[{"data":1,"prerenderedAt":618},["ShallowReactive",2],{"method-holisticfusion2026":3},{"method":4,"reference":70,"equipment":95,"figures":136,"results":137},{"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":30,"sensors":35,"platform":42,"estimator":46,"association":47,"timeModel":48,"deskew":49,"loopClosure":50,"globalOptimization":51,"mapRepresentation":52,"prior":53,"outputGeometry":54,"compute":55,"codeUrl":56,"codeLicense":57,"relatedVersions":58},"holisticfusion2026","Nubert et al., 2026","Holistic Fusion","Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation With Factor Graphs",2026,"recent","C07","estimation_framework_or_library","Holistic Fusion 是以 GTSAM 因子圖為核心的通用狀態估測框架：IMU 為骨幹，外部模組提供的位姿、位置、速度與地標量測都可作為因子接入；各個參考座標系（例如會漂移的 LiDAR 地圖座標系、里程計座標系與 GNSS 世界座標系）之間的對齊關係被當成隨機漫步的狀態一起最佳化，並沿路徑以局部關鍵影格對齊。線上以固定延遲平滑器提供 IMU 頻率的輸出，並另外提供平滑、不跳動的里程計座標系；離線則做整段批次最佳化，可作為事後處理的參考軌跡。","A task- and setup-agnostic GTSAM factor-graph framework that treats reference-frame alignments (drifting map, odometry and GNSS frames) as random-walk states, fuses heterogeneous absolute, relative, velocity and landmark measurements around IMU pre-integration, and provides online fixed-lag, smooth odometry and offline batch estimates.","full_text_reviewed","peer_reviewed_published","main_body","本文直接在施工機具上驗證：HEAP 液壓步行挖掘機於瑞士 Oberglatt 工業施工現場作業，建物之間的走廊同時失去 GNSS 且 LiDAR 幾何退化，框架透過同時融合 CompSLAM 掃描對地圖與 Coin-LIO 並估計各地圖座標系的漂移，仍完成任務並建立地圖。但施工現場部分只有定性結果（地圖與漂移曲線），定量評估來自四足機器人資料；其參考座標系對齊思路對長時間、跨區段的工地點雲整合具參考價值（推論）。",[20,21,22,23],"real_construction_site","independent_reference","cross_site","controlled_experiment",[25,26,27,28,29],"On the two ANYmal hikes, HF World with GNSS filtering reaches ATE 0.49 and 0.12 m against post-processed ground truth, while TSIF odometry drifts to 33.38 and 18.22 m (Table V)","HF Odom gives zero jumps (NOJ 0) and much lower jitter than world-frame estimates (Table VI)","On five indoor mocap sequences, HF World with LiDAR registration and tight leg kinematics gives ATE 2.8 cm, comparable to Open3D-SLAM (3.0 cm) but at high rate (Table IX)","Online latency in the lower microsecond range and asynchronous optimization of 1.29 to 8.96 ms for 10 to 100 Hz state creation (Table VII)","On a walking excavator at an industrial construction site in Oberglatt, the mission could be completed although the corridor between two buildings was both GNSS-denied and geometrically degenerate for CompSLAM scan-to-map registration (Sec. VI-E1; Figs. 23-24)",[31,32,33,34],"Initialization must be handled carefully (risk of ill-posed problems or wrong local minima); with a single GNSS antenna yaw is initially unobservable; many tuning parameters for new users (Sec. VII-B)","Hike, RACER and HEAP references are post-processed trajectories produced by offline batch optimization with GNSS, i.e., by the framework itself, not an independent system (Sec. VI-B)","HEAP construction-site results are qualitative (Figs. 23-25)","Leg-only HF estimates are less accurate than the engineered TSIF estimator (Tables VIII-IX)",[36,37,38,39,40,41],"IMU (core)","LiDAR registration poses (e.g., Open3D SLAM, CompSLAM, Coin-LIO outputs)","GNSS (single or dual antenna)","leg kinematics or wheel encoder","mm-wave radar velocity","cabin rotary encoder (HEAP)",[43,44,45],"ANYmal quadruped (hikes, parkour, indoor)","RACER Polaris RZR S4 1000 Turbo off-road vehicle","HEAP hydraulic walking excavator","GTSAM factor graph fusing IMU pre-integration with absolute pose, absolute position (GNSS), 3D landmark, local velocity and relative measurements; dynamic reference-frame alignment states modelled as an SE(3) random walk, local keyframe alignment along the path, landmark and global