[{"data":1,"prerenderedAt":578},["ShallowReactive",2],{"method-vilens2023":3},{"method":4,"reference":66,"equipment":85,"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":29,"sensors":37,"platform":43,"estimator":45,"association":46,"timeModel":47,"deskew":48,"loopClosure":49,"globalOptimization":50,"mapRepresentation":51,"prior":52,"outputGeometry":53,"compute":54,"codeUrl":55,"codeLicense":56,"relatedVersions":57},"vilens2023","Wisth et al., 2023","VILENS","VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots",2023,"recent","C07","odometry","VILENS 是針對足式機器人的里程計，以因子圖（factor graph）在固定時間窗內緊耦合（tightly coupled）融合 IMU、腿部運動學、相機與 LiDAR 四種感測器。其關鍵在於把腿部運動學換算的速度預積分成因子，並在狀態中加入可線上估計的線速度偏差，用來吸收足端滑移、地面變形與足部橡膠形變造成的系統性漂移；此偏差需靠與外部感測器的緊耦合才可觀測。LiDAR 部分同時使用高頻的平面與線特徵追蹤，以及低頻、以局部子地圖為目標的 ICP 相對位姿因子。系統只做里程計，不含迴圈閉合，也不輸出全域點雲地圖。","VILENS tightly fuses IMU, leg kinematics, cameras and lidar in a fixed-lag factor graph, adding an online-estimated velocity bias to the preintegrated leg-odometry factor to absorb slippage and terrain deformation; it is an odometry system without loop closure.","full_text_reviewed","peer_reviewed_published","main_body","未在施工工地測試。實驗場域為瑞士軍事救援訓練場、英國消防學院戶外工業訓練場、DARPA SubT 城市賽道（停用核電廠的地下設施）、石灰岩礦與 Seemühle 礦坑，共 2 小時、1.8 km（Sec. VI-B）。與營建的關聯是平台層面：C11b 收錄的多篇營建研究使用四足機器人（如 [kim2022scaffoldrobotdog][schillberg2025quadrupedasbuilt][tuomisto2026quadrupedbim][chen2025quadrupedinspection][gan2025decoupled]），但依 C11b 紀錄，至少 [kim2022scaffoldrobotdog] 使用 LIO-SAM 而非足式專用估測器。另依 C10 紀錄，VILENS 的 ICP 模組曾離線用於產生 Hilti-Oxford 與 Oxford Spires 的稠密參考軌跡（[zhang2023hiltioxford] Sec. V-C；[tao2025oxfordspires] Sec. 5.1.7）。",[20,21,22],"underground_or_tunnel","independent_reference","cross_site",[24,25,26,27,28],"Full VILENS reached the lowest mean 10 m RPE across five datasets, 0.10 m translation and 0.96 deg rotation, although not the lowest on every sequence (e.g., SMR rotation 1.30 deg vs 1.18 deg for VILENS-LVI; FSC translation tied with VILENS-IR at 0.15 m) (Table II)","Average improvement of 62% translational and 51% rotational error versus the loosely coupled CompSLAM baseline (abstract)","Online velocity-bias estimation reduced RPE on average by 9.0% (translation) and 5.9% (rotation) (Sec. VI)","Handles degenerate scenes (dark, dusty, feature-poor, long corridors) without hand-engineered modality switching (Sec. VII-B)","Using kinematics inside the graph instead of an external filter reduced position drift by 45% in a kinematic-inertial-only test (Sec. VI-E)",[30,31,32,33,34,35,36],"ICP registration is prone to failure in degenerate geometry such as long tunnels; a robust cost is used to reject such factors (Sec. IV-F)","Lidar registration covariance is a constant set empirically (Sec. IV-F)","In the SMM tunnel sequence ICP was unstable and close to divergence, so it was excluded from that analysis (Sec. VII-B)","Odometry only, no loop closure, so drift accumulates over long trajectories (Sec. VI-C, Figs. 9-10)","Accelerometer biases are initialized as zero or a known constant and converge only after motion (Sec. V-G)","(inference) Point-cloud or map geometric quality is not evaluated; evidence is limited to trajectory RPE","Observability of the linear velocity bias is argued and confirmed only empirically; an analytic proof of observability and convergence is left to future work (Appendix C)",[38,39,40,41,42],"IMU (Xsens MTi-100 on ANYmal B300; Epson G365 on ANYmal C100; 400 Hz)","leg kinematics (ANYdrive joint encoders and torque sensors, 400 Hz)","3D LiDAR (Velodyne VLP-16, 10 Hz)","stereo camera (RealSense D435i gray stereo 848x480 at 30 Hz, or Sevensense Alphasense gray stereo 720x540 at 30 Hz)","monocular fisheye camera (FLIR BFS-U3-16S2C-CS RGB, 1440x1080 at 30 Hz, 150 deg diagonal FoV; SUB configuration)",[44],"legged (ANYmal B300 and C100 quadrupeds)","fixed-lag factor-graph smoothing with iSAM2 in GTSAM (5 s lag), with preintegrated IMU factors and a preintegrated leg-velocity factor whose linear and angular velocity biases