[{"data":1,"prerenderedAt":709},["ShallowReactive",2],{"method-mins2025":3},{"method":4,"reference":63,"equipment":86,"figures":140,"results":141},{"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":34,"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},"mins2025","Lee et al., 2025b","MINS","MINS: Efficient and Robust Multisensor-Aided Inertial Navigation System",2025,"recent","C07","odometry_with_local_mapping","MINS 以 IMU 為核心，在一個 MSCKF 形式的擴展卡爾曼濾波器中緊耦合相機、輪速計、LiDAR 與 GNSS：每種感測器都有專屬的量測更新，並能線上校正所有感測器的外參、時間偏移與內參。面對非同步量測，系統以高階流形上多項式內插取得任一時刻的位姿並建立內插誤差模型，且依運動動態調整複製狀態的頻率，以兼顧精度與計算量。LiDAR 採用直接點到平面更新並維護 ikd 樹局部地圖。","An IMU-centric MSCKF-style filter that tightly fuses cameras, wheel odometry, LiDAR and GNSS with online spatiotemporal and intrinsic calibration, using high-order on-manifold interpolation with an error model and dynamic cloning to handle asynchronous sensors efficiently.","full_text_reviewed","peer_reviewed_published","supplementary","MINS 可同時融合輪速計、LiDAR、相機與 GNSS，並線上校正時間與外參，這與施工現場巡檢載具常見的多感測器配置相符（推論）。UD Husky 資料包含室內結構化與室外非結構化環境，並以動作捕捉與 RTK 為真值；不過論文只評估軌跡，沒有點雲地圖精度。",[20,21,22,23],"public_benchmark","simulation","independent_reference","cross_site",[25,26,27,28,29],"On UD Husky indoor I1 and I2, MINS(I,L) ATE 0.07 and 0.11 m, lower than FAST-LIO2 (0.18, 0.16) and LIW-OAM (0.12, 0.16) (Table 7)","With all sensors, MINS(I,C,L,W,G) ATE 0.96 to 3.03 m on outdoor and unstructured sequences where GNSS alone gives 2.17 to 15.71 m and several baselines exceed 5 m (Table 7)","Total time per update 47 ms with all sensors versus 146 ms for Lvio-Fusion and 484 ms for VINS-Fusion with GNSS on KAIST Urban 38 (Table 8)","In simulation the full combination gives the lowest RMSE (0.261 deg \u002F 0.050 m) with NEES under 4 for all combinations (Table 5)","Dynamic cloning with coefficient 1 cuts run time to 61.9 % of fixed 30 Hz cloning with a small accuracy loss (Table 4)",[31,32,33],"Fusing all sensors is not always the most accurate choice; optimal sensor weighting is future work (Sec. 8)","No loop closure or global map","KAIST single-LiDAR baselines were fed a synthetic 20 Hz LiDAR merged from two 16-channel units (Sec. 7)",[35,36,37,38,39],"IMU","cameras (one or more)","wheel encoders","LiDAR (one or more)","GNSS",[41,42,21],"car (KAIST Urban dataset)","Clearpath Husky UGV (UD Husky dataset)","MSCKF-style EKF with the IMU as the backbone; camera updates with nullspace projection and measurement compression; integrated 2D wheel odometry updates with wheel intrinsics; direct LiDAR point-on-plane updates against an ikd-tree local map anchored to a cloned pose; GNSS position updates after 4-DoF alignment, then estimation in the global frame; online spatiotemporal and intrinsic calibration of all sensors (Sec. 3)","visual feature tracks for MSCKF updates; LiDAR points matched to planes in a dense local map (FAST-LIO2-style point-on-plane); wheel and GNSS measurements used directly (Sec. 3)","asynchronous measurements handled by high-order on-manifold polynomial interpolation between stochastic clones (default third order, 20 Hz cloning) with an interpolation error model; dynamic cloning adapts the clone rate to motion (Sec. 4)","not described; each point cloud is treated as a measurement at a single time t_k whose pose is obtained through the on-manifold interpolation (Secs. 3.4, 4)","no","none; GNSS provides global correction when available","dense LiDAR local map (ikd-tree) anchored to a clone; no global map","initial calibration guesses; IMU-wheel dynamic initialization (Sec. 5)","IMU pose, velocity and calibration states at the IMU rate","timing on a ThinkPad P17 laptop with an Intel i7 and 32 GB RAM (Table 8); simulation run times single-threaded on an Intel i7 (Table 5)","https:\u002F\u002Fgithub.com\u002Frpng\u002FMINS","GPL-3.0 (GitHub license metadata)",[56,60],{"relation":57,"title":58,"doi_or_url":59},"preprint","MINS arXiv v1 (2023)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.15390",{"relation":61,"title":62,"doi_or_url":53},"code_release","rpng\u002FMINS (GPL-3.0)",{"id":5,"kind":64,"shortName":7,"title":8,"authors":65,"year":9,"venue":70,"venueType":71,"publisher":72,"volumeIssuePages":73,"doi":74,"arxivId":75,"url":76,"firstPublicDate":77,"publicationStatus":16,"metadataStatus":78,"fulltextStatus":15,"era":10,"classicReason":79,"codeUrl":53,"cluster":11,"topics":80,"mdpi":81,"verification":82,"label":6,"fulltextRoute":83,"versionRead":84,"addedByCensus":85},"method",[66,67,68,69],"Woosik Lee","Patrick Geneva","Chuchu Chen","Guoquan Huang","Journal of Field Robotics","journal","Wiley","42(7), pp. 3252-3284","10.1002\u002Frob.22546","2309.15390","https:\u002F\u002Fdoi.org\u002F10.1002\u002Frob.22546","2023-09-27","metadata_verified","not_applicable",[11],false,"corrected","NTU institutional (Chrome) and arXiv","Wiley version of record (Sections 6-8, Tables 4-8, via NTU access) together with arXiv v1 (Sections 1-5; same section structure); appendices not read",true,[87,95,99,103,106,109,115,119,122,124,126,130,134],{"category":88,"model":89,"canonical":90,"role":91,"dataset":92,"specs":93,"locator":94},"imu","MTi-300","Xsens MTi-300","method input","KAIST Urban","100 Hz","Sec. 7",{"category":96,"model":97,"canonical":97,"role":91,"dataset":92,"specs":98,"locator":94},"stereo_camera","Flea3 stereo","10 Hz",{"category":100,"model":101,"canonical":101,"role":91,"dataset":92,"specs":102,"locator":94},"lidar","16-channel Velodyne (two units)","10 Hz each; merged into a synthetic 20 Hz LiDAR for single-LiDAR baselines",{"category":104,"model":105,"canonical":105,"role":91,"dataset":92,"specs":98,"locator":94},"gnss","EVK-7P",{"category":107,"model":108,"canonical":108,"role":91,"dataset":92,"specs":93,"locator":94},"wheel_or_leg_odometry","LM13",{"category":110,"model":111,"canonical":111,"role":91,"dataset":112,"specs":113,"locator":114},"platform","Husky","UD Husky dataset","UGV for the UD Husky dataset","Sec. 7; Figure 15",{"category":96,"model":116,"canonical":117,"role":91,"dataset":112,"specs":118,"locator":94},"RealSense T265","Intel RealSense T265","two 30 Hz cameras and a 200 Hz IMU",{"category":104,"model":120,"canonical":120,"role":91,"dataset":112,"specs":121,"locator":94},"GPS 18x","1 Hz",{"category":100,"model":123,"canonical":123,"role":91,"dataset":112,"specs":98,"locator":94},"64-channel Ouster",{"category":107,"model":125,"canonical":125,"role":91,"dataset":112,"specs":98,"locator":94},"Husky wheel encoders",{"category":104,"model":127,"canonical":127,"role":128,"dataset":112,"specs":129,"locator":94},"Reach M+ (RTK)","reference or ground truth","5 Hz; outdoor ground truth",{"category":131,"model":132,"canonical":132,"role":128,"dataset":112,"specs":133,"locator":94},"other","OptiTrack","indoor ground truth",{"category":135,"model":136,"canonical":136,"role":137,"dataset":92,"specs":138,"locator":139},"compute","ThinkPad P17","compute for runtime","Intel i7, 32 GB RAM","Table 8",[],{"totalRows":142,"groupCount":143,"groups":144,"others":698},97,6,[145,402,488,557],{"slug":146,"group":147,"sourceId":5,"sourceLabel":6,"table":148,"selfRows":149,"metrics":150,"seqs":155,"entrants":175,"cells":205,"outcomes":394,"locators":396,"hardware":398,"wordings":399,"notes":400},"mins2025-table-7","mins2025:Table 7","Table 7",38,[151],{"label":152,"unit":153,"statistic":154,"alignment":154},"Average (5 runs) position ATE","m","not_reported",[156,159,161,164,166,169,171,173],{"dataset":112,"sequence":157,"environment":158},"I1","indoor structured",{"dataset":112,"sequence":160,"environment":158},"I2",{"dataset":112,"sequence":162,"environment":163},"O1","outdoor structured",{"dataset":112,"sequence":165,"environment":163},"O2",{"dataset":112,"sequence":167,"environment":168},"T1","outdoor