calibration states; online iSAM2 fixed-lag smoother with IMU-rate prediction and asynchronous updates, and offline batch optimization for post-processed ground truth (Sec. IV)","front-end agnostic: consumes poses, positions, velocities and landmarks from external modules (e.g., LiDAR scan-to-map registration, leg odometry, GNSS); outlier handling by robust norms (Sec. IV)","states created at a configurable rate (10 to 100 Hz evaluated) with IMU-rate propagation; delayed and out-of-order measurements attached to the nearest states (Secs. IV-V; Table VII)","not applicable (no raw point clouds are processed by the framework)","not part of the framework; handled by upstream modules","offline batch optimization over the full mission; online fixed-lag smoothing (Sec. IV)","none internally; aligns external map frames (e.g., LiDAR map, odometry frame) to the world frame","sensor extrinsics (optionally estimated as global states)","robot state in world, map and smooth odometry frames at IMU rate; offline trajectory used as post-processed ground truth","evaluation PC with Intel i9 13900K; online latency about 22 to 30 us and asynchronous optimization 1.29 to 8.96 ms depending on state-creation rate (Table VII); offline optimization 0.66 s to 64.1 s per mission (Table IV)","https:\u002F\u002Fgithub.com\u002Fleggedrobotics\u002Fholistic_fusion","BSD-3-Clause (GitHub license metadata)",[59,63,66],{"relation":60,"title":61,"doi_or_url":62},"preprint","Holistic Fusion arXiv v2 (accepted version)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.06479v2",{"relation":64,"title":65,"doi_or_url":56},"code_release","leggedrobotics\u002Fholistic_fusion",{"relation":67,"title":68,"doi_or_url":69},"successor","Generalizes the dual-graph estimator Graph MSF built for construction robots (see nubert2022constructionfusion)","not_applicable",{"id":5,"kind":71,"shortName":7,"title":8,"authors":72,"year":9,"venue":80,"venueType":81,"publisher":82,"volumeIssuePages":83,"doi":84,"arxivId":85,"url":86,"firstPublicDate":87,"publicationStatus":16,"metadataStatus":88,"fulltextStatus":15,"era":10,"classicReason":69,"codeUrl":56,"cluster":11,"topics":89,"mdpi":90,"verification":91,"label":6,"fulltextRoute":92,"versionRead":93,"addedByCensus":94},"method",[73,74,75,76,77,78,79],"Julian Nubert","Turcan Tuna","Jonas Frey","Cesar Cadena","Katherine J. Kuchenbecker","Shehryar Khattak","Marco Hutter","IEEE Transactions on Robotics","journal","IEEE","42, pp. 3366-3387","10.1109\u002Ftro.2026.3714645","2504.06479","https:\u002F\u002Fdoi.org\u002F10.1109\u002FTRO.2026.3714645","2025-04-08","metadata_verified",[11],false,"corrected","arXiv","arXiv v2 (2026-07-16), accepted T-RO version; IEEE version of record not opened",true,[96,103,110,117,122,126,130],{"category":97,"model":98,"canonical":98,"role":99,"dataset":100,"specs":101,"locator":102},"platform","ANYmal","method input","ANYmal hike, parkour and indoor missions (authors)","quadruped with IMU, LiDAR, leg kinematics and a single GNSS antenna","Table III; Sec. VI-A",{"category":104,"model":105,"canonical":106,"role":99,"dataset":107,"specs":108,"locator":109},"lidar","VLP-16","Velodyne VLP-16","ANYmal parkour (authors)","LiDAR on ANYmal in the parkour experiment; its accumulated cloud meshed with HF offline poses","Sec. VI-C2; Fig. 18",{"category":111,"model":112,"canonical":112,"role":113,"dataset":114,"specs":115,"locator":116},"other","Qualisys mocap system","reference or ground truth","ANYmal parkour and indoor (authors)","ground truth for parkour and indoor experiments","Sec. VI-B",{"category":97,"model":118,"canonical":118,"role":99,"dataset":119,"specs":120,"locator":121},"Polaris RZR S4 1000 Turbo (customized, RACER)","RACER off-road dataset","three LiDARs, mm-wave radar, single GNSS antenna and wheel encoder; 4.1 km at up to 9.66 m\u002Fs","Sec. VI-D",{"category":97,"model":45,"canonical":45,"role":99,"dataset":123,"specs":124,"locator":125},"HEAP construction missions (authors)","IMU, two GNSS antennas, cabin-to-chassis rotary encoder","Sec. VI-E",{"category":104,"model":127,"canonical":128,"role":99,"dataset":123,"specs":129,"locator":125},"OS0-128","Ouster OS0-128","LiDAR on HEAP; feeds CompSLAM scan-to-map registration and Coin-LIO",{"category":131,"model":132,"canonical":132,"role":133,"dataset":134,"specs":135,"locator":116},"compute","PC with Intel i9 13900K","compute for runtime",null,"all evaluations",[],{"totalRows":138,"groupCount":139,"groups":140,"others":611},99,5,[141,302,414,545],{"slug":142,"group":143,"sourceId":5,"sourceLabel":6,"table":144,"selfRows":145,"metrics":146,"seqs":154,"entrants":162,"cells":188,"outcomes":295,"locators":296,"hardware":298,"wordings":299,"notes":300},"holisticfusion2026-table-v","holisticfusion2026:Table V","Table V",32,[147,151],{"label":148,"unit":149,"statistic":150,"alignment":150},"ATE [m]","m","not_reported",{"label":152,"unit":153,"statistic":150,"alignment":150},"ARE [deg]","deg",[155,159],{"dataset":156,"sequence":157,"environment":158},"ANYmal hike missions (authors)","Forest","forest hike, degraded GNSS under vegetation",{"dataset":156,"sequence":160,"environment":161},"Mountain (Seealpsee)","alpine hike, unstable GNSS due to vegetation and elevation",[163,165,167,170,172,174,176,178,180,182,184,186],{"name":164,"methodId":134,"linkable":90,"proposed":90,"self":90},"TSIF - Odom",{"name":166,"methodId":134,"linkable":90,"proposed":90,"self":90},"Open3D SLAM - LR",{"name":168,"methodId":169,"linkable":94,"proposed":90,"self":90},"MINS - World (div.)","mins2025",{"name":171,"methodId":169,"linkable":94,"proposed":90,"self":90},"MINS - World (split)",{"name":173,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF GNSS+IMU - World",{"name":175,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF GNSS+IMU - Odom",{"name":177,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF (LR-between) - World",{"name":179,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF (LR-between) - Odom",{"name":181,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF - World",{"name":183,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF - Odom",{"name":185,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF (GNSS filtered) - World",{"name":187,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF (GNSS filtered) - Odom",[189,193,196,198,200,202,204,206,208,211,213,215,217,219,220,222,224,227,229,231,233,235,237,239,241,244,246,248,250,253,255,257,259,262,264,266,268,271,273,275,277,280,282,284,286,289,291,293],[190,190,190,191,192,190,192,192,190],0,33.38,-1,[190,194,190,195,192,190,192,192,190],1,15.42,[190,190,194,197,192,190,192,192,190],18.22,[190,194,194,199,192,190,192,192,190],9.09,[194,190,190,201,192,190,192,192,190],1.46,[194,194,190,203,192,190,192,192,190],2.53,[194,190,194,205,192,190,192,192,190],1.25,[194,194,194,207,192,190,192,192,190],4.47,[209,190,190,210,192,190,192,192,190],2,0.44,[209,194,190,212,192,190,192,192,190],2.14,[209,190,194,214,192,190,192,192,190],4.53,[209,194,194,216,192,190,192,192,190],29.86,[218,190,190,210,192,190,192,192,190],3,[218,194,190,212,192,190,192,192,190],[218,190,194,221,192,190,192,192,190],0.23,[218,194,194,223,192,190,192,192,190],4.1,[225,190,190,226,192,190,192,192,190],4,0.53,[225,194,190,228,192,190,192,192,190],6.47,[225,190,194,230,192,190,192,192,190],1.03,[225,194,194,232,192,190,192,192,190],11.25