are estimated online (Sec. III, IV, V)","visual FAST\u002FKLT feature tracks with reprojection factors and lidar-derived depth; tracked lidar plane and line primitives with anchor-frame residuals; ICP (after Pomerleau et al.) against a local submap of the last 5 m of scans, added as a relative-pose factor at about 2 Hz; DCS robust cost on visual and lidar factors (Sec. IV-D to IV-F, V)","discrete keyframe states (5-15 Hz) with IMU forward propagation at 400 Hz (Sec. V, V-D, Table V)","lidar points motion-compensated with the IMU-propagated state, referenced to the closest camera keyframe timestamp (Sec. V-A)","none (odometry only; authors state it can be integrated with an external SLAM system, Sec. VI-C)","none","no global map; local ICP submap of scans registered over the last 5 m travelled; plane\u002Fline landmarks inside the factor graph (Sec. IV-F)","none in the estimator (prior survey-grade maps used only to generate ground truth in some experiments, Sec. VI-B)","pose and velocity estimates: IMU-propagated at 400 Hz, factor-graph optimized at 10 Hz, ICP-optimized at 2 Hz (Table V); local elevation mapping is done by downstream modules; no exported global point cloud is reported","real-time onboard operation; timing on a laptop with Intel E-2186M (6 cores) and 16 GB RAM, lidar ICP about 150 ms per call at 2 Hz and optimization about 8.7 ms (Table IV)",null,"not_applicable",[58,62],{"relation":59,"title":60,"doi_or_url":61},"preprint","VILENS (arXiv 2107.07243, v1 2021-07-15, v3 2024-10-07 carrying the T-RO header)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2107.07243",{"relation":63,"title":64,"doi_or_url":65},"conference_version","Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry (ICRA 2020; the T-RO paper states it significantly extends this work)","10.1109\u002FICRA40945.2020.9197214",{"id":5,"kind":67,"shortName":7,"title":8,"authors":68,"year":9,"venue":72,"venueType":73,"publisher":74,"volumeIssuePages":75,"doi":76,"arxivId":77,"url":61,"firstPublicDate":78,"publicationStatus":16,"metadataStatus":79,"fulltextStatus":15,"era":10,"classicReason":56,"codeUrl":55,"cluster":11,"topics":80,"mdpi":81,"verification":82,"label":6,"fulltextRoute":83,"versionRead":84,"addedByCensus":81},"method",[69,70,71],"David Wisth","Marco Camurri","Maurice Fallon","IEEE Transactions on Robotics","journal","IEEE","39(1), 309-326","10.1109\u002Ftro.2022.3193788","2107.07243","2021-07-15","metadata_verified",[11],false,"confirmed","arXiv","arXiv v3 (2024-10-07), accepted manuscript carrying the IEEE T-RO 39(1):309-326 header; IEEE version of record not compared",[86,92,95,100,103,107,111,114,120,124,127,133,138,143],{"category":87,"model":88,"canonical":88,"role":89,"dataset":55,"specs":90,"locator":91},"platform","ANYmal B300","method input","quadruped, 4 legs, 12 active DoF; stock and DARPA SubT-modified versions (SMR, FSC, SUB)","Sec. VI-A, Fig. 1",{"category":87,"model":93,"canonical":93,"role":89,"dataset":55,"specs":94,"locator":91},"ANYmal C100","quadruped (LSM, SMM)",{"category":96,"model":97,"canonical":97,"role":89,"dataset":55,"specs":98,"locator":99},"wheel_or_leg_odometry","ANYdrive joint encoder","400 Hz, resolution \u003C 0.025 deg","Table I",{"category":96,"model":101,"canonical":101,"role":89,"dataset":55,"specs":102,"locator":99},"ANYdrive torque sensor","400 Hz, resolution \u003C 0.1 N m",{"category":104,"model":105,"canonical":105,"role":89,"dataset":55,"specs":106,"locator":99},"lidar","Velodyne VLP-16","10 Hz, resolution 16 px x 1824 px",{"category":108,"model":109,"canonical":109,"role":89,"dataset":55,"specs":110,"locator":99},"imu","Xsens MTi-100","400 Hz; initial bias 0.2 deg\u002Fs and 5 mg; bias stability 10 deg\u002Fh and 15 mg (ANYmal B300: SMR, FSC, SUB)",{"category":108,"model":112,"canonical":112,"role":89,"dataset":55,"specs":113,"locator":99},"Epson G365","400 Hz; initial bias 0.1 deg\u002Fs and 3 mg; bias stability 1.2 deg\u002Fh and 15 mg (ANYmal C100: