[{"data":1,"prerenderedAt":572},["ShallowReactive",2],{"method-rkolio2026":3},{"method":4,"reference":61,"equipment":83,"figures":162,"results":163},{"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":34,"platform":37,"estimator":43,"association":44,"timeModel":45,"deskew":46,"loopClosure":47,"globalOptimization":47,"mapRepresentation":48,"prior":47,"outputGeometry":49,"compute":50,"codeUrl":51,"codeLicense":52,"relatedVersions":53},"rkolio2026","Malladi et al., 2026","RKO-LIO","Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling",2026,"recent","C05","odometry_with_local_mapping","RKO-LIO 不採用卡爾曼濾波或預積分因子圖，而是假設相鄰 LiDAR 幀間線加速度與角速度固定，以簡化模型積分 IMU 取得 ICP 初值與逐點去畸變，再以掃描對地圖 ICP 精修。作者在 ICP 中加入依 IMU 加速度資訊自適應調整的姿態正則化，並主張不需感測器特定的雜訊模型與校正，即可用同一組參數跨車載、背包、四足與無人機平台。","LiDAR-inertial odometry without filtering or preintegration: a simple constant-acceleration IMU model provides the ICP initial guess and deskew, and an adaptive IMU-based orientation regularizer is added to scan-to-map ICP, run with one configuration across platforms.","full_text_reviewed","peer_reviewed_published","supplementary","Oxford Spires 以背包式 LiDAR 在大學校園室內外錄製，地面真值由逐掃描對準地面雷射掃描（TLS）地圖產生，屬既有建築情境；未於施工現場測試。",[20,21,22],"public_benchmark","independent_reference","completed_building",[24,25,26,27],"Same configuration across Oxford Spires, Leg-KILO, HeLiPR, DigiForests, DRZ Living Lab and own car datasets (Sec. IV)","Generally best odometry on both metrics on Oxford Spires and mostly on par with a reference SLAM result (Sec. IV-B)","Only compared method without failures on all presented datasets (Sec. IV-B)","Rural 52 km sequence ATE 714.82 m versus 1086.51 m for FAST-LIO2 (Table II)",[29,30,31,32,33],"Relies on the constant linear acceleration and angular velocity assumption between frames; authors bound its error for typical 0.1 s intervals (Sec. III-B)","Odometry only; no loop closure (inference from system scope)","Biases are assumed constant and initialized assuming no motion over the first interval; initialization was disabled on Oxford Spires because sequences start in motion (Sec. III-B; Sec. IV-A)","Not best everywhere: FAST-LIO2 better on Residential and HeLiPR Bridge, Leg-KILO better on quadruped data (Tables II, III, V)","Disabling double downsampling for sparse LiDARs improved results, which is a sensor-aware setting (Sec. IV-C)",[35,36],"3D LiDAR","IMU (consumer to industrial grade)",[38,39,40,41,42],"vehicle (own car, HeLiPR)","backpack (Oxford Spires, DigiForests)","legged (Unitree Go1)","UAV (DJI M210 v2, DRZ Living Lab)","tree-harvesting machine (qualitative, Fig. 1)","No filter or factor graph for the pose: IMU samples between two scans are bias- and gravity-compensated, averaged, and integrated with a constant linear acceleration and angular velocity model to give the ICP initial guess and per-point deskew; scan-to-map point-to-point ICP adds an accelerometer-based orientation cost weighted by 1\u002Fbeta, with beta = beta0 (1 + sigma_a^2) and beta0 = 200, where body acceleration comes from a small Kalman filter with maximum expected jerk 3 m\u002Fs3; biases assumed constant and estimated from the first inter-scan interval","Scan-to-map ICP building on the KISS-ICP scan-alignment module: point-to-point residuals with a fixed association threshold of 0.5 m in a VDB voxel map (1.0 m voxels), double downsampling at 0.5 v for map update and 1.5 v for registration (optional off switch for sparse LiDARs), points within 1 m of the sensor clipped","discrete