"LIO-SAM dataset","liosam 1","MIT campus (LIO-SAM data)",{"dataset":446,"sequence":450,"environment":448},"liosam 2",{"dataset":446,"sequence":452,"environment":448},"liosam 3",[454,456],{"name":455,"methodId":5,"linkable":215,"proposed":215,"self":215},"FAST-LIO2 (1000)",{"name":457,"methodId":5,"linkable":215,"proposed":215,"self":215},"FAST-LIO2 (ARM)",[459,461,463,465,467,469,471,473,475,477,479,481,483,485,486,489,491,494,496,499,501,503,505,508,510,513,515,518,520,523,525,528,530,533,535,538,540,543],[220,220,220,460,222,220,220,222,220],12.56,[224,220,220,462,222,220,224,222,220],45.58,[220,220,224,464,222,220,220,222,220],17.61,[224,220,224,466,222,220,224,222,220],65.89,[220,220,227,468,222,220,220,222,220],15.31,[224,220,227,470,222,220,224,222,220],57.29,[220,220,230,472,222,220,220,222,220],22.05,[224,220,230,474,222,220,224,222,220],100,[220,220,233,476,222,220,220,222,220],25.44,[224,220,233,478,222,220,224,222,220],91.05,[220,220,236,480,222,220,220,222,220],22.48,[224,220,236,482,222,220,224,222,220],94.62,[220,220,368,484,222,220,220,222,220],20.14,[224,220,368,310,222,220,224,222,220],[220,220,487,488,222,220,220,222,220],7,23.9,[224,220,487,490,222,220,224,222,220],68.04,[220,220,492,493,222,220,220,222,220],8,31.56,[224,220,492,495,222,220,224,222,220],92.38,[220,220,497,498,222,220,220,222,220],9,15.72,[224,220,497,500,222,220,224,222,220],69.09,[220,220,502,288,222,220,220,222,220],10,[224,220,502,504,222,220,224,222,220],68.95,[220,220,506,507,222,220,220,222,220],11,15.84,[224,220,506,509,222,220,224,222,220],66.64,[220,220,511,512,222,220,220,222,220],12,16.87,[224,220,511,514,222,220,224,222,220],70.24,[220,220,516,517,222,220,220,222,220],13,14.25,[224,220,516,519,222,220,224,222,220],57.03,[220,220,521,522,222,220,220,222,220],14,13.65,[224,220,521,524,222,220,224,222,220],54.82,[220,220,526,527,222,220,220,222,220],15,21.79,[224,220,526,529,222,220,224,222,220],89.65,[220,220,531,532,222,220,220,222,220],16,14.77,[224,220,531,534,222,220,224,222,220],60.6,[220,220,536,537,222,220,220,222,220],17,11.47,[224,220,536,539,222,220,224,222,220],45.27,[220,220,541,542,222,220,220,222,220],18,16.64,[224,220,541,544,222,220,224,222,220],44.26,[],[401],[548,549],"DJI Manifold 2-C (1.8 GHz quad-core Intel i7-8550U, 8 GB RAM)","Khadas VIM3 (2.2 GHz quad-core Cortex-A73, 4 GB RAM)",[],[552],"Average total processing time per scan (odometry plus mapping) of FAST-LIO2 with 1000 m map; competitor Odo.\u002FMap. columns (LILI-OM, LIO-SAM, LINS) and map-size variants omitted for row cap",{"slug":554,"group":555,"sourceId":171,"sourceLabel":172,"table":401,"selfRows":556,"metrics":557,"seqs":564,"entrants":583,"cells":586,"outcomes":727,"locators":728,"hardware":729,"wordings":730,"notes":731},"hu2025mapeval-table-vi","hu2025mapeval:Table 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(parkland0)","Trees",[584,585],{"name":214,"methodId":5,"linkable":215,"proposed":82,"self":215},{"name":217,"methodId":91,"linkable":82,"proposed":82,"self":82},[587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,669,670,672,673,675,677,679,680,682,684,686,688,690,692,694,696,697,699,701,703,705,707,709,711,713,715,717,719,721,723,725],[220,220,220,588,222,220,222,222,220],7.53,[220,220,224,590,222,220,222,222,220],4.51,[220,220,227,592,222,220,222,222,220],5.3,[220,220,230,594,222,220,222,222,220],6.06,[220,220,233,596,222,220,222,222,220],6.81,[220,220,236,598,222,220,222,222,220],6.4,[224,220,220,600,222,220,222,222,220],4.04,[224,220,224,602,222,220,222,222,220],4.53,[224,220,227,604,222,220,222,222,220],5.09,[224,220,230,606,222,220,222,222,220],4.23,[224,220,233,608,222,220,222,222,220],5.4,[224,220,236,610,222,220,222,222,220],4.