[{"data":1,"prerenderedAt":618},["ShallowReactive",2],{"method-resple2025":3},{"method":4,"reference":61,"equipment":82,"figures":148,"results":149},{"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":24,"limitations":29,"sensors":33,"platform":36,"estimator":42,"association":43,"timeModel":44,"deskew":45,"loopClosure":46,"globalOptimization":47,"mapRepresentation":48,"prior":47,"outputGeometry":49,"compute":50,"codeUrl":51,"codeLicense":52,"relatedVersions":53},"resple2025","Cao et al., 2025","RESPLE","RESPLE: Recursive Spline Estimation for LiDAR-Based Odometry",2025,"recent","C05","odometry_with_local_mapping","RESPLE 把三次 B 樣條（B-spline）直接嵌入狀態空間模型，以遞迴式（濾波）方式估計六自由度連續時間運動，而非以滑動視窗最佳化擬合樣條。狀態向量由位置控制點與姿態控制點增量組成，以修改後的迭代擴展卡爾曼濾波更新，每個 LiDAR 點以其時間戳在樣條上求位姿後計算點到平面殘差。同一骨幹可組成 LiDAR-only、LiDAR-慣性、多 LiDAR 與多 LiDAR-慣性里程計。","A recursive (filter-based) continuous-time estimator that embeds 6-DoF cubic B-splines in the state and updates spline control points with a modified iterated EKF, supporting single or multiple LiDARs with or without IMU.","full_text_reviewed","peer_reviewed_published","supplementary","GrandTour 評估序列中有兩段地下序列（JTL、JTS），但作者註明未公開釋出；其餘為都市、森林與山區。另有頭盔式 HelmDyn 室內高動態資料與校園雙輪足機器人序列。未見施工現場資料或點雲幾何精度評估。",[20,21,22,23],"public_benchmark","underground_or_tunnel","controlled_experiment","independent_reference",[25,26,27,28],"Comparable or superior accuracy and robustness to Traj-LO, CTE-MLO and FAST-LIO2 across NTU VIRAL, MCD, GrandTour and HelmDyn (abstract; Sec. V)","LiDAR-only variant had only one failure on GrandTour sequences where compared systems failed multiple times (Sec. V-B)","Lowest APE on all 10 HelmDyn sequences against Traj-LO, CTE-MLO, FAST-LIO2, Point-LIO and SLICT2 (Table IV)","Fastest in the runtime comparison, 2x to 9x faster than real time (Tables V and VI)",[30,31,32],"No global correction backend; potential degeneracy to be addressed with visual sensors in future work (Sec. VI)","Point-wise (batch size 1) or very large observation batches reduce robustness through false associations, especially at low knot frequency (Sec. V-E)","Two underground GrandTour sequences (JTL, JTS) and PKH are not in the public release, so that evidence cannot be reproduced (Table III)",[34,35],"one or multiple 3D LiDARs (Ouster OS1-16, Livox Mid70, Mid360, Avia, Hesai XT32)","IMU optional (VN100 or LiDAR built-in IMU)",[37,38,39,40,41],"UAV (NTU VIRAL)","vehicle (MCD)","legged (GrandTour ANYmal D)","wearable helmet (HelmDyn)","wheeled bipedal robot DIABLO (R-Campus, about 1400 m; end-to-end error reported)","recursive Bayesian estimator: modified iterated EKF over cubic B-spline control points (position control points and orientation increments), without error-state formulation","point-to-plane residual per point, plane fitted from N=5 neighbours in ikd-Tree","continuous-time cubic B-spline (knot frequency 100 Hz in experiments)","not required: each point evaluated at its own timestamp on the spline","none (backend for global correction listed as future work, Sec. VI)","none","point map in ikd-Tree","continuous-time trajectory and point map; export format not_reported","Laptop Intel i7-11800H, 48 GB RAM, Ubuntu 22.04; RESPLE node 0.97 to 4.46 ms per 10 ms observation batch (Table V); on HD_03 1.40 ms (LO) and 1.74 ms (LIO) with runtime efficiency 0.14 and 0.17 versus 0.23 (Traj-LO), 0.79 (CTE-MLO) and 3.31 (SLICT2) using 5 CPU threads (Table VI)","https:\u002F\u002Fgithub.com\u002FASIG-X\u002FRESPLE","GPL-3.0 (LICENSE file checked)",[54,58],{"relation":55,"title":56,"doi_or_url":57},"preprint","RESPLE (arXiv