,[139,190,190,234,192,190,192,192,190],101.55,[139,194,190,236,192,190,192,192,190],104.17,[139,190,194,238,192,190,192,192,190],37.84,[139,194,194,240,192,190,192,192,190],21.61,[242,190,190,243,192,190,192,192,190],6,0.52,[242,194,190,245,192,190,192,192,190],5.56,[242,190,194,247,192,190,192,192,190],1.12,[242,194,194,249,192,190,192,192,190],13,[251,190,190,252,192,190,192,192,190],7,66,[251,194,190,254,192,190,192,192,190],29.17,[251,190,194,256,192,190,192,192,190],109.95,[251,194,194,258,192,190,192,192,190],41.79,[260,190,190,261,192,190,192,192,190],8,0.42,[260,194,190,263,192,190,192,192,190],1.42,[260,190,194,265,192,190,192,192,190],0.38,[260,194,194,267,192,190,192,192,190],2.63,[269,190,190,270,192,190,192,192,190],9,22.91,[269,194,190,272,192,190,192,192,190],11.55,[269,190,194,274,192,190,192,192,190],24.97,[269,194,194,276,192,190,192,192,190],13.62,[278,190,190,279,192,190,192,192,190],10,0.49,[278,194,190,281,192,190,192,192,190],1.23,[278,190,194,283,192,190,192,192,190],0.12,[278,194,194,285,192,190,192,192,190],1.22,[287,190,190,288,192,190,192,192,190],11,15.94,[287,194,190,290,192,190,192,192,190],8.87,[287,190,194,292,192,190,192,192,190],10.69,[287,194,194,294,192,190,192,192,190],6.11,[],[297],"Table V; Sec. VI-C1",[],[],[301],"ANYmal autonomous hikes (Forest and Mountain\u002FSeealpsee); global ATE [m] and ARE [deg] against post-processed ground truth (offline HF batch optimization with GNSS); HF World = world frame, HF Odom = smooth odometry frame (Sec. IV-D3); LR = LiDAR registration; MINS diverged mid-way on Mountain ('div.'), 'split' excludes the divergence",{"slug":303,"group":304,"sourceId":5,"sourceLabel":6,"table":305,"selfRows":306,"metrics":307,"seqs":321,"entrants":325,"cells":337,"outcomes":406,"locators":408,"hardware":410,"wordings":411,"notes":412},"holisticfusion2026-table-vi","holisticfusion2026:Table VI","Table VI",24,[308,312,315,318],{"label":309,"unit":310,"statistic":311,"alignment":69},"RTE [%]","%","mean",{"label":313,"unit":314,"statistic":311,"alignment":69},"RRE [deg\u002Fm]","deg\u002Fm",{"label":316,"unit":317,"statistic":69,"alignment":69},"NOJ [#] (number of jumps)","count",{"label":319,"unit":320,"statistic":150,"alignment":69},"Jitter [m\u002Fs^3]","m\u002Fs^3",[322],{"dataset":156,"sequence":323,"environment":324},"Forest and Mountain (average)","forest and alpine hikes",[326,327,329,331,332,333,334,335,336],{"name":164,"methodId":134,"linkable":90,"proposed":90,"self":90},{"name":328,"methodId":134,"linkable":90,"proposed":90,"self":90},"Open3D-SLAM - LR (\u003C=10 Hz)",{"name":330,"methodId":169,"linkable":94,"proposed":90,"self":90},"MINS - World",{"name":177,"methodId":5,"linkable":94,"proposed":94,"self":94},{"name":179,"methodId":5,"linkable":94,"proposed":94,"self":94},{"name":181,"methodId":5,"linkable":94,"proposed":94,"self":94},{"name":183,"methodId":5,"linkable":94,"proposed":94,"self":94},{"name":185,"methodId":5,"linkable":94,"proposed":94,"self":94},{"name":187,"methodId":5,"linkable":94,"proposed":94,"self":94},[338,340,342,344,346,348,350,351,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,389,391,393,395,397,399,401,403,404],[190,190,190,339,192,190,192,192,190],5.01,[190,194,190,341,192,190,192,192,190],0.57,[190,209,190,343,192,190,192,192,190],45,[190,218,190,345,192,190,192,192,190],48400,[194,190,190,347,192,190,192,192,190],6.28,[194,194,190,349,192,190,192,192,190],3.66,[194,209,190,194,192,190,192,192,190],[194,218,190,134,190,190,192,192,190],[209,190,190,353,192,190,192,192,190],4.52,[209,194,190,355,192,190,192,192,190],0.62,[209,209,190,357,192,190,192,192,190],1364,[209,218,190,359,192,190,192,192,190],70619,[218,190,190