[{"data":1,"prerenderedAt":593},["ShallowReactive",2],{"method-steamlio2025":3},{"method":4,"reference":67,"equipment":89,"figures":149,"results":150},{"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":30,"sensors":36,"platform":40,"estimator":43,"association":44,"timeModel":45,"deskew":46,"loopClosure":47,"globalOptimization":48,"mapRepresentation":49,"prior":50,"outputGeometry":51,"compute":52,"codeUrl":53,"codeLicense":54,"relatedVersions":55},"steamlio2025","Burnett et al., 2025","STEAM-LIO (GP continuous-time LIO)","Continuous-Time Radar-Inertial and Lidar-Inertial Odometry Using a Gaussian Process Motion Prior",2025,"recent","C05","odometry_with_local_mapping","本文以高斯過程（白雜訊加速度，即近似等速）作為連續時間運動先驗，在滑動視窗（約兩個 LiDAR 影格）中批次估計 SE(3) 位姿、機體速度與 IMU 偏差。因角速度屬於狀態，陀螺儀直接作為狀態量測；加速度計則只預積分成相對速度因子，其餘位置積分交給高斯過程。LiDAR 點以連續時間點到平面因子加入，每個點的位姿由相鄰兩個估計時刻的後驗內插取得，因此去畸變與配準在同一最佳化中反覆更新；由於先驗為稀疏的馬可夫形式，預積分與內插的計算量隨估計時刻數線性增加。同一框架也用於二維旋轉雷達，形成雷達慣性里程計。","Continuous-time radar- and lidar-inertial odometry that uses an exactly sparse white-noise-on-acceleration Gaussian-process prior in a two-frame sliding window, treats gyroscope readings as direct state measurements, preintegrates only accelerometer readings into relative-velocity factors, and builds point-to-plane (or Doppler-compensated radar) factors by posterior GP interpolation at each measurement time.","full_text_reviewed","peer_reviewed_published","supplementary","論文未涉及營建場域，驗證為城市車載（KITTI-raw、Boreas 一年四季含暴雪）與 Newer College 手持劇烈運動資料。它把本資料庫的 STEAM 高斯過程理論 [barfoot2014gp] 落實為可即時運作的 LiDAR 慣性里程計，並示範同一框架可用於在雪、霧、粉塵中較不受影響的旋轉雷達；營建工地常見粉塵與手持劇烈晃動，雷達慣性與連續時間去畸變的組合有潛在價值，但雷達為二維且精度明顯低於 LiDAR（推論）。",[20,21,22],"public_benchmark","independent_reference","cross_site",[24,25,26,27,28,29],"On Newer College, STEAM-LIO had the lowest RMS ATE on 01-Short (0.3042 m) and the lowest overall ATE when errors of all sequences are concatenated (0.2946 m versus 0.3152 m FAST-LIO2 and 0.3048 m DLIO), running in real time (74 ms) (Table II, Sec. V-B)","Continuous-time LiDAR-only STEAM-LO did not fail on 06-Dynamic Spinning, where KISS-ICP and the constant-velocity baseline failed (Table II)","Most of the inertial gain came from the gyroscope; adding the accelerometer gave only a minor further improvement (Sec. V-B, VI)","STEAM-LIO was much less sensitive to the prior power spectral density than STEAM-LO, which failed when Q was doubled (Table IV)","Adding an IMU improved radar odometry by 43% (1.68% to 0.95% average drift on Boreas) (Table III, Sec. V-D)","Estimated uncertainty was nearly consistent (NEES 1.04 on a Boreas snowstorm sequence) (Sec. V-D)",[31,32,33,34,35],"On Boreas the IMU gave little improvement over LiDAR-only odometry for a slow ground vehicle (0.45% versus 0.46% average drift) (Table III, Sec. V-D)","Relies on the robot being stationary at startup for gravity initialization (Sec. IV-B)","Including acceleration in the state (white-noise-on-jerk or Singer priors) improved some datasets but made the pipeline less reliable, so it was left out (Sec. I)","Posterior-interpolated measurement factors are an approximation rather than exact marginalization of measurement times (Sec. IV)","No explicit loop closure; maps drift slightly (Sec. V-D)",[37,38,39],"3D spinning LiDAR (Velodyne Alpha-Prime 128-beam on Boreas; 64-beam Ouster in Newer College; 64-beam Velodyne in KITTI-raw, LiDAR-only)","IMU (Applanix raw IMU at 200 Hz on Boreas; Ouster internal IMU at 100 Hz in Newer College)","2D spinning radar (Navtech CIR304-H) for the radar-inertial variant",[41,42],"vehicle (Boreas and KITTI-raw)","handheld (Newer College sensor mast)","sliding-window batch