poses with per-point deskew from IMU-derived relative transforms","per-point transform from IMU-based motion between LiDAR frames (Sec. III-B)","none","VDB voxel grid (voxel size 1.0 m) storing a fixed number of points per voxel; double downsampling","odometry and local voxel map; export format not_reported","not_reported: no hardware or per-scan timings are given; the authors only state that the system runs faster than the sensor frame rate on all presented datasets","https:\u002F\u002Fgithub.com\u002FPRBonn\u002Frko_lio","MIT (LICENSE file checked)",[54,58],{"relation":55,"title":56,"doi_or_url":57},"preprint","A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling (arXiv v2)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2509.06593",{"relation":59,"title":60,"doi_or_url":51},"code_release","PRBonn\u002Frko_lio",{"id":5,"kind":62,"shortName":7,"title":8,"authors":63,"year":9,"venue":68,"venueType":69,"publisher":70,"volumeIssuePages":71,"doi":72,"arxivId":73,"url":74,"firstPublicDate":75,"publicationStatus":16,"metadataStatus":76,"fulltextStatus":15,"era":10,"classicReason":77,"codeUrl":51,"cluster":11,"topics":78,"mdpi":79,"verification":80,"label":6,"fulltextRoute":81,"versionRead":82,"addedByCensus":79},"method",[64,65,66,67],"Meher V. R. Malladi","Tiziano Guadagnino","Luca Lobefaro","Cyrill Stachniss","IEEE Robotics and Automation Letters","journal","IEEE","11(6):7420-7427","10.1109\u002Flra.2026.3685966","2509.06593","https:\u002F\u002Fapi.crossref.org\u002Fworks?query.bibliographic=A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling","2025-09-08","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v2 (2026-04-16), accepted manuscript (received 2026-02-18, accepted 2026-04-08); IEEE version of record not compared",[84,91,95,101,105,108,112,117,120,123,127,131,136,139,143,149,151,154,160],{"category":85,"model":86,"canonical":86,"role":87,"dataset":88,"specs":89,"locator":90},"lidar","Hesai QT64","dataset sensor","Oxford Spires","backpack-mounted","Sec. IV-A; Fig. 1",{"category":92,"model":93,"canonical":93,"role":87,"dataset":88,"specs":94,"locator":90},"imu","cellphone-grade IMU (Fig. 1 caption names an Alphasense IMU)","400 Hz",{"category":96,"model":97,"canonical":97,"role":98,"dataset":88,"specs":99,"locator":100},"tls_scanner","terrestrial laser-scanning map (scanner model not reported)","reference or ground truth","each undistorted scan registered to the TLS map to compute ground truth","Sec. IV-A",{"category":85,"model":102,"canonical":102,"role":87,"dataset":103,"specs":104,"locator":90},"Velodyne VLP-16","Leg-KILO dataset","on a Unitree Go1 quadruped",{"category":92,"model":106,"canonical":106,"role":87,"dataset":103,"specs":107,"locator":100},"onboard IMU of Unitree Go1","500 Hz",{"category":109,"model":110,"canonical":110,"role":87,"dataset":103,"specs":111,"locator":100},"platform","Unitree Go1 quadruped","indoor sequences, one parking lot and one running sequence",{"category":85,"model":113,"canonical":113,"role":87,"dataset":114,"specs":115,"locator":116},"Aeva Aeries II","HeLiPR","low field-of-view solid-state LiDAR, one of four LiDARs in the dataset","Sec. IV-A; Sec. IV-B",{"category":85,"model":118,"canonical":118,"role":87,"dataset":114,"specs":119,"locator":100},"Livox Avia","non-repetitive scan pattern",{"category":92,"model":121,"canonical":121,"role":87,"dataset":114,"specs":122,"locator":100},"Xsens MTi-300","100 Hz",{"category":124,"model":125,"canonical":125,"role":98,"dataset":114,"specs":126,"locator":100},"gnss","RTK-GPS