,361,192,190,192,192,190],4.84,[218,194,190,363,192,190,192,192,190],4.22,[218,209,190,365,192,190,192,192,190],5727,[218,218,190,367,192,190,192,192,190],245777,[225,190,190,369,192,190,192,192,190],4.09,[225,194,190,371,192,190,192,192,190],2.73,[225,209,190,373,192,190,192,192,190],1898,[225,218,190,375,192,190,192,192,190],5094,[139,190,190,377,192,190,192,192,190],4.07,[139,194,190,379,192,190,192,192,190],0.97,[139,209,190,381,192,190,192,192,190],2482,[139,218,190,383,192,190,192,192,190],59382,[242,190,190,385,192,190,192,192,190],3.13,[242,194,190,387,192,190,192,192,190],0.71,[242,209,190,190,192,190,192,192,190],[242,218,190,390,192,190,192,192,190],3470,[251,190,190,392,192,190,192,192,190],2.62,[251,194,190,394,192,190,192,192,190],0.81,[251,209,190,396,192,190,192,192,190],631,[251,218,190,398,192,190,192,192,190],50053,[260,190,190,400,192,190,192,192,190],3.1,[260,194,190,402,192,190,192,192,190],0.65,[260,209,190,190,192,190,192,192,190],[260,218,190,405,192,190,192,192,190],3890,[407],"not reported ('-')",[409],"Table VI; Sec. VI-C1",[],[],[413],"Average local estimation quality and smoothness over the two hikes; RTE and RRE averaged over all 1 m pairs; NOJ = jumps above 10 cm between consecutive 400 Hz estimates; jitter = jerk (third derivative of position); MINS full Forest plus split Mountain",{"slug":415,"group":416,"sourceId":5,"sourceLabel":6,"table":417,"selfRows":418,"metrics":419,"seqs":426,"entrants":431,"cells":446,"outcomes":511,"locators":539,"hardware":541,"wordings":542,"notes":543},"holisticfusion2026-table-ix","holisticfusion2026:Table IX","Table IX",20,[420,423,424,425],{"label":421,"unit":422,"statistic":311,"alignment":150},"ATE [cm]","cm",{"label":152,"unit":153,"statistic":311,"alignment":150},{"label":309,"unit":310,"statistic":311,"alignment":150},{"label":313,"unit":314,"statistic":311,"alignment":150},[427],{"dataset":428,"sequence":429,"environment":430},"ANYmal indoor locomotion dataset (authors)","five sequences (average)","indoor mocap arena",[432,434,436,438,440,442,444],{"name":433,"methodId":134,"linkable":90,"proposed":90,"self":90},"Open3D-SLAM (LR only, low rate)",{"name":435,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF World (IMU + leg odometry velocity, loosely fused)",{"name":437,"methodId":134,"linkable":90,"proposed":90,"self":90},"TSIF (IMU + leg kinematics, tightly fused)",{"name":439,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF World (IMU + leg kinematics, tightly fused)",{"name":441,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF World (IMU + LR + leg odometry velocity, loosely fused)",{"name":443,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF World (IMU + LR + leg kinematics, tightly fused)",{"name":445,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF Offline (IMU + LR + leg kinematics, tightly fused)",[447,448,450,452,454,456,458,460,462,464,466,468,470,473,475,478,481,483,486,489,492,494,497,500,503,504,506,508],[190,190,190,218,190,190,192,192,190],[190,194,190,449,194,190,192,192,190],2.7,[190,209,190,451,209,190,192,192,190],2.59,[190,218,190,453,218,190,192,192,190],1.4,[194,190,190,455,225,190,192,192,190],13.7,[194,194,190,457,139,190,192,192,190],5.69,[194,209,190,459,242,190,192,192,190],2.72,[194,218,190,461,251,190,192,192,190],1.39,[209,190,190,463,260,190,192,192,190],7.9,[209,194,190,465,269,190,192,192,190],3.73,[209,209,190,467,278,190,192,192,190],2.17,[209,218,190,469,287,190,192,192,190],1.24,[218,190,190,471,472,190,192,192,190],10.1,12,[218,194,190,474,249,190,192,192,190],4.97,[218,209,190,476,477,190,192,192,190],2.6,14,[218,218,190,479,480,190,192,192,190],1.52,15,[225,190,190,225,482,190