LSM, SMM)",{"category":115,"model":116,"canonical":117,"role":89,"dataset":55,"specs":118,"locator":119},"stereo_camera","RealSense D435i","Intel RealSense D435I","gray stereo, 30 Hz, 848 px x 480 px, diagonal FoV 100.6 deg; software-synchronized (SMR, FSC)","Table I, Sec. V-F",{"category":121,"model":122,"canonical":122,"role":89,"dataset":55,"specs":123,"locator":99},"camera","FLIR BFS-U3-16S2C-CS","RGB mono fisheye, 30 Hz, 1440 px x 1080 px, diagonal FoV 150 deg (SUB)",{"category":115,"model":125,"canonical":125,"role":89,"dataset":55,"specs":126,"locator":99},"Sevensense Alphasense","gray stereo, 30 Hz, 720 px x 540 px, diagonal FoV 165.4 deg (LSM, SMM)",{"category":128,"model":129,"canonical":129,"role":130,"dataset":55,"specs":131,"locator":132},"total_station","Leica TS16 (called a laser tracker in the paper)","reference or ground truth","tracks the robot; orientation estimated by an optimization-based method (SMR, FSC)","Sec. VI-B, Fig. 15",{"category":134,"model":135,"canonical":135,"role":130,"dataset":55,"specs":136,"locator":137},"tls_scanner","survey-grade lidar scanners (models not_reported)","accurate prior maps; ground truth by ICP of the robot scans to the prior map (SUB, LSM, SMM)","Sec. VI-B",{"category":139,"model":140,"canonical":140,"role":130,"dataset":55,"specs":141,"locator":142},"other","Vicon motion capture","200 Hz, used as velocity ground truth","Sec. VI-E, Sec. VII-D, Fig. 18",{"category":144,"model":145,"canonical":145,"role":146,"dataset":55,"specs":147,"locator":148},"compute","Intel E-2186M","compute for runtime","processor in a laptop; 6 cores\u002F12 threads, 2.9 GHz base frequency; 16 GB RAM","Sec. VII-D, Table IV",[],{"totalRows":151,"groupCount":152,"groups":153,"others":565},84,6,[154,401,479,532],{"slug":155,"group":156,"sourceId":5,"sourceLabel":6,"table":157,"selfRows":158,"metrics":159,"seqs":240,"entrants":260,"cells":270,"outcomes":393,"locators":394,"hardware":395,"wordings":396,"notes":397},"vilens2023-table-ii","vilens2023:Table II","Table II",48,[160,165,167,169,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238],{"label":161,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.12)","m","mean","not_reported",{"label":166,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.15)",{"label":168,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.11)",{"label":170,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.97)","deg",{"label":173,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 1.05)",{"label":175,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.93)",{"label":177,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 1.07)",{"label":179,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.09)",{"label":181,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.17)",{"label":183,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.07)",{"label":185,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.90)",{"label":187,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.94)",{"label":189,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.86)",{"label":191,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.78)",{"label":193,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.08)",{"label":195,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.03)",{"label":197,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.40)",{"label":199,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.43)",{"label":201,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 1.13)",{"label":203,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.34)",{"label":205,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.30)",{"label":207,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.33)",{"label":209,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.04)",{"label":211,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 1.73)",{"label":213,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 