v3, marked as published in RA-L)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.11580",{"relation":59,"title":60,"doi_or_url":51},"code_release","ASIG-X\u002FRESPLE",{"id":5,"kind":62,"shortName":7,"title":8,"authors":63,"year":9,"venue":67,"venueType":68,"publisher":69,"volumeIssuePages":70,"doi":71,"arxivId":72,"url":73,"firstPublicDate":74,"publicationStatus":16,"metadataStatus":75,"fulltextStatus":15,"era":10,"classicReason":76,"codeUrl":51,"cluster":11,"topics":77,"mdpi":78,"verification":79,"label":6,"fulltextRoute":80,"versionRead":81,"addedByCensus":78},"method",[64,65,66],"Ziyu Cao","William Talbot","Kailai Li","IEEE Robotics and Automation Letters","journal","IEEE","10(10):10666-10673","10.1109\u002Flra.2025.3604758","2504.11580","https:\u002F\u002Fapi.crossref.org\u002Fworks?query.bibliographic=RESPLE Recursive Spline Estimation for LiDAR-Based Odometry","2025-04-15","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v3 (2025-09-11), header states published in IEEE RA-L, DOI 10.1109\u002FLRA.2025.3604758; IEEE version of record not compared",[83,91,96,101,102,107,111,114,119,125,127,133,138,141],{"category":84,"model":85,"canonical":86,"role":87,"dataset":88,"specs":89,"locator":90},"lidar","Ouster OS1-16 (horizontal)","Ouster OS1-16","dataset sensor","NTU VIRAL","drone; adopted LiDAR for RESPLE","Table I; Sec. V-B1",{"category":92,"model":93,"canonical":93,"role":87,"dataset":88,"specs":94,"locator":95},"imu","VN100","adopted IMU","Table I",{"category":84,"model":97,"canonical":98,"role":87,"dataset":99,"specs":100,"locator":95},"Livox Mid70","Livox MID70","MCD","large-scale urban, fast ground vehicle",{"category":92,"model":93,"canonical":93,"role":87,"dataset":99,"specs":94,"locator":95},{"category":84,"model":103,"canonical":103,"role":87,"dataset":104,"specs":105,"locator":106},"Hesai XT32 (L1)","GrandTour","on the Boxi multi-sensor rig","Table I; Table III",{"category":84,"model":108,"canonical":109,"role":87,"dataset":104,"specs":110,"locator":106},"Livox Mid360 (L2)","Livox MID-360","second LiDAR; its built-in IMU used as I",{"category":92,"model":112,"canonical":112,"role":87,"dataset":104,"specs":113,"locator":95},"built-in IMU of Livox Mid360","used as I in GrandTour",{"category":115,"model":116,"canonical":116,"role":87,"dataset":104,"specs":117,"locator":118},"platform","ANYmal D quadruped robot with Boxi rig","71 Swiss environments, 15 km over 8 hours in the dataset","Sec. V-B3",{"category":84,"model":120,"canonical":109,"role":121,"dataset":122,"specs":123,"locator":124},"Livox Mid360","method input","HelmDyn (own experiment)","helmet-mounted, 12 x 12 x 8 m3 space, walking, running, jumping and in-hand waving","Table I; Sec. V-C; Fig. 4A",{"category":92,"model":112,"canonical":112,"role":121,"dataset":122,"specs":126,"locator":95},"used for R-LIO on HelmDyn",{"category":128,"model":129,"canonical":129,"role":130,"dataset":122,"specs":131,"locator":132},"other","Qualisys motion capture: 12 Oqus 700+ and 8 Arqus A12 cameras with passive markers","reference or ground truth","submillimeter, low latency","Sec. V-C",{"category":84,"model":134,"canonical":134,"role":121,"dataset":135,"specs":136,"locator":137},"Livox Avia","R-Campus (own experiment)","on the wheeled bipedal robot","Sec. V-C; Fig. 4B",{"category":115,"model":139,"canonical":139,"role":121,"dataset":135,"specs":140,"locator":137},"DIABLO wheeled bipedal robot","about 1400 m campus route at 1.2 m\u002Fs",{"category":142,"model":143,"canonical":143,"role":144,"dataset":145,"specs":146,"locator":147},"compute","laptop with