INS based system","ground truth trajectories for each sensor",{"category":85,"model":128,"canonical":128,"role":87,"dataset":129,"specs":130,"locator":116},"Hesai XT32, QT32 and QT64 (different sessions)","DigiForests","backpack sensor rig; LiDAR inclined 45 deg in the first season",{"category":85,"model":132,"canonical":133,"role":87,"dataset":134,"specs":135,"locator":100},"Ouster OS-0","Ouster OS0","DRZ Living Lab","mounted on a drone",{"category":109,"model":137,"canonical":137,"role":87,"dataset":134,"specs":138,"locator":100},"DJI M210 v2","drone platform",{"category":140,"model":141,"canonical":141,"role":98,"dataset":134,"specs":142,"locator":100},"other","motion capture system","ground truth for nine sequences",{"category":85,"model":144,"canonical":145,"role":146,"dataset":147,"specs":148,"locator":90},"OS1-128","Ouster OS1-128","method input","own car dataset","car-mounted",{"category":92,"model":150,"canonical":150,"role":146,"dataset":147,"specs":122,"locator":90},"built-in InvenSense IMU of the LiDAR",{"category":124,"model":152,"canonical":152,"role":98,"dataset":147,"specs":153,"locator":100},"SBG Ellipse-D GNSS-INS","reference poses from offline LiDAR bundle adjustment incorporating RTK-GPS",{"category":85,"model":155,"canonical":156,"role":146,"dataset":157,"specs":158,"locator":159},"Hesai XT32","Hesai XT-32",null,"on a tree-harvesting machine (qualitative example only)","Fig. 1",{"category":92,"model":161,"canonical":161,"role":146,"dataset":157,"specs":158,"locator":159},"Xsens MTi-100",[],{"totalRows":164,"groupCount":165,"groups":166,"others":566},39,5,[167,282,413,504],{"slug":168,"group":169,"sourceId":5,"sourceLabel":6,"table":170,"selfRows":171,"metrics":172,"seqs":177,"entrants":193,"cells":210,"outcomes":275,"locators":277,"hardware":278,"wordings":279,"notes":280},"rkolio2026-table-ii","rkolio2026:Table II","Table II",15,[173],{"label":174,"unit":175,"statistic":176,"alignment":176},"ATE (m)","m","not_reported",[178,181,184,187,190],{"dataset":147,"sequence":179,"environment":180},"Urban","urban city",{"dataset":147,"sequence":182,"environment":183},"Hill","hill road",{"dataset":147,"sequence":185,"environment":186},"Residential","residential area",{"dataset":147,"sequence":188,"environment":189},"Forest","forest road, 20 km",{"dataset":147,"sequence":191,"environment":192},"Rural","rural roads with underpasses and >100 m elevation change, 52 km",[194,198,201,204,206,208],{"name":195,"methodId":196,"linkable":197,"proposed":79,"self":79},"KISS-ICP","kissicp2023",true,{"name":199,"methodId":200,"linkable":197,"proposed":79,"self":79},"DLIO","dlio2023",{"name":202,"methodId":203,"linkable":197,"proposed":79,"self":79},"FAST-LIO2","fastlio2_2022",{"name":205,"methodId":5,"linkable":197,"proposed":197,"self":197},"Ours (RKO-LIO)",{"name":207,"methodId":5,"linkable":197,"proposed":79,"self":197},"Ours, no-AVG, no-AR (ablation)",{"name":209,"methodId":5,"linkable":197,"proposed":79,"self":197},"Ours, no-AR (ablation)",[211,215,218,221,224,227,229,231,233,235,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,267,269,271,273],[212,212,212,213,214,212,214,214,212],0,8.17,-1,[212,212,216,217,214,212,214,214,212],1,17.69,[212,212,219,220,214,212,214,214,212],2,36.02,[212,212,222,223,214,212,214,214,212],3,178.49,[212,212,225,226,214,212,214,214,212],4,1460.35,[216,212,212,228,214,212,214,214,212],4.94,[216,212,216,230,214,212,214,214,212],12.56,[216,212,219,232,214,212,214,214,212],15.87,[216,212,222,234,214,212,214,214,212],79.9,[216,212,225,157,212,212,214,214,212],[219,212,212,237,214,212,214,214,212],3.53,[219,212,216,239,214,212,214,214,212],6.16,[219,212,219,241,214,212,214,214,212],15.46,[219,212,222,243,214,212,214,214,212],71.69,[219,212,225,245,214,212,214,214,212],1086.51,[222,212,212,247,214,212,214,214,212],3.52,[222,212,216,249,214,212,214,214,212],6.05,[222,212,219,251,214,212,214,214,212],24,[222,212,222,253,214,212,214,214,212],50.11,[222,212,225,255,214,212,214,214,212],714.82,[225,212,212,257,214,212,214,214,212],4.16,[225,212,216,259,214,212,214,214,212],8.49,[225,212,219,261,214,212,214,214,212],28.67,[225,212,222,263,214,212,214,214,212],58.02,[225,212,225,265,214,212,214,214,212],833.48,[165,212,212,165,214,212,214,214,212],[165,212,216,268,214,212,214,214,212],5.42,[165,212,219,270,214,212,214,214,212],23.54,[165,212,222,272,214,212,214,214,212],50.55,[165,212,225,274,214,212,214,214,212],732.79,[276],"failed to run on the sequence (dash)",[170],[],[],[281],"Own car sequences (OS1-128 with built-in InvenSense IMU; Forest 20 km, Rural 52 km); reference by offline LiDAR bundle adjustment with RTK-GPS; ATE only transcribed; ablation rows no-AVG no-AR and no-AR disable IMU averaging and adaptive regularization; dash means failure to run",{"slug":283,"group":284,"sourceId":5,"sourceLabel":6,"table":285,"selfRows":286,"metrics":287,"seqs":294,"entrants":306,"cells":313,"outcomes":406,"locators":407,"hardware":408,"wordings":409,"notes":410},"rkolio2026-table-i","rkolio2026:Table I","Table I",10,[288,290],{"label":289,"unit":175,"statistic":176,"alignment":176},"ATE (m), averaged over sequences of each scene",{"label":291,"unit":292,"statistic":293,"alignment":176},"RPE (%), intervals 1, 2, 5, 10, 20, 50, 100 m","%","mean",[295,298,300,302,304],{"dataset":88,"sequence":296,"environment":297},"Blenheim","university campus and college buildings, outdoor and indoor, backpack",{"dataset":88,"sequence":299,"environment":297},"Bodleian",{"dataset":88,"sequence":301,"environment":297},"Christ",{"dataset":88,"sequence":303,"environment":297},"Keble",{"dataset":88,"sequence":305,"environment":297},"Radcliffe",[307,308,309,310,311],{"name":195,"methodId":196,"linkable":197,"proposed":79,"self":79},{"name":199,"methodId":200,"linkable":197,"proposed":79,"self":79},{"name":202,"methodId":203,"linkable":197,"proposed":79,"self":79},{"name":205,"methodId":5,"linkable":197,"proposed":197,"self":197},{"name":312,"methodId":157,"linkable":79,"proposed":79,"self":79},"VILENS-SLAM (SLAM reference, results from Tao et al.)",[314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,376,378,379,381,382,383,385,386,388,390,392,394,395,397,399,401,403,405],[212,212,212,315,214,212,214,214,212],1.16,[212,212,216,317,214,212,214,214,212],2.15,[212,212,219,319,214,212,214,214,212],0.96,[212,212,222,321,214,212,214,214,212],9.48,[212,212,225,323,214,212,214,214,212],0.46,[216,212,212,325,214,212,214,214,212],11.35,[216,212,216,327,214,212,214,214,212],0.4,[216,212,219,329,214,212,214,214,212],0.17,[216,212,222,331,214,212,214,214,212],0.36,[216,212,225,333,214,212,214,214,212],0.92,[219,212,212,335,214,212,214,214,212],0.94,[219,212,216,337,214,212,214,214,212],0.26,[219,212,219,339,214,212,214,214,212],0.72,[219,212,222,341,214,212,214,214,212],6.06,[219,212,225,323,214,212,214,214,212],[222,212,212,344,214,212,214,214,212],0.2,[222,212,216,346,214,212,214,214,212],0.86,[222,212,219,348,214,212