1.51)",{"label":215,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.71)",{"label":217,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.39)",{"label":219,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.99)",{"label":221,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.90)",{"label":223,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.22)",{"label":225,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 6.60)",{"label":227,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 5.26)",{"label":229,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.61)",{"label":231,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.31)",{"label":233,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.32)",{"label":235,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 2.12)",{"label":237,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 1.84)",{"label":239,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.64)",[241,245,248,251,254,257],{"dataset":242,"sequence":243,"environment":244},"authors' ANYmal datasets","SMR","Swiss Military Rescue Facility, Wangen CH; concrete, gravel and grass (ANYmal B300, 106 m, 13 min)",{"dataset":242,"sequence":246,"environment":247},"FSC","Fire Service College, UK; outdoor industrial site with standing water, oil, gravel and mud (ANYmal B300, 240 m, 34 min)",{"dataset":242,"sequence":249,"environment":250},"SUB","DARPA SubT Urban Beta course, Satsop WA; dark inactive nuclear power plant (ANYmal B300, 490 m, 60 min)",{"dataset":242,"sequence":252,"environment":253},"LSM","decommissioned limestone mine, Wiltshire UK (ANYmal C100, 474 m, 20 min)",{"dataset":242,"sequence":255,"environment":256},"SMM","Seemuehle mine CH, autonomous exploration (ANYmal C100, 522 m, 17 min)",{"dataset":242,"sequence":258,"environment":259},"Mean","mean over the five experiments",[261,264,266,268],{"name":262,"methodId":5,"linkable":263,"proposed":81,"self":263},"VILENS-LVI (ablation: lidar and visual features with IMU)",true,{"name":265,"methodId":5,"linkable":263,"proposed":81,"self":263},"VILENS-LVIK (ablation: adds leg kinematics)",{"name":267,"methodId":5,"linkable":263,"proposed":81,"self":263},"VILENS-IR (ablation: ICP and IMU only, output 2 Hz)",{"name":269,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS (full)",[271,275,278,281,284,286,289,292,294,297,299,300,302,304,307,310,313,315,317,319,322,325,328,331,334,337,340,341,344,347,350,353,356,358,360,363,364,367,370,372,375,378,380,382,383,386,388,390],[272,272,272,273,274,272,274,274,272],0,0.14,-1,[276,272,272,277,274,272,274,274,272],1,0.15,[279,276,272,280,274,272,274,274,272],2,0.24,[282,279,272,283,274,272,274,274,272],3,0.12,[272,282,272,285,274,272,274,274,272],1.18,[276,287,272,288,274,272,274,274,272],4,1.38,[279,290,272,291,274,272,274,274,272],5,1.69,[282,152,272,293,274,272,274,274,272],1.3,[272,295,276,296,274,272,274,274,272],7,0.2,[276,298,276,280,274,272,274,274,272],8,[279,295,276,277,274,272,274,274,272],[282,301,276,277,274,272,274,274,272],9,[272,303,276,293,274,272,274,274,272],10,[276,305,276,306,274,272,274,274,272],11,1.17,[279,308,276,309,274,272,274,274,272],12,2.03,[282,311,276,312,274,272,274,274,272],13,1.14,[272,301,279,314,274,272,274,274,276],0.11,[276,316,279,314,274,272,274,274,276],14,[279,316,279,318,274,272,274,274,276],0.1,[282,320,279,321,274,272,274,274,276],15,0.05,[272,323,279,324,274,272,274,274,276],16,0.75,[276,326,279,327,274,272,274,274,276],17,0.74,[279,329,279,330,274,272,274,274,276],18,1.88,[282,332,279,333,274,272,274,274,276],19,0.56,[272,335,282,336,274,272,274,274,276],20,0.34,[276,338,282,339,274,272,274,274,276],21,0.29,[279,316,282,318,274,272,274,274,276],[282,342,282,343,274,272,274,274,276],22,0.04,[272,345,282,346,274,272,274,274,276],23,2.32,[276,348,282,349,274,272,274,274,276],24,1.92,[279,351,282,352,274,272,274,274,276],25,1.44,[282,354,282,355,274,272,274,274,276],26,0.59,[272,357,287,327,274,272,274,274,276],27,[276,359,287,327,274,272,274,274,276],28,[279,361,287,362,274,272,274,274,276],29,0.27,[282,316,287,283,274,272,274,274,276],[272,365,287,366,274,272,274,274,276],30,4.73,[276,368,287,369,274,272,274,274,276],31,4.38,[279,345,287,371,274,272,274,274,276],2.73,[282,373,287,374,274,272,274,274,276],32,1.19,[272,376,290,377,274,272,274,274,279],33,0.31,[276,379,290,377,274,272,274,274,279],34,[279,272,290,381,274,272,274,274,279],0.17,[282,301,290,318,274,272,274,274,279],[272,384,290,385,274,272,274,274,279],35,2.06,[276,387,290,349,274,272,274,274,279],36,[279,152,290,389,274,272,274,274,279],1.95,[282,391,290,392,274,272,274,274,279],37,0.96,[],[157],[],[],[398,399,400],"Mean 