continuous-time estimation (window of two LiDAR frames, about 200 ms) with a white-noise-on-acceleration Gaussian-process prior on SE(3) pose and body-centric velocity, direct gyroscope factors, preintegrated accelerometer relative-velocity factors and bias random-walk priors; Gauss-Newton with outer re-association loops and marginalization (Sec. III, IV, IV-C)","continuous-time point-to-plane factors from a coarsely voxelized scan (default 1.5 m) to a sliding local voxel map (1.0 m voxels, up to 20 points, minimum spacing 0.1 m), weighted by a planarity heuristic; radar uses Doppler-compensated point-to-point factors with a Cauchy loss (Sec. IV, IV-A, IV-D)","continuous-time Gaussian process (WNOA prior, exactly sparse, linear cost) with estimation times at scan start and end; measurement factors built by posterior GP interpolation at each timestamp; timestamps binned (400 Hz on Boreas) to limit interpolations (Sec. III, IV-A, V)","each outer iteration undistorts the scan with the posterior continuous-time trajectory of the previous iteration (Sec. IV-A, Alg. 1)","none explicit; on Newer College the incrementally built map allows implicit loop closure when areas are revisited (Sec. V)","none","sliding local voxel point map centred on the robot; on Boreas voxels unobserved for about one second are cleared (Sec. IV, V)","gravity orientation estimated from accelerometer data assuming the robot is stationary at startup; tuned power spectral density diag(Q) = {50, 50, 50, 5, 5, 5} (Sec. IV-B, V)","continuous-time trajectory (pose and body-centric velocity with covariance) and lidar or radar point maps (Figs. 1, 9, 18)","real time on an Intel Xeon E5-2698 v4 with 16 threads: STEAM-LIO 74 ms per frame on Newer College and 97 ms on Boreas; STEAM-LO 89 ms on KITTI-raw (Tables I-III)","https:\u002F\u002Fgithub.com\u002FutiasASRL\u002Fsteam_icp","MIT (LICENSE file checked)",[56,60,63],{"relation":57,"title":58,"doi_or_url":59},"preprint","arXiv 2402.06174 (v1 2024-02-09, v2 2024-11-20 accepted version)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.06174",{"relation":61,"title":62,"doi_or_url":53},"code_release","utiasASRL\u002Fsteam_icp (STEAM-LO, STEAM-LIO, STEAM-RO, STEAM-RIO)",{"relation":64,"title":65,"doi_or_url":66},"predecessor_method","Exactly sparse GP trajectory estimation (STEAM) theory","barfoot2014gp",{"id":5,"kind":68,"shortName":7,"title":8,"authors":69,"year":9,"venue":73,"venueType":74,"publisher":75,"volumeIssuePages":76,"doi":77,"arxivId":78,"url":79,"firstPublicDate":80,"publicationStatus":16,"metadataStatus":81,"fulltextStatus":15,"era":10,"classicReason":82,"codeUrl":53,"cluster":11,"topics":83,"mdpi":84,"verification":85,"label":6,"fulltextRoute":86,"versionRead":87,"addedByCensus":88},"method",[70,71,72],"Keenan Burnett","Angela P. Schoellig","Timothy D. Barfoot","IEEE Transactions on Robotics","journal","IEEE","41:1059-1076","10.1109\u002Ftro.2024.3521856","2402.06174","https:\u002F\u002Fdoi.org\u002F10.1109\u002FTRO.2024.3521856","2024-02-09","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v2 (2024-11-20), the version accepted to T-RO; IEEE version of record not read",true,[90,97,102,108,113,118,123,126,131,134,141,146],{"category":91,"model":92,"canonical":92,"role":93,"dataset":94,"specs":95,"locator":96},"lidar","Velodyne Alpha-Prime 128-beam","dataset sensor","Boreas","128-beam lidar on the Boreas data collection platform","Fig. 14; Sec. V-D",{"category":98,"model":99,"canonical":99,"role":93,"dataset":94,"specs":100,"locator":101},"radar","Navtech CIR304-H","mechanical spinning 2D radar, 360 deg horizontal FOV, 1600 Hz azimuth