Intel i7-11800H CPU, 48 GB RAM","compute for runtime",null,"Ubuntu 22.04; all evaluations","Sec. V",[],{"totalRows":150,"groupCount":151,"groups":152,"others":617},74,4,[153,391,523,578],{"slug":154,"group":155,"sourceId":5,"sourceLabel":6,"table":156,"selfRows":157,"metrics":158,"seqs":164,"entrants":202,"cells":216,"outcomes":383,"locators":385,"hardware":387,"wordings":388,"notes":389},"resple2025-table-ii-ntu-viral","resple2025:Table II (NTU VIRAL)","Table II (NTU VIRAL)",36,[159],{"label":160,"unit":161,"statistic":162,"alignment":163},"APE (RMSE, meters)","m","RMSE","not_reported",[165,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200],{"dataset":88,"sequence":166,"environment":167},"eee_01","campus indoor and outdoor, drone",{"dataset":88,"sequence":169,"environment":167},"eee_02",{"dataset":88,"sequence":171,"environment":167},"eee_03",{"dataset":88,"sequence":173,"environment":167},"nya_01",{"dataset":88,"sequence":175,"environment":167},"nya_02",{"dataset":88,"sequence":177,"environment":167},"nya_03",{"dataset":88,"sequence":179,"environment":167},"rtp_01",{"dataset":88,"sequence":181,"environment":167},"rtp_02",{"dataset":88,"sequence":183,"environment":167},"rtp_03",{"dataset":88,"sequence":185,"environment":167},"sbs_01",{"dataset":88,"sequence":187,"environment":167},"sbs_02",{"dataset":88,"sequence":189,"environment":167},"sbs_03",{"dataset":88,"sequence":191,"environment":167},"spms_01",{"dataset":88,"sequence":193,"environment":167},"spms_02",{"dataset":88,"sequence":195,"environment":167},"spms_03",{"dataset":88,"sequence":197,"environment":167},"tnp_01",{"dataset":88,"sequence":199,"environment":167},"tnp_02",{"dataset":88,"sequence":201,"environment":167},"tnp_03",[203,207,209,212,214],{"name":204,"methodId":205,"linkable":206,"proposed":78,"self":78},"T-LO (Traj-LO)","trajlo2024",true,{"name":208,"methodId":145,"linkable":78,"proposed":78,"self":78},"C-MLO (CTE-MLO, 2 LiDARs)",{"name":210,"methodId":211,"linkable":206,"proposed":78,"self":78},"F-LIO2 (FAST-LIO2)","fastlio2_2022",{"name":213,"methodId":5,"linkable":206,"proposed":206,"self":206},"R-LO (RESPLE LiDAR-only)",{"name":215,"methodId":5,"linkable":206,"proposed":206,"self":206},"R-LIO (RESPLE LiDAR-inertial)",[217,221,224,227,230,232,235,237,240,243,246,248,250,253,255,258,261,264,267,269,271,273,275,277,279,281,283,284,285,286,287,289,291,293,294,295,296,298,299,301,303,304,306,308,310,312,314,316,318,319,321,323,324,326,328,330,332,334,336,338,340,342,344,346,348,350,351,352,353,355,356,357,358,359,361,362,364,366,367,368,370,372,373,375,376,377,378,380,381,382],[218,218,218,219,220,218,220,220,218],0,0.055,-1,[218,218,222,223,220,218,220,220,218],1,0.039,[218,218,225,226,220,218,220,220,218],2,0.035,[218,218,228,229,220,218,220,220,218],3,0.047,[218,218,151,231,220,218,220,220,218],0.052,[218,218,233,234,220,218,220,220,218],5,0.05,[218,218,236,234,220,218,220,220,218],6,[218,218,238,239,220,218,220,220,218],7,0.058,[218,218,241,242,220,218,220,220,218],8,0.057,[218,218,244,245,220,218,220,220,218],9,0.048,[218,218,247,223,220,218,220,220,218],10,[218,218,249,223,220,218,220,220,218],11,[218,218,251,252,220,218,220,220,218],12,0.121,[218,218,254,145,218,218,220,220,218],13,[218,218,256,257,220,218,220,220,218],14,0.103,[218,218,259,260,220,218,220,220,218],15,0.505,[218,218,262,263,220,218,220,220,218],16,0.607,[218,218,265,266,220,218,220,220,218],17,0.101,[222,218,218,268,220,218,220,220,218],0.08,[222,218,222,270,220,218,220,220,218],0.07,[222,218,225,272,220,218,220,220,218],0.12,[222,218,228,274,220,218,220,220