10 m RPE with std in parentheses; VILENS variants run at 15 Hz unless noted; ground truth Leica TS16 tracking","Mean 10 m RPE with std in parentheses; VILENS variants run at 15 Hz unless noted; ground truth ICP to survey-grade prior map","Mean 10 m RPE with std in parentheses; VILENS variants run at 15 Hz unless noted; ground truth mixed",{"slug":402,"group":403,"sourceId":5,"sourceLabel":6,"table":404,"selfRows":348,"metrics":405,"seqs":430,"entrants":437,"cells":441,"outcomes":473,"locators":474,"hardware":475,"wordings":476,"notes":477},"vilens2023-table-iii","vilens2023:Table III","Table III",[406,407,408,409,410,411,412,414,415,417,418,420,421,422,423,424,426,427,429],{"label":161,"unit":162,"statistic":163,"alignment":164},{"label":168,"unit":162,"statistic":163,"alignment":164},{"label":187,"unit":171,"statistic":163,"alignment":164},{"label":177,"unit":171,"statistic":163,"alignment":164},{"label":166,"unit":162,"statistic":163,"alignment":164},{"label":183,"unit":162,"statistic":163,"alignment":164},{"label":413,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.79)",{"label":191,"unit":171,"statistic":163,"alignment":164},{"label":416,"unit":162,"statistic":163,"alignment":164},"10 m RPE translation mu (sigma = 0.05)",{"label":195,"unit":162,"statistic":163,"alignment":164},{"label":419,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.41)",{"label":203,"unit":171,"statistic":163,"alignment":164},{"label":209,"unit":162,"statistic":163,"alignment":164},{"label":179,"unit":162,"statistic":163,"alignment":164},{"label":193,"unit":162,"statistic":163,"alignment":164},{"label":425,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.62)",{"label":229,"unit":171,"statistic":163,"alignment":164},{"label":428,"unit":171,"statistic":163,"alignment":164},"10 m RPE rotation mu (sigma = 0.65)",{"label":239,"unit":171,"statistic":163,"alignment":164},[431,432,433,434,435,436],{"dataset":242,"sequence":243,"environment":244},{"dataset":242,"sequence":246,"environment":247},{"dataset":242,"sequence":249,"environment":250},{"dataset":242,"sequence":252,"environment":253},{"dataset":242,"sequence":255,"environment":256},{"dataset":242,"sequence":258,"environment":259},[438,440],{"name":439,"methodId":5,"linkable":263,"proposed":81,"self":263},"VILENS-NO-BIAS (ablation: no online velocity bias)",{"name":269,"methodId":5,"linkable":263,"proposed":263,"self":263},[442,443,444,445,446,448,449,451,452,454,455,457,458,459,460,461,462,464,465,467,468,469,470,472],[272,272,272,283,274,272,274,274,272],[276,276,272,283,274,272,274,274,272],[272,279,272,293,274,272,274,274,272],[276,282,272,293,274,272,274,274,272],[272,287,276,447,274,272,274,274,272],0.21,[276,290,276,277,274,272,274,274,272],[272,152,276,450,274,272,274,274,272],1.24,[276,295,276,312,274,272,274,274,272],[272,298,279,453,274,272,274,274,272],0.06,[276,301,279,321,274,272,274,274,272],[272,303,279,456,274,272,274,274,272],0.64,[276,305,279,333,274,272,274,274,272],[272,308,282,321,274,272,274,274,272],[276,308,282,343,274,272,274,274,272],[272,303,282,456,274,272,274,274,272],[276,305,282,333,274,272,274,274,272],[272,311,287,463,274,272,274,274,272],0.13,[276,316,287,283,274,272,274,274,272],[272,320,287,466,274,272,274,274,272],1.12,[276,323,287,374,274,272,274,274,272],[272,311,290,314,274,272,274,274,272],[276,290,290,318,274,272,274,274,272],[272,326,290,471,274,272,274,274,272],1.02,[276,329,290,392,274,272,274,274,272],[],[404],[],[],[478],"Ablation