measurements; no Doppler output","Sec. IV-D; Fig. 14",{"category":103,"model":104,"canonical":104,"role":105,"dataset":94,"specs":106,"locator":107},"gnss","Applanix GNSS\u002FINS (model not stated)","reference or ground truth","post-processed ground truth with GPS corrections, IMU and wheel encoders; raw 200 Hz IMU extracted without bias correction for the method","Sec. V-D; Fig. 14",{"category":109,"model":110,"canonical":110,"role":93,"dataset":94,"specs":111,"locator":112},"imu","Applanix IMU (raw measurements from the GNSS\u002FINS logs)","200 Hz","Sec. V-D; Fig. 3",{"category":114,"model":115,"canonical":115,"role":93,"dataset":94,"specs":116,"locator":117},"camera","FLIR Blackfly S","not used by the method","Fig. 14",{"category":91,"model":119,"canonical":119,"role":93,"dataset":120,"specs":121,"locator":122},"Ouster 64-beam LiDAR (model not stated)","Newer College Dataset","handheld sensor mast","Sec. V-B",{"category":109,"model":124,"canonical":124,"role":93,"dataset":120,"specs":125,"locator":122},"Ouster internal IMU","100 Hz; used to avoid LiDAR-IMU synchronization problems",{"category":91,"model":127,"canonical":127,"role":93,"dataset":128,"specs":129,"locator":130},"Velodyne 64-beam LiDAR (model not stated)","KITTI-raw","motion-distorted raw point clouds","Sec. V-A",{"category":103,"model":132,"canonical":132,"role":105,"dataset":128,"specs":133,"locator":130},"OXTS RTK GPS (model not stated)","ground truth",{"category":135,"model":136,"canonical":136,"role":137,"dataset":138,"specs":139,"locator":140},"compute","Intel Xeon CPU E5-2698 v4","compute for runtime",null,"16 threads","Sec. V",{"category":142,"model":143,"canonical":143,"role":93,"dataset":120,"specs":144,"locator":145},"platform","handheld sensor mast (Newer College Dataset)","aggressive high-frequency motions, dynamic swinging of the mast","Sec. V; Sec. V-B",{"category":142,"model":147,"canonical":147,"role":93,"dataset":94,"specs":148,"locator":107},"Boreas data collection vehicle","repeated route at the University of Toronto over one year; 102 km or 4.3 h test set",[],{"totalRows":151,"groupCount":152,"groups":153,"others":577},88,7,[154,316,476,525],{"slug":155,"group":156,"sourceId":5,"sourceLabel":6,"table":157,"selfRows":158,"metrics":159,"seqs":170,"entrants":202,"cells":217,"outcomes":309,"locators":310,"hardware":311,"wordings":313,"notes":314},"steamlio2025-table-iii","steamlio2025:Table III","Table III",41,[160,164,167],{"label":161,"unit":162,"statistic":163,"alignment":82},"translational drift","%","mean",{"label":165,"unit":166,"statistic":163,"alignment":82},"rotational drift","deg\u002F100m",{"label":168,"unit":169,"statistic":163,"alignment":82},"Delta T","ms",[171,174,176,178,180,182,184,186,188,190,192,194,196,198,200],{"dataset":94,"sequence":172,"environment":173},"Seq. Avg. (13 sequences)","vehicle, University of Toronto repeated route, varying seasons and weather",{"dataset":94,"sequence":175,"environment":173},"per frame",{"dataset":94,"sequence":177,"environment":173},"2020-12-04",{"dataset":94,"sequence":179,"environment":173},"2021-01-26 (snowstorm)",{"dataset":94,"sequence":181,"environment":173},"2021-02-09",{"dataset":94,"sequence":183,"environment":173},"2021-03-09",{"dataset":94,"sequence":185,"environment":173},"2020-04-22",{"dataset":94,"sequence":187,"environment":173},"2021-06-29-18",{"dataset":94,"sequence":189,"environment":173},"2021-06-29-20",{"dataset":94,"sequence":191,"environment":173},"2021-09-08",{"dataset":94,"sequence":193,"environment":173},"2021-09-09",{"dataset":94,"sequence":195,"environment":173},"2021-10-05",{"dataset":94,"sequence":197,"environment":173},"2021-10-26",{"dataset":94,"sequence":199,"environment":173},"2021-11-06",{"dataset":94,"sequence":201,"environment":173},"2021-11-28",[203,205,207,209,211,213,215],{"name":204,"methodId":138,"linkable":84,"proposed":84,"self":84},"VTR3-Lidar [1]",{"name":206,"methodId":5,"linkable":88,"proposed":88,"self":88},"STEAM-LO",{"name":208,"methodId":5,"linkable":88,"proposed":88,"self":88},"STEAM-LIO",{"name":210,"methodId":5,"linkable":88,"proposed":88,"self":88},"STEAM-LO (SE2)",{"name":212,"methodId":138,"linkable":84,"proposed":84,"self":84},"VTR3-Radar [1]",{"name":214,"methodId":5,"linkable":88,"proposed":88,"self":88},"STEAM-RO",{"name":216,"methodId":5,"linkable":88,"proposed":88,"self":88},"STEAM-RIO",[218,222,225,228,230,232,233,235,236,238,240,242,243,246,248,250,253,255,257,260,262,264,266,268,270,272,274,275,277,278,279,280,282,283,286,287,289,290,292,294,297,298,301,302,304,305,307],[219,219,219,220,221,219,221,221,219],0,0.54,-1,[219,223,219,224,221,219,221,221,219],1,0.16,[219,226,223,227,221,219,219,221,219],2,250,[223,219,219,229,221,219,221,221,219],0.46,[223,223,219,231,221,219,221,221,219],0.15,[223,226,223,151,221,219,219,221,219],[226,219,219,234,221,219,221,221,219],0.45,[226,223,219,231,221,219,221,221,219],[226,226,223,237,221,219,219,221,219],97,[239,219,219,224,221,219,221,221,219],3,[239,223,219,241,221,219,221,221,219],0.06,[239,226,223,151,221,219,219,221,219],[244,219,219,245,221,219,221,221,219],4,2.02,[244,223,219,247,221,219,221,221,219],0.58,[244,226,223,249,221,219,219,221,219],75,[251,219,219,252,221,219,221,221,219],5,1.68,[251,223,219,254,221,219,221,221,219],0.49,[251,226,223,256,221,219,219,221,219],115,[258,219,219,259,221,219,221,221,219],6,0.95,[258,223,219,261,221,219,221,221,219],0.27,[258,226,223,263,221,219,219,221,219],139,[223,219,226,265,221,219,221,221,219],0.41,[226,219,226,267,221,219,221,221,219],0.39,[223,219,239,269,221,219,221,221,219],0.62,[226,219,239,271,221,219,221,221,219],0.53,[223,219,244,273,221,219,221,221,219],0.38,[226,219,244,273,221,219,221,221,219],[223,219,251,276,221,219,221,221,219],0.47,[226,219,251,229,221,219,221,221,219],[223,219,258,267,221,219,221,221,219],[226,219,258,267,221,219,221,221,219],[223,219,152,281,221,219,221,221,219],0.48,[226,219,152,281,221,219,221,221,219],[223,219,284,285,221,219,221,221,219],8,0.52,[226,219,284,285,221,219,221,221,219],[223,219,288,276,221,219,221,221,219],9,[226,219,288,276,221,219,221,221,219],[223,219,291,285,221,219,221,221,219],10,[226,219,291,293,221,219,221,221,219],0.55,[223,219,295,296,221,219,221,221,219],11,0.5,[226,219,295,254,221,219,221,221,219],[223,219,299,300,221,219,221,221,219],12,0.4,[226,219,299,273,221,219,221,221,219],[223,219,303,300,221,219,221,221,219],13,[226,219,303,265,221,219,221,221,219],[223,219,306,265,221,219,221,221,219],14,[226,219,306,308,221,219,221,221,219],0.37,[],[157],[312],"Intel Xeon E5-2698 v4, 16 threads",[],[315],"Boreas test set (102 km, 4.3 h, repeated route over one year incl. snowstorms); KITTI-style translational drift (%) and rotational drift (deg\u002F100 m); first three methods evaluated in SE(3), last four in SE(2) (radar is 2D)",{"slug":317,"group":318,"sourceId":5,"sourceLabel":6,"table":319,"selfRows":320,"metrics":321,"seqs":335,"entrants":348,"cells":374,"outcomes":468,"locators":470,"hardware":471,"wordings":473,"notes":474},"steamlio2025-table-ii","steamlio2025:Table II","Table II",24,[322,327,329,331,333],{"label":323,"unit":324,"statistic":325,"alignment":326},"root mean squared ATE","m","RMSE","SE3",{"label":328,"unit":169,"statistic":163,"alignment":82},"Delta