7,[220,224,220,612,222,220,222,222,220],41.97,[220,224,224,614,222,220,222,222,220],209.2,[220,224,227,616,222,220,222,222,220],111.3,[220,224,230,618,222,220,222,222,220],363.9,[220,224,233,620,222,220,222,222,220],30.05,[220,224,236,622,222,220,222,222,220],898.7,[224,224,220,624,222,220,222,222,220],25.02,[224,224,224,626,222,220,222,222,220],207.8,[224,224,227,628,222,220,222,222,220],112.9,[224,224,230,630,222,220,222,222,220],58.76,[224,224,233,632,222,220,222,222,220],27.03,[224,224,236,634,222,220,222,222,220],498.3,[220,227,220,636,222,220,222,222,220],77.13,[220,227,224,638,222,220,222,222,220],82.63,[220,227,227,640,222,220,222,222,220],90.55,[220,227,230,642,222,220,222,222,220],28.69,[220,227,233,644,222,220,222,222,220],92.67,[220,227,236,646,222,220,222,222,220],24.41,[224,227,220,648,222,220,222,222,220],89.9,[224,227,224,650,222,220,222,222,220],82.69,[224,227,227,652,222,220,222,222,220],93.33,[224,227,230,654,222,220,222,222,220],94.23,[224,227,233,656,222,220,222,222,220],93.54,[224,227,236,658,222,220,222,222,220],96.06,[220,230,220,660,222,220,222,222,220],-8.82,[220,230,224,662,222,220,222,222,220],-8.76,[220,230,227,664,222,220,222,222,220],-8.4,[220,230,230,666,222,220,222,222,220],-8.69,[220,230,233,668,222,220,222,222,220],-8.78,[220,230,236,668,222,220,222,222,220],[224,230,220,671,222,220,222,222,220],-8.65,[224,230,224,336,222,220,222,222,220],[224,230,227,674,222,220,222,222,220],-8.34,[224,230,230,676,222,220,222,222,220],-8.61,[224,230,233,678,222,220,222,222,220],-8.74,[224,230,236,671,222,220,222,222,220],[220,233,220,681,222,220,222,222,220],43.67,[220,233,224,683,222,220,222,222,220],47.75,[220,233,227,685,222,220,222,222,220],48.85,[220,233,230,687,222,220,222,222,220],115.8,[220,233,233,689,222,220,222,222,220],36.97,[220,233,236,691,222,220,222,222,220],105.3,[224,233,220,693,222,220,222,222,220],36.55,[224,233,224,695,222,220,222,222,220],48.07,[224,233,227,356,222,220,222,222,220],[224,233,230,698,222,220,222,222,220],43.17,[224,233,233,700,222,220,222,222,220],36.29,[224,233,236,702,222,220,222,222,220],31.64,[220,236,220,704,222,220,222,222,220],72.43,[220,236,224,706,222,220,222,222,220],84.43,[220,236,227,708,222,220,222,222,220],86.65,[220,236,230,710,222,220,222,222,220],57.64,[220,236,233,712,222,220,222,222,220],88.62,[220,236,236,714,222,220,222,222,220],64.69,[224,236,220,716,222,220,222,222,220],82.74,[224,236,224,718,222,220,222,222,220],85.6,[224,236,227,720,222,220,222,222,220],88.17,[224,236,230,722,222,220,222,222,220],87.41,[224,236,233,724,222,220,222,222,220],89.86,[224,236,236,726,222,220,222,222,220],91.46,[],[401],[],[],[732],"Map metrics for FAST-LIO2 and PALoc maps in larger scenes (FusionPortable corridor, canteen, escalator, building; Newer College math easy and parkland0); same registration and parameters as Table V; values identical in arXiv v2 and the RA-L version of record",{"slug":734,"group":735,"sourceId":736,"sourceLabel":737,"table":738,"selfRows":739,"metrics":740,"seqs":753,"entrants":776,"cells":783,"outcomes":961,"locators":963,"hardware":964,"wordings":965,"notes":966},"lioekf2024-table-i","lioekf2024:Table I","lioekf2024","Wu et al., 2024a","Table I",32,[741,744,747,750],{"label":742,"unit":743,"statistic":182,"alignment":179},"Avg. tra. (KITTI relative translation error)","%",{"label":745,"unit":746,"statistic":182,"alignment":179},"Avg. rot. (KITTI relative rotation error)","deg\u002Fm (as printed)",{"label":748,"unit":749,"statistic":179,"alignment":179},"ATE. tra. (unit taken as m from the column name; the Table I footnote lists the two ATE units in swapped order)","m",{"label":751,"unit":752,"statistic":179,"alignment":179},"ATE. rot. (unit taken as deg