,214,214,212],0.25,[222,212,222,350,214,212,214,214,212],0.08,[222,212,225,352,214,212,214,214,212],0.15,[225,212,212,354,214,212,214,214,212],0.56,[225,212,216,356,214,212,214,214,212],1.11,[225,212,219,358,214,212,214,214,212],0.11,[225,212,222,360,214,212,214,214,212],0.12,[225,212,225,362,214,212,214,214,212],0.07,[212,216,212,364,214,212,214,214,216],46.99,[212,216,216,366,214,212,214,214,216],19.1,[212,216,219,368,214,212,214,214,216],11.01,[212,216,222,370,214,212,214,214,216],38.48,[212,216,225,372,214,212,214,214,216],3.58,[216,216,212,374,214,212,214,214,216],46.87,[216,216,216,356,214,212,214,214,216],[216,216,219,377,214,212,214,214,216],4.17,[216,216,222,257,214,212,214,214,216],[216,216,225,380,214,212,214,214,216],4.28,[219,216,212,356,214,212,214,214,216],[219,216,216,337,214,212,214,214,216],[219,216,219,384,214,212,214,214,216],23.64,[219,216,222,268,214,212,214,214,216],[219,216,225,387,214,212,214,214,216],1.4,[222,216,212,389,214,212,214,214,216],0.95,[222,216,216,391,214,212,214,214,216],0.74,[222,216,219,393,214,212,214,214,216],0.89,[222,216,222,319,214,212,214,214,216],[222,216,225,396,214,212,214,214,216],0.7,[225,216,212,398,214,212,214,214,216],1.13,[225,216,216,400,214,212,214,214,216],1.68,[225,216,219,402,214,212,214,214,216],0.38,[225,216,222,404,214,212,214,214,216],0.39,[225,216,225,404,214,212,214,214,216],[],[285],[],[],[411,412],"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",{"slug":414,"group":415,"sourceId":5,"sourceLabel":6,"table":416,"selfRows":417,"metrics":418,"seqs":422,"entrants":435,"cells":442,"outcomes":497,"locators":498,"hardware":499,"wordings":500,"notes":501},"rkolio2026-table-iii","rkolio2026:Table III","Table III",6,[419,420],{"label":174,"unit":175,"statistic":176,"alignment":176},{"label":421,"unit":292,"statistic":293,"alignment":176},"RPE (%)",[423,426,429,432],{"dataset":103,"sequence":424,"environment":425},"Corridor","indoor corridor",{"dataset":103,"sequence":427,"environment":428},"Indoor","indoor",{"dataset":103,"sequence":430,"environment":431},"Parking","parking lot",{"dataset":103,"sequence":433,"environment":434},"Running","running gait",[436,437,438,439,441],{"name":195,"methodId":196,"linkable":197,"proposed":79,"self":79},{"name":199,"methodId":200,"linkable":197,"proposed":79,"self":79},{"name":202,"methodId":203,"linkable":197,"proposed":79,"self":79},{"name":440,"methodId":157,"linkable":79,"proposed":79,"self":79},"Leg-KILO",{"name":205,"methodId":5,"linkable":197,"proposed":197,"self":197},[443,445,446,448,450,452,453,455,457,459,461,462,464,465,467,469,470,472,473,475,477,479,481,483,485,487,489,491,493,495],[212,212,212,444,214,212,214,214,212],10.59,[212,212,216,335,214,212,214,214,212],[212,212,219,447,214,212,214,214,212],18.8,[212,212,222,449,214,212,214,214,212],4.29,[216,212,212,451,214,212,214,214,212],2.7,[216,212,216,362,214,212,214,214,212],[216,212,219,454,214,212,214,214,212],0.9,[216,212,222,456,214,212,214,214,212],0.24,[219,212,212,458,214,212,214,214,212],0.28,[219,212,216,460,214,212,214,214,212],0.21,[219,212,219,456,214,212,214,214,212],[219,212,222,463,214,212,214,214,212],0.09,[222,212,212,329,214,212,214,214,212],[222,212,216,466,214,212,214,214,212],0.05,[222,212,219,468,214,212,214,214,212],0.16,[222,212,222,466,214,212,214,214,212],[225,212,212,471,214,212,214,214,212],0.22,[225,212,216,466,214,212,214,214,212],[225,212,219,474,214,212,214,214,212],0.23,[225,212,222,476,214,212,214,214,212],0.1,[212,216,212,478,214,212,214,214,216],175.08,[212,216,219,480,214,212,214,214,216],348.8,[216,216,212,482,214,212,214,214,216],2.83,[216,216,219,484,214,212,214,214,216],5.56,[219,216,212,486,214,212,214,214,216],1.94,[219,216,219,488,214,212,214,214,216],1.5,[222,216,212,490,214,212,214,214,216],0.64,[222,216,219,492,214,212,214,214,216],0.43,[225,216,212,494,214,212,214,214,216],1.51,[225,216,219,496,214,212,214,214,216],1.8,[],[416],[],[],[502,503],"Leg-KILO