T (163ms)",{"label":330,"unit":169,"statistic":163,"alignment":82},"Delta T (138ms)",{"label":332,"unit":169,"statistic":163,"alignment":82},"Delta T (76ms)",{"label":334,"unit":169,"statistic":163,"alignment":82},"Delta T (74ms)",[336,339,341,343,345,347],{"dataset":120,"sequence":337,"environment":338},"01-Short","handheld sensor mast, Oxford college quads and parkland",{"dataset":120,"sequence":340,"environment":338},"02-Long",{"dataset":120,"sequence":342,"environment":338},"05-Quad w\u002F Dynamics",{"dataset":120,"sequence":344,"environment":338},"06-Dynamic Spinning",{"dataset":120,"sequence":346,"environment":338},"07-Parkland Mound",{"dataset":120,"sequence":175,"environment":338},[349,352,355,358,361,364,366,368,370,372],{"name":350,"methodId":351,"linkable":88,"proposed":84,"self":84},"CT-ICP* [18] (explicit loop closures)","cticp2022",{"name":353,"methodId":354,"linkable":88,"proposed":84,"self":84},"KISS-ICP [5] (result from [71])","kissicp2023",{"name":356,"methodId":357,"linkable":88,"proposed":84,"self":84},"FAST-LIO2 [10] (result from [71])","fastlio2_2022",{"name":359,"methodId":360,"linkable":88,"proposed":84,"self":84},"DLIO [11]","dlio2023",{"name":362,"methodId":363,"linkable":88,"proposed":84,"self":84},"SLICT* [52] (explicit loop closures)","slict2023",{"name":365,"methodId":138,"linkable":84,"proposed":84,"self":84},"CLIO* [60] (loop closures, uses camera)",{"name":367,"methodId":5,"linkable":88,"proposed":84,"self":88},"Constant Velocity (ablation baseline)",{"name":369,"methodId":5,"linkable":88,"proposed":88,"self":88},"STEAM-LO (Ours)",{"name":371,"methodId":5,"linkable":88,"proposed":88,"self":88},"STEAM-LO + Gyro (Ours)",{"name":373,"methodId":5,"linkable":88,"proposed":88,"self":88},"STEAM-LIO (Ours)",[375,377,379,381,383,384,386,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,466],[219,219,219,376,221,219,221,221,219],0.36,[223,219,219,378,221,219,221,221,219],0.6675,[223,219,223,380,221,219,221,221,219],1.5311,[223,219,226,382,221,219,221,221,219],0.104,[223,219,239,138,219,219,221,221,219],[223,219,244,385,221,219,221,221,219],0.2027,[226,219,219,387,221,219,221,221,219],0.3775,[226,219,223,389,221,219,221,221,219],0.3324,[226,219,226,391,221,219,221,221,219],0.0879,[226,219,239,393,221,219,221,221,219],0.0771,[226,219,244,395,221,219,221,221,219],0.1483,[239,219,219,397,221,219,221,221,219],0.3606,[239,219,223,399,221,219,221,221,219],0.3268,[239,219,226,401,221,219,221,221,219],0.0837,[239,219,239,403,221,219,221,221,219],0.0612,[239,219,244,405,221,219,221,221,219],0.1196,[244,219,219,407,221,219,221,221,219],0.3843,[244,219,223,409,221,219,221,221,219],0.3496,[244,219,226,411,221,219,221,221,219],0.1155,[244,219,239,413,221,219,221,221,219],0.0844,[244,219,244,415,221,219,221,221,219],0.129,[251,219,219,417,221,219,221,221,219],0.408,[251,219,223,419,221,219,221,221,219],0.381,[251,219,239,421,221,219,221,221,219],0.091,[258,219,219,423,221,219,221,221,219],0.8558,[258,219,223,425,221,219,221,221,219],2.5792,[258,219,226,427,221,219,221,221,219],0.3575,[258,219,239,138,219,219,221,221,219],[258,219,244,430,221,219,221,221,219],0.596,[258,223,251,432,221,219,219,221,219],163,[152,219,219,434,221,219,221,221,219],0.3398,[152,219,223,436,221,219,221,221,219],0.4546,[152,219,226,438,221,219,221,221,219],0.1083,[152,219,239,440,221,219,221,221,219],0.0802,[152,219,244,442,221,219,221,221,219],0.1537,[152,226,251,444,221,219,219,221,219],138,[284,219,219,446,221,219,221,221,219],0.3055,[284,219,223,448,221,219,221,221,219],0.334,[284,219,226,450,221,219,221,221,219],0.109,[284,219,239,452,221,219,221,221,219],0.0824,[284,219,244,454,221,219,221,221,219],0.1444,[284,239,251,456,221,219,219,221,219],76,[288,219,219,458,221,219,221,221,219],0.3042,[288,219,223,460,221,219,221,221,219],0.3372,[288,219,226,462,221,219,221,221,219],0.1086,[288,219,239,464,221,219,221,221,219],0.0821,[288,219,244,454,221,219,221,221,219],[288,244,251,467,221,219,219,221,219],74,[469],"failed",[319],[472],"Intel