,218],0.06,[222,218,151,276,220,218,220,220,218],0.09,[222,218,233,278,220,218,220,220,218],0.1,[222,218,236,280,220,218,220,220,218],0.13,[222,218,238,282,220,218,220,220,218],0.14,[222,218,241,282,220,218,220,220,218],[222,218,244,276,220,218,220,220,218],[222,218,247,268,220,218,220,220,218],[222,218,249,276,220,218,220,220,218],[222,218,251,288,220,218,220,220,218],0.21,[222,218,254,290,220,218,220,220,218],0.33,[222,218,256,292,220,218,220,220,218],0.2,[222,218,259,276,220,218,220,220,218],[222,218,262,276,220,218,220,220,218],[222,218,265,278,220,218,220,220,218],[225,218,218,297,220,218,220,220,218],0.069,[225,218,222,297,220,218,220,220,218],[225,218,225,300,220,218,220,220,218],0.111,[225,218,228,302,220,218,220,220,218],0.053,[225,218,151,276,220,218,220,220,218],[225,218,233,305,220,218,220,220,218],0.108,[225,218,236,307,220,218,220,220,218],0.125,[225,218,238,309,220,218,220,220,218],0.131,[225,218,241,311,220,218,220,220,218],0.137,[225,218,244,313,220,218,220,220,218],0.086,[225,218,247,315,220,218,220,220,218],0.078,[225,218,249,317,220,218,220,220,218],0.076,[225,218,251,288,220,218,220,220,218],[225,218,254,320,220,218,220,220,218],0.336,[225,218,256,322,220,218,220,220,218],0.217,[225,218,259,276,220,218,220,220,218],[225,218,262,325,220,218,220,220,218],0.11,[225,218,265,327,220,218,220,220,218],0.089,[228,218,218,329,220,218,220,220,218],0.044,[228,218,222,331,220,218,220,220,218],0.023,[228,218,225,333,220,218,220,220,218],0.046,[228,218,228,335,220,218,220,220,218],0.033,[228,218,151,337,220,218,220,220,218],0.036,[228,218,233,339,220,218,220,220,218],0.037,[228,218,236,341,220,218,220,220,218],0.059,[228,218,238,343,220,218,220,220,218],0.071,[228,218,241,345,220,218,220,220,218],0.054,[228,218,244,347,220,218,220,220,218],0.04,[228,218,247,349,220,218,220,220,218],0.034,[228,218,249,337,220,218,220,220,218],[228,218,251,305,220,218,220,220,218],[228,218,254,280,220,218,220,220,218],[228,218,256,354,220,218,220,220,218],0.216,[228,218,259,231,220,218,220,220,218],[228,218,262,270,220,218,220,220,218],[228,218,265,234,220,218,220,220,218],[151,218,218,337,220,218,220,220,218],[151,218,222,360,220,218,220,220,218],0.022,[151,218,225,335,220,218,220,220,218],[151,218,228,363,220,218,220,220,218],0.03,[151,218,151,365,220,218,220,220,218],0.032,[151,218,233,363,220,218,220,220,218],[151,218,236,231,220,218,220,220,218],[151,218,238,369,220,218,220,220,218],0.049,[151,218,241,371,220,218,220,220,218],0.056,[151,218,244,349,220,218,220,220,218],[151,218,247,374,220,218,220,220,218],0.031,[151,218,249,335,220,218,220,220,218],[151,218,251,307,220,218,220,220,218],[151,218,254,252,220,218,220,220,218],[151,218,256,379,220,218,220,220,218],0.109,[151,218,259,369,220,218,220,220,218],[151,218,262,229,220,218,220,220,218],[151,218,265,333,220,218,220,220,218],[384],"failed",[386],"Table II",[],[],[390],"NTU VIRAL drone sequences, horizontal OS1-16 for RESPLE; APE RMSE with the official NTU VIRAL evaluation script; T-LO, C-MLO (two LiDARs) and F-LIO2 values are copied from refs. [13] (Traj-LO), [16] (CTE-MLO) and [18] (Nguyen et al. 2024, not the FAST-LIO2 paper), respectively, per the Table II footnote; x marks failure",{"slug":392,"group":393,"sourceId":5,"sourceLabel":6,"table":394,"selfRows":395,"metrics":396,"seqs":398,"entrants":419,"cells":434,"outcomes":516,"locators":517,"hardware":519,"wordings":520,"notes":521},"resple2025-table-iii-grandtour","resple2025:Table III (GrandTour)","Table III (GrandTour)",32,[397],{"label":160,"unit":161,"statistic":162,"alignment":163},[399,402,404,407,410,413,415,417],{"dataset":104,"sequence":400,"environment":401},"JTL","underground (not in public release)",{"dataset":104,"sequence":403,"environment":401},"JTS",{"dataset":104,"sequence":405,"environment":406},"RIV-1","wild (forest or mountain)",{"dataset":104,"sequence":408,"environment":409},"PKH","wild (not in public release)",{"dataset":104,"sequence":411,"environment":412},"HEAP-1","urban",{"dataset":104,"sequence":414,"environment":406},"TRIM-1",{"dataset":104,"sequence":416,"environment":406},"ALB-2",{"dataset":104,"sequence":418,"environment":406},"LMB-2",[420,422,424,426,428,430,432],{"name":421,"methodId":205,"linkable":206,"proposed":78,"self":78},"T-LO (L1)",{"name":423,"methodId":145,"linkable":78,"proposed":78,"self":78},"C-MLO (L1+L2)",{"name":425,"methodId":211,"linkable":206,"proposed":78,"self":78},"F-LIO2 (L1+I)",{"name":427,"methodId":5,"linkable":206,"proposed":206,"self":206},"R-LO (L1)",{"name":429,"methodId":5,"linkable":206,"proposed":206,"self":206},"R-MLO (L1+L2)",{"name":431,"methodId":5,"linkable":206,"proposed":206,"self":206},"R-LIO (L1+I)",{"name":433,"methodId":5,"linkable":206,"proposed":206,"self":206},"R-MLIO (L1+L2+I)",[435,436,438,439,440,442,443,444,446,447,449,450,452,454,456,457,458,459,461,463,464,466,468,470,472,473,475,477,478,480,481,483,484,485,487,489,490,492,493,495,496,498,500,501,502,503,504,506,507,508,510,511,512,513,514,515],[218,218,218,333,220,218,220,220,218],[218,218,222,437,220,218,220,220,218],0.074,[218,218,225,145,218,218,220,220,218],[218,218,228,145,218,218,220,220,218],[218,218,151,441,220,218,220,220,218],0.045,[218,218,233,229,220,218,220,220,218],[218,218,236,329,220,218,220,220,218],[218,218,238,445,220,218,220,220,218],0.043,[222,218,218,145,218,218,220,220,218],[222,218,222,448,220,218,220,220,218],0.264,[222,218,225,145,218,218,220,220,218],[222,218,228,451,220,218,220,220,218],4.576,[222,218,151,453,220,218,220,220,218],0.038,[222,218,233,455,220,218,220,220,218],0.063,[222,218,236,234,220,218,220,220,218],[222,218,238,145,218,218,220,220,218],[225,218,218,145,218,218,220,220,218],[225,218,222,460,220,218,220,220,218],2.585,[225,218,225,462,220,218,220,220,218],5.522,[225,218,228,145,218,218,220,220,218],[225,218,151,465,220,218,220,220,218],0.151,[225,218,233,467,220,218,220,220,218],0.218,[225,218,236,469,220,218,220,220,218],0.442,[225,218,238,471,220,218,220,220,218],3.086,[228,218,218,226,220,218,220,220,218],[228,218,222,474,220,218,220,220,218],0.256,[228,218,225,476,220,218,220,220,218],0.041,[228,218,228,145,218,218,220,220,218],[228,218,151,479,220,218,220,220,218],0.026,[228,218,233,223,220,218,220,220,218],[228,218,236,482,220,218,220,220,218],0.018,[228,218,238,347,220,218,220,220,218],[151,218,218,479,220,218,220,220,218],[151,218,222,486,220,218,220,220,218],0.088,[151,218,225,488,220,218,220,220,218],0.066,[151,218,228,341,220,218,220,220,218],[151,218,151,491,220,218,220,220,218],0.027,[151,218,233,363,220,218,220,220,218],[151,218,236,494,220,218,220,220,218],0.029,[151,218,238,223,220,218,220,220,218],[233,218,218,497,220,218,220,220,218],0.028,[233,218,222,499,220,218,220,220,218],0.128,[233,218,225,223,220,218,220,220,218],[233,218,228,317,220,218,220,220,218],[233,218,151,360,220,218,220,220,218],[233,218,233,339,220,218,220,220,218],[233,218,236,505,220,218,220,220,218],0.015,[233,218,238,333,220,218,220,220,218],[236,218,218,497,220,218,220,220,218],[236,218,222,509,220,218,220,220,218],0.091,[236,218,225,333,220,218,220,220,218],[236,218,228,245,220,218,220,220,218],[236,218,151,497,220,218,220,220,218],[236,218,233,497,220,218,220,220,218],[236,218,236,374,220,218,220,220,218],[236,218,238,226,220,218,220,220,218],[384],[518],"Table