from the column name; the Table I footnote lists the two ATE units in swapped order)","deg",[754,758,760,762,766,768,770,774],{"dataset":755,"sequence":756,"environment":757},"UrbanNav","20210517","urban driving, Hong Kong",{"dataset":755,"sequence":759,"environment":757},"20210518",{"dataset":755,"sequence":761,"environment":757},"20210521",{"dataset":763,"sequence":764,"environment":765},"M2DGR","street 01-05 (average)","campus streets, wheeled robot",{"dataset":763,"sequence":767,"environment":765},"street 06",{"dataset":763,"sequence":769,"environment":765},"street 08",{"dataset":771,"sequence":772,"environment":773},"Newer College Dataset","short exp","handheld, Oxford college",{"dataset":771,"sequence":775,"environment":773},"long exp",[777,778,781],{"name":7,"methodId":5,"linkable":215,"proposed":82,"self":215},{"name":779,"methodId":780,"linkable":215,"proposed":82,"self":82},"LIO-SAM","liosam2020",{"name":782,"methodId":736,"linkable":215,"proposed":215,"self":82},"LIO-EKF",[784,786,788,790,792,794,796,798,800,802,804,806,807,809,811,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,846,848,850,852,854,855,857,858,860,862,864,866,868,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,902,904,906,908,910,912,914,916,918,920,922,924,926,928,930,931,932,933,934,936,938,940,942,944,946,948,950,951,952,953,954,956,958,959],[220,220,220,785,222,220,222,222,220],4.11,[220,224,220,787,222,220,222,222,220],1.68,[220,227,220,789,222,220,222,222,220],17.62,[220,230,220,791,222,220,222,222,220],4.45,[224,220,220,793,222,220,222,222,220],3.18,[224,224,220,795,222,220,222,222,220],1.4,[224,227,220,797,222,220,222,222,220],20.74,[224,230,220,799,222,220,222,222,220],4.2,[227,220,220,801,222,220,222,222,220],3.2,[227,224,220,803,222,220,222,222,220],1.45,[227,227,220,805,222,220,222,222,220],24.73,[227,230,220,257,222,220,222,222,220],[220,220,224,808,222,220,222,222,220],2.73,[220,224,224,810,222,220,222,222,220],1.3,[220,227,224,812,222,220,222,222,220],23.02,[220,230,224,814,222,220,222,222,220],3.27,[224,220,224,816,222,220,222,222,220],2.52,[224,224,224,818,222,220,222,222,220],1.31,[224,227,224,820,222,220,222,222,220],20.37,[224,230,224,822,222,220,222,222,220],2.98,[227,220,224,824,222,220,222,222,220],2.2,[227,224,224,826,222,220,222,222,220],1.14,[227,227,224,828,222,220,222,222,220],22.44,[227,230,224,830,222,220,222,222,220],7.46,[220,220,227,832,222,220,222,222,220],3.56,[220,224,227,834,222,220,222,222,220],1.63,[220,227,227,836,222,220,222,222,220],47.29,[220,230,227,838,222,220,222,222,220],5.18,[224,220,227,840,222,220,222,222,220],2.94,[224,224,227,842,222,220,222,222,220],1.62,[224,227,227,844,222,220,222,222,220],30.98,[224,230,227,590,222,220,222,222,220],[227,220,227,847,222,220,222,222,220],2.96,[227,224,227,849,222,220,222,222,220],1.54,[227,227,227,851,222,220,222,222,220],34.97,[227,230,227,853,222,220,222,222,220],4.47,[220,220,230,842,222,220,222,222,220],[220,224,230,856,222,220,222,222,220],0.79,[220,227,230,257,222,220,222,222,220],[220,230,230,859,222,220,222,222,220],1.79,[224,220,230,861,222,220,222,222,220],3.15,[224,224,230,863,222,220,222,222,220],1.52,[224,227,230,865,222,220,222,222,220],10.19,[224,230,230,867,222,220,222,222,220],4.27,[227,220,230,787,222,220,222,222,220],[227,224,230,870,222,220,222,222,220],0.83,[227,227,230,872,222,220,222,222,220],5.33,[227,230,230,874,222,220,222,222,220],1.7,[220,220,233,876,222,220,222,222,220],3.41,[220,224,233,878,222,220,222,222,220],1.55,[220,227,233,880,222,220,222,222,220],8.93,[220,230,233,882,222,220,222,222,220],2.29,[224,220,233,884,222,220,222,222,220],3.65,[224,224,233,886,222,220,222,222,220],1.65,[224,227,233,888,222,220,222,222,220],9.04,[224,230