dataset, Unitree Go1 quadruped with VLP-16 and 500 Hz IMU; Indoor ground truth from a prior map, others from offline optimization with loop closures; Leg-KILO also uses leg kinematics","Leg-KILO dataset, Unitree Go1 quadruped with VLP-16 and 500 Hz IMU; Indoor ground truth from a prior map, others from offline optimization with loop closures; Leg-KILO also uses leg kinematics; RPE only for Corridor and Parking (not applicable to Indoor and Running, which are shorter than 100 m)",{"slug":505,"group":506,"sourceId":5,"sourceLabel":6,"table":507,"selfRows":417,"metrics":508,"seqs":513,"entrants":521,"cells":526,"outcomes":559,"locators":561,"hardware":562,"wordings":563,"notes":564},"rkolio2026-table-iv","rkolio2026:Table IV","Table IV",[509,511],{"label":510,"unit":175,"statistic":176,"alignment":176},"ATE (m), season average",{"label":512,"unit":292,"statistic":293,"alignment":176},"RPE (%), season average",[514,517,519],{"dataset":129,"sequence":515,"environment":516},"2023-03","dense forest, backpack",{"dataset":129,"sequence":518,"environment":516},"2023-10",{"dataset":129,"sequence":520,"environment":516},"2024-07",[522,523,524,525],{"name":195,"methodId":196,"linkable":197,"proposed":79,"self":79},{"name":199,"methodId":200,"linkable":197,"proposed":79,"self":79},{"name":202,"methodId":203,"linkable":197,"proposed":79,"self":79},{"name":205,"methodId":5,"linkable":197,"proposed":197,"self":197},[527,528,529,530,532,533,534,535,536,537,539,540,541,542,543,544,546,548,549,550,551,553,555,557],[212,212,212,157,212,212,214,214,212],[212,212,216,157,212,212,214,214,212],[212,212,219,157,212,212,214,214,212],[216,212,212,531,214,212,214,214,212],0.27,[216,212,216,360,214,212,214,214,212],[216,212,219,463,214,212,214,214,212],[219,212,212,157,212,212,214,214,212],[219,212,216,157,212,212,214,214,212],[219,212,219,350,214,212,214,214,212],[222,212,212,538,214,212,214,214,212],0.18,[222,212,216,358,214,212,214,214,212],[222,212,219,476,214,212,214,214,212],[212,216,212,157,212,212,214,214,212],[212,216,216,157,212,212,214,214,212],[212,216,219,157,212,212,214,214,212],[216,216,212,545,214,212,214,214,212],1.55,[216,216,216,547,214,212,214,214,212],1.45,[216,216,219,339,214,212,214,214,212],[219,216,212,157,212,212,214,214,212],[219,216,216,157,212,212,214,214,212],[219,216,219,552,214,212,214,214,212],0.51,[222,216,212,554,214,212,214,214,212],0.79,[222,216,216,556,214,212,214,214,212],1.14,[222,216,219,558,214,212,214,214,212],0.87,[560],"failed on at least one sequence of the recording period (dash)",[507],[],[],[565],"DigiForests backpack sessions (Hesai XT32, QT32, QT64; LiDAR inclined 45 deg in the first season); reference trajectories from offline VILENS with GNSS and loop closures; averages per season; dash means failure on at least one sequence of that season",[567],{"group":568,"slug":569,"sourceLabel":6,"table":570,"selfRows":219,"datasets":571},"rkolio2026:Text Sec. IV-B (DRZ Living Lab)","rkolio2026-text-sec-iv-b-drz-living-lab","Text Sec. IV-B (DRZ Living Lab)",[134],1790510660928]