III",[],[],[522],"GrandTour ANYmal D quadruped with Boxi rig: L1 Hesai XT32, L2 Livox Mid360, I built-in IMU of L2; APE RMSE via evo after interpolating estimates at ground-truth timestamps; x marks failure; sequences marked * are not in the public release",{"slug":524,"group":525,"sourceId":5,"sourceLabel":6,"table":526,"selfRows":151,"metrics":527,"seqs":535,"entrants":540,"cells":550,"outcomes":570,"locators":571,"hardware":572,"wordings":574,"notes":575},"resple2025-table-vi","resple2025:Table VI","Table VI",[528,532],{"label":529,"unit":530,"statistic":531,"alignment":76},"Processing time (ms)","ms","mean",{"label":533,"unit":534,"statistic":531,"alignment":76},"Runtime efficiency xi","ratio",[536],{"dataset":537,"sequence":538,"environment":539},"HelmDyn (own dataset)","HD_03","indoor motion-capture space, helmet, dynamic motion",[541,542,544,546,548],{"name":204,"methodId":205,"linkable":206,"proposed":78,"self":78},{"name":543,"methodId":145,"linkable":78,"proposed":78,"self":78},"C-MLO (CTE-MLO)",{"name":545,"methodId":145,"linkable":78,"proposed":78,"self":78},"SLICT2",{"name":547,"methodId":5,"linkable":206,"proposed":206,"self":206},"R-LO",{"name":549,"methodId":5,"linkable":206,"proposed":206,"self":206},"R-LIO",[551,553,555,557,559,561,563,565,567,568],[218,218,218,552,220,218,218,220,218],11.55,[222,218,218,554,220,218,218,220,218],7.85,[225,218,218,556,220,218,218,220,218],165.73,[228,218,218,558,220,218,218,220,218],1.4,[151,218,218,560,220,218,218,220,218],1.74,[218,222,218,562,220,218,218,220,222],0.23,[222,222,218,564,220,218,218,220,222],0.79,[225,222,218,566,220,218,218,220,222],3.31,[228,222,218,282,220,218,218,220,222],[151,222,218,569,220,218,218,220,222],0.17,[],[526],[573],"laptop, Intel i7-11800H CPU, 48 GB RAM, Ubuntu 22.04; 5 CPU threads for all systems in the runtime comparison",[],[576,577],"Runtime comparison on HD_03 (helmet Livox Mid360); processing time per available interval (available time 50 ms for T-LO and SLICT2, 10 ms for the others)","Runtime efficiency xi = processing time divided by available time; xi at most 1 means real time",{"slug":579,"group":580,"sourceId":5,"sourceLabel":6,"table":581,"selfRows":225,"metrics":582,"seqs":585,"entrants":589,"cells":600,"outcomes":611,"locators":612,"hardware":613,"wordings":614,"notes":615},"resple2025-text-sec-v-c-r-campus","resple2025:Text Sec. V-C (R-Campus)","Text Sec. V-C (R-Campus)",[583],{"label":584,"unit":161,"statistic":163,"alignment":47},"end-to-end error",[586],{"dataset":135,"sequence":587,"environment":588},"R-Campus","university campus, wheeled bipedal robot",[590,592,594,596,598],{"name":591,"methodId":5,"linkable":206,"proposed":206,"self":206},"RESPLE LO",{"name":593,"methodId":5,"linkable":206,"proposed":206,"self":206},"RESPLE LIO",{"name":595,"methodId":145,"linkable":78,"proposed":78,"self":78},"CTE-MLO",{"name":597,"methodId":211,"linkable":206,"proposed":78,"self":78},"FAST-LIO2",{"name":599,"methodId":205,"linkable":206,"proposed":78,"self":78},"Traj-LO",[601,603,605,607,609],[218,218,218,602,220,218,220,220,218],0.28,[222,218,218,604,220,218,220,220,218],0.27,[225,218,218,606,220,218,220,220,218],0.3,[228,218,218,608,220,218,220,220,218],2.7,[151,218,218,610,220,218,220,220,218],80.31,[],[132],[],[],[616],"Own R-Campus sequence: Livox Avia on the DIABLO wheeled bipedal robot, about 1400 m at 1.2 m\u002Fs, start and end at the same place; end-to-end error",[],1790510660786]