of online velocity bias estimation; mean 10 m RPE with std in parentheses (LSM rotation row duplicates SUB and differs from Table II)",{"slug":480,"group":481,"sourceId":5,"sourceLabel":6,"table":482,"selfRows":152,"metrics":483,"seqs":497,"entrants":500,"cells":513,"outcomes":525,"locators":526,"hardware":527,"wordings":529,"notes":530},"vilens2023-table-iv","vilens2023:Table IV","Table IV",[484,487,489,491,493,495],{"label":485,"unit":486,"statistic":163,"alignment":56},"Timing mu (sigma = 0.12) ms, module IMU at 400 Hz","ms",{"label":488,"unit":486,"statistic":163,"alignment":56},"Timing mu (sigma = 0.30) ms, module Leg kinematics at 400 Hz",{"label":490,"unit":486,"statistic":163,"alignment":56},"Timing mu (sigma = 7.69) ms, module Visual features with lidar depth at 10 Hz",{"label":492,"unit":486,"statistic":163,"alignment":56},"Timing mu (sigma = 6.24) ms, module Lidar point cloud features at 10 Hz",{"label":494,"unit":486,"statistic":163,"alignment":56},"Timing mu (sigma = 59.23) ms, module Lidar ICP at 2 Hz",{"label":496,"unit":486,"statistic":163,"alignment":56},"Timing mu (sigma = 3.25) ms, module Optimization at 10 Hz",[498],{"dataset":242,"sequence":499,"environment":499},"",[501,503,505,507,509,511],{"name":502,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS module: IMU",{"name":504,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS module: Leg kinematics",{"name":506,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS module: Visual features with lidar depth",{"name":508,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS module: Lidar point cloud features",{"name":510,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS module: Lidar ICP",{"name":512,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS module: Optimization",[514,515,517,519,521,523],[272,272,272,321,274,272,272,274,272],[276,276,272,516,274,272,272,274,272],0.07,[279,279,272,518,274,272,272,274,272],9.48,[282,282,272,520,274,272,272,274,272],19.72,[287,287,272,522,274,272,272,274,272],149.75,[290,290,272,524,274,272,272,274,272],8.65,[],[482],[528],"laptop, Intel E-2186M (6 cores, 12 threads, 2.9 GHz base), 16 GB RAM",[],[531],"Timing of VILENS modules, mean with std; module frequency given in the table",{"slug":533,"group":534,"sourceId":5,"sourceLabel":6,"table":535,"selfRows":282,"metrics":536,"seqs":543,"entrants":545,"cells":552,"outcomes":559,"locators":560,"hardware":561,"wordings":562,"notes":563},"vilens2023-table-v","vilens2023:Table V","Table V",[537,539,541],{"label":538,"unit":486,"statistic":163,"alignment":56},"Mean latency of output 'IMU forward-propagated' at 400 Hz",{"label":540,"unit":486,"statistic":163,"alignment":56},"Mean latency of output 'Factor graph optimized' at 10 Hz",{"label":542,"unit":486,"statistic":163,"alignment":56},"Mean latency of output 'ICP optimized' at 2 Hz",[544],{"dataset":242,"sequence":499,"environment":499},[546,548,550],{"name":547,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS output: IMU forward-propagated",{"name":549,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS output: Factor graph optimized",{"name":551,"methodId":5,"linkable":263,"proposed":263,"self":263},"VILENS output: ICP optimized",[553,555,557],[272,272,272,554,274,272,274,274,272],2.3,[276,276,272,556,274,272,274,274,272],95.2,[279,279,272,558,274,272,274,274,272],395.2,[],[535],[],[],[564],"Frequency and mean latency of VILENS outputs, latency relative to the IMU input",[566,572],{"group":567,"slug":568,"sourceLabel":569,"table":404,"selfRows":279,"datasets":570},"helmberger2022hilti:Table III","helmberger2022hilti-table-iii","Helmberger et al., 2022",[571],"Hilti SLAM Challenge Dataset (2021)",{"group":573,"slug":574,"sourceLabel":575,"table":482,"selfRows":276,"datasets":576},"okvis2x2025:Table IV","okvis2x2025-table-iv","Boche et al., 2025",[577],"Hilti-Oxford (Hilti 2022 challenge)",1790510662171]