Xeon E5-2698 v4, 16 threads (own runs); quoted values from cited papers",[],[475],"Newer College Dataset (handheld, 6 km); RMS ATE after Umeyama alignment; star = explicit loop closures, dagger = results from DLIOM [71], double dagger = uses camera; other baselines as originally published",{"slug":477,"group":478,"sourceId":5,"sourceLabel":6,"table":479,"selfRows":299,"metrics":480,"seqs":483,"entrants":496,"cells":499,"outcomes":519,"locators":520,"hardware":521,"wordings":522,"notes":523},"steamlio2025-table-iv","steamlio2025:Table IV","Table IV",[481],{"label":482,"unit":324,"statistic":325,"alignment":326},"ATE",[484,486,488,490,492,494],{"dataset":120,"sequence":485,"environment":121},"01-Short, Q x1\u002F4",{"dataset":120,"sequence":487,"environment":121},"01-Short, Q x1\u002F2",{"dataset":120,"sequence":489,"environment":121},"01-Short, Q x1",{"dataset":120,"sequence":491,"environment":121},"01-Short, Q x2",{"dataset":120,"sequence":493,"environment":121},"01-Short, Q x4",{"dataset":120,"sequence":495,"environment":121},"01-Short, Q x8",[497,498],{"name":206,"methodId":5,"linkable":88,"proposed":88,"self":88},{"name":208,"methodId":5,"linkable":88,"proposed":88,"self":88},[500,502,504,506,508,509,510,511,513,514,516,517],[219,219,219,501,221,219,221,221,219],0.3098,[223,219,219,503,221,219,221,221,219],0.308,[219,219,223,505,221,219,221,221,219],0.3287,[223,219,223,507,221,219,221,221,219],0.3071,[219,219,226,434,221,219,221,221,219],[223,219,226,458,221,219,221,221,219],[219,219,239,138,219,219,221,221,219],[223,219,239,512,221,219,221,221,219],0.3057,[219,219,244,138,219,219,221,221,219],[223,219,244,515,221,219,221,221,219],0.3083,[219,219,251,138,219,219,221,221,219],[223,219,251,518,221,219,221,221,219],0.3056,[469],[479],[],[],[524],"Ablation: ATE on Newer College 01-Short when scaling the default power spectral density diag(Q) = {50, 50, 50, 5, 5, 5}",{"slug":526,"group":527,"sourceId":5,"sourceLabel":6,"table":528,"selfRows":258,"metrics":529,"seqs":536,"entrants":543,"cells":553,"outcomes":570,"locators":571,"hardware":572,"wordings":574,"notes":575},"steamlio2025-table-i","steamlio2025:Table I","Table I",[530,532,534],{"label":531,"unit":162,"statistic":163,"alignment":82},"KITTI RTE",{"label":533,"unit":169,"statistic":163,"alignment":82},"Delta T (44ms)",{"label":535,"unit":169,"statistic":163,"alignment":82},"Delta T (89ms)",[537,540,542],{"dataset":128,"sequence":538,"environment":539},"Overall (00-10 without 03)","vehicle, urban and highway",{"dataset":128,"sequence":541,"environment":539},"Seq. 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Avg. = mean of per-sequence values; sequence 03 not available; STEAM-LO evaluated at the newest pose of the window",[578,583,588],{"group":579,"slug":580,"sourceLabel":6,"table":581,"selfRows":239,"datasets":582},"steamlio2025:Table V","steamlio2025-table-v","Table V",[94],{"group":584,"slug":585,"sourceLabel":6,"table":586,"selfRows":223,"datasets":587},"steamlio2025:Text Sec.V-B","steamlio2025-text-sec-v-b","Text Sec.V-B",[120],{"group":589,"slug":590,"sourceLabel":6,"table":591,"selfRows":223,"datasets":592},"steamlio2025:Text Sec.V-D","steamlio2025-text-sec-v-d","Text Sec.V-D",[94],1790510660471]