,192,192,190],16,[225,194,190,484,485,190,192,192,190],2.68,17,[225,209,190,487,488,190,192,192,190],2.61,18,[225,218,190,490,491,190,192,192,190],1.36,19,[139,190,190,493,418,190,192,192,190],2.8,[139,194,190,495,496,190,192,192,190],2.75,21,[139,209,190,498,499,190,192,192,190],2.04,22,[139,218,190,501,502,190,192,192,190],1.32,23,[242,190,190,493,418,190,192,192,190],[242,194,190,505,306,190,192,192,190],2.58,[242,209,190,212,507,190,192,192,190],25,[242,218,190,509,510,190,192,192,190],1.29,26,[512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538],"std as printed: 1.5","std as printed: 0.64","std as printed: 1.92","std as printed: 1.12","std as printed: 6.3","std as printed: 2.73","std as printed: 2.49","std as printed: 1.02","std as printed: 2.9","std as printed: 0.75","std as printed: 2.26","std as printed: 0.9","std as printed: 4.0","std as printed: 1.36","std as printed: 2.58","std as printed: 1.11","std as printed: 2.4","std as printed: 0.59","std as printed: 2.41","std as printed: 1.01","std as printed: 1.4","std as printed: 0.54","std as printed: 1.67","std as printed: 0.96","std as printed: 0.52","std as printed: 1.63","std as printed: 0.98",[540],"Table IX; Sec. VI-C3",[],[],[544],"ANYmal indoor locomotion dataset, five sequences, averaged; Qualisys mocap ground truth; mean values stored, standard deviation in the outcome field; LR = LiDAR registration (Open3D-SLAM)",{"slug":546,"group":547,"sourceId":5,"sourceLabel":6,"table":548,"selfRows":472,"metrics":549,"seqs":558,"entrants":561,"cells":568,"outcomes":592,"locators":605,"hardware":607,"wordings":608,"notes":609},"holisticfusion2026-table-vii","holisticfusion2026:Table VII","Table VII",[550,553,556,557],{"label":551,"unit":552,"statistic":311,"alignment":150},"Latency [us]","us",{"label":554,"unit":555,"statistic":311,"alignment":150},"Async optimization time [ms]","ms",{"label":148,"unit":149,"statistic":311,"alignment":150},{"label":152,"unit":153,"statistic":311,"alignment":150},[559],{"dataset":156,"sequence":560,"environment":324},"hike",[562,564,566],{"name":563,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF at 10 Hz state creation",{"name":565,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF at 40 Hz state creation",{"name":567,"methodId":5,"linkable":94,"proposed":94,"self":94},"HF at 100 Hz state creation",[569,571,572,574,576,578,580,582,584,586,588,590],[190,190,190,570,190,190,190,192,190],22.41,[190,194,190,509,194,190,190,192,190],[190,209,190,573,209,190,190,192,190],0.384,[190,218,190,575,218,190,190,192,190],1.715,[194,190,190,577,225,190,190,192,190],25.31,[194,194,190,579,139,190,190,192,190],3.86,[194,209,190,581,242,190,190,192,190],0.373,[194,218,190,583,251,190,190,192,190],1.562,[209,190,190,585,260,190,190,192,190],29.63,[209,194,190,587,269,190,190,192,190],8.96,[209,209,190,589,278,190,190,192,190],0.374,[209,218,190,591,287,190,190,192,190],1.557,[593,594,595,596,597,598,599,600,601,602,603,604],"std as printed: 13.62","std as printed: 0.25","std as printed: 0.258","std as printed: 1.511","std as printed: 17.07","std as printed: 0.77","std as printed: 0.259","std as printed: 1.429","std as printed: 19.99","std as printed: 1.35","std as printed: 0.26","std as printed: 1.424",[606],"Table VII; Sec. VI-C1",[132],[],[610],"ANYmal hike: computational complexity and accuracy versus state-creation rate; mean values stored, standard deviation in the outcome field; evaluation PC Intel i9 13900K",[612],{"group":613,"slug":614,"sourceLabel":6,"table":615,"selfRows":287,"datasets":616},"holisticfusion2026:Table IV","holisticfusion2026-table-iv","Table IV",[617],"Holistic Fusion missions (authors)",1790510657224]