,233,890,222,220,222,222,220],2.41,[227,220,233,892,222,220,222,222,220],3.37,[227,224,233,894,222,220,222,222,220],1.56,[227,227,233,896,222,220,222,222,220],9.05,[227,230,233,898,222,220,222,222,220],2.27,[220,220,236,900,222,220,222,222,220],1.1,[220,224,236,886,222,220,222,222,220],[220,227,236,903,222,220,222,222,220],2.12,[220,230,236,905,222,220,222,222,220],1.71,[224,220,236,907,222,220,222,222,220],3.73,[224,224,236,909,222,220,222,222,220],5.78,[224,227,236,911,222,220,222,222,220],4.21,[224,230,236,913,222,220,222,222,220],6.36,[227,220,236,915,222,220,222,222,220],1.28,[227,224,236,917,222,220,222,222,220],1.85,[227,227,236,919,222,220,222,222,220],2.22,[227,230,236,921,222,220,222,222,220],1.88,[220,220,368,923,222,220,222,222,220],1.05,[220,224,368,925,222,220,222,222,220],1.01,[220,227,368,927,222,220,222,222,220],5.14,[220,230,368,929,222,220,222,222,220],2.49,[224,220,368,91,220,220,222,222,220],[224,224,368,91,220,220,222,222,220],[224,227,368,91,220,220,222,222,220],[224,230,368,91,220,220,222,222,220],[227,220,368,935,222,220,222,222,220],0.63,[227,224,368,937,222,220,222,222,220],0.73,[227,227,368,939,222,220,222,222,220],4.16,[227,230,368,941,222,220,222,222,220],1.75,[220,220,487,943,222,220,222,222,220],1.09,[220,224,487,945,222,220,222,222,220],1.33,[220,227,487,947,222,220,222,222,220],6.33,[220,230,487,949,222,220,222,222,220],4.22,[224,220,487,91,220,220,222,222,220],[224,224,487,91,220,220,222,222,220],[224,227,487,91,220,220,222,222,220],[224,230,487,91,220,220,222,222,220],[227,220,487,955,222,220,222,222,220],0.74,[227,224,487,957,222,220,222,222,220],0.91,[227,227,487,927,222,220,222,222,220],[227,230,487,960,222,220,222,222,220],2.34,[962],"not_run",[738],[],[],[967],"Default parameters for FAST-LIO2 and LIO-SAM, LIO-SAM loop closure disabled; one LIO-EKF configuration for all data; KITTI relative errors and ATE; LIO-SAM not run on Newer College (needs IMU attitude)",[969,976,982,988,995,1003,1009,1015,1019,1025,1030,1037,1045,1051,1056,1062,1069,1076,1080,1087,1106,1112,1118,1123,1128,1133,1139,1146,1152,1157,1163,1169,1175,1182,1187,1193,1199,1204,1211,1217,1221,1227,1232,1236,1244,1249,1253,1261,1266,1272,1277,1282,1289,1294,1299,1305,1310,1315,1320,1325,1329,1334,1338,1343,1347,1352,1356,1360,1366,1371,1375,1381,1385,1389,1394,1400,1406,1410,1416,1422,1426,1433,1438,1444,1449,1455,1460,1465,1470,1476,1481,1487,1492,1496,1500],{"group":970,"slug":971,"sourceLabel":972,"table":973,"selfRows":974,"datasets":975},"r3livepp2024:Table III","r3livepp2024-table-iii","Lin & Zhang, 2024","Table III",26,[158],{"group":977,"slug":978,"sourceLabel":6,"table":979,"selfRows":980,"datasets":981},"fastlio2_2022:Table IV","fastlio2-2022-table-iv","Table IV",24,[446,158,416,152],{"group":983,"slug":984,"sourceLabel":985,"table":973,"selfRows":980,"datasets":986},"r3live2022:Table III","r3live2022-table-iii","Lin & Zhang, 2022",[987],"R3LIVE Experiment-3 (authors' data, D-GPS RTK)",{"group":989,"slug":990,"sourceLabel":991,"table":992,"selfRows":993,"datasets":994},"loglio2024:Table II","loglio2024-table-ii","Huang et al., 2024b","Table II",22,[763],{"group":996,"slug":997,"sourceLabel":998,"table":992,"selfRows":999,"datasets":1000},"palvio2026:Table II","palvio2026-table-ii","Tang et al., 2026",21,[1001,1002],"MARS-LVIG","i2Nav-Robot",{"group":1004,"slug":1005,"sourceLabel":991,"table":973,"selfRows":1006,"datasets":1007},"loglio2024:Table III","loglio2024-table-iii",19,[1008],"NTU VIRAL",{"group":1010,"slug":1011,"sourceLabel":1012,"table":738,"selfRows":541,"datasets":1013},"fasterlio2022:Table I","fasterlio2022-table-i","Bai et al., 2022",[446,158,1014,416],"ULHK 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