unstructured",{"dataset":112,"sequence":170,"environment":168},"T2",{"dataset":112,"sequence":172,"environment":168},"T3",{"dataset":112,"sequence":174,"environment":168},"T4",[176,179,181,184,186,188,191,194,196,198,199,201,203],{"name":177,"methodId":178,"linkable":85,"proposed":81,"self":81},"VINS-Fusion(V)","vinsfusion2019",{"name":180,"methodId":178,"linkable":85,"proposed":81,"self":81},"VINS-Fusion(L)",{"name":182,"methodId":183,"linkable":85,"proposed":81,"self":81},"ORB-SLAM3","orbslam3_2021",{"name":185,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,C)",{"name":187,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,C,W)",{"name":189,"methodId":190,"linkable":85,"proposed":81,"self":81},"FAST-LIO2","fastlio2_2022",{"name":192,"methodId":193,"linkable":81,"proposed":81,"self":81},"LIW-OAM",null,{"name":195,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,L)",{"name":197,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,L,W)",{"name":39,"methodId":193,"linkable":81,"proposed":81,"self":81},{"name":200,"methodId":178,"linkable":85,"proposed":81,"self":81},"VINS-Fusion(G)",{"name":202,"methodId":193,"linkable":81,"proposed":81,"self":81},"Lvio-Fusion",{"name":204,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,C,L,W,G)",[206,210,213,215,218,221,224,226,228,230,232,233,235,237,239,241,243,245,247,249,250,252,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,296,298,300,302,304,306,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,338,339,341,343,344,346,348,350,353,355,357,359,361,363,365,367,368,369,370,371,374,376,377,378,379,381,384,386,388,390,392],[207,207,207,208,209,207,209,209,207],0,0.63,-1,[207,207,211,212,209,207,209,209,207],1,0.71,[207,207,214,193,207,207,209,209,207],2,[207,207,216,217,209,207,209,209,207],3,4.39,[207,207,219,220,209,207,209,209,207],4,4.82,[207,207,222,223,209,207,209,209,207],5,4.92,[207,207,143,225,209,207,209,209,207],1.6,[207,207,227,193,207,207,209,209,207],7,[211,207,207,229,209,207,209,209,207],0.55,[211,207,211,231,209,207,209,209,207],0.49,[211,207,214,193,207,207,209,209,207],[211,207,216,234,209,207,209,209,207],4.07,[211,207,219,236,209,207,209,209,207],3.36,[211,207,222,238,209,207,209,209,207],4.31,[211,207,143,240,209,207,209,209,207],0.92,[211,207,227,242,209,207,209,209,207],3.11,[214,207,207,244,209,207,209,209,207],0.5,[214,207,211,246,209,207,209,209,207],0.48,[214,207,214,248,209,207,209,209,207],4.51,[214,207,216,193,207,207,209,209,207],[214,207,219,251,209,207,209,209,207],2.5,[214,207,222,193,207,207,209,209,207],[214,207,143,254,209,207,209,209,207],1.69,[214,207,227,256,209,207,209,209,207],2.74,[216,207,207,258,209,207,209,209,207],0.33,[216,207,211,260,209,207,209,209,207],0.73,[216,207,214,262,209,207,209,209,207],2.93,[216,207,216,264,209,207,209,209,207],3.92,[216,207,219,266,209,207,209,209,207],3.16,[216,207,222,268,209,207,209,209,207],3.19,[216,207,143,270,209,207,209,209,207],2.64,[216,207,227,272,209,207,209,209,207],4.66,[219,207,207,274,209,207,209,209,207],0.22,[219,207,211,276,209,207,209,209,207],0.67,[219,207,214,278,209,207,209,209,207],1.39,[219,207,216,280,209,207,209,209,207],2.57,[219,207,219,282,209,207,209,209,207],2.4,[219,207,222,284,209,207,209,209,207],1.95,[219,207,143,286,209,207,209,209,207],2.16,[219,207,227,288,209,207,209,209,207],3.83,[222,207,207,290,209,207,209,209,207],0.18,[222,207,211,292,209,207,209,209,207],0.16,[222,207,214,294,209,207,209,209,207],2.34,[222,207,216,256,209,207,209,209,207],[222,207,219,297,209,207,209,209,207],0.95,[222,207,222,299,209,207,209,209,207],2.82,[222,207,143,301,209,207,209,209,207],1.37,[222,207,227,303,209,207,209,209,207],2.02,[143,207,207,305,209,207,209,209,207],0.12,[143,207,211,292,209,207,209,209,207],[143,207,214,308,209,207,209,209,207],1.01,[143,207,216,310,209,207,209,209,207],2.92,[143,207,219,312,209,207,209,209,207],2.36,[143,207,222,314,209,207,209,209,207],3.61,[143,207,143,316,209,207,209,209,207],2.37,[143,207,227,318,209,207,209,209,207],3.04,[227,207,207,320,209,207,209,209,207],0.07,[227,207,211,322,209,207,209,209,207],0.11,[227,207,214,324,209,207,209,209,207],1.73,[227,207,216,326,209,207,209,209,207],2.05,[227,207,219,328,209,207,209,209,207],1.64,[227,207,222,330,209,207,209,209,207],1.28,[227,207,143,332,209,207,209,209,207],1.93,[227,207,227,334,209,207,209,209,207],2.13,[336,207,207,337,209,207,209,209,207],8,0.08,[336,207,211,292,209,207,209,209,207],[336,207,214,340,209,207,209,209,207],1.35,[336,207,216,342,209,207,209,209,207],1.99,[336,207,219,340,209,207,209,209,207],[336,207,222,345,209,207,209,209,207],1.05,[336,207,143,347,209,207,209,209,207],1.25,[336,207,227,349,209,207,209,209,207],1.62,[351,207,214,352,209,207,209,209,207],9,3.17,[351,207,216,354,209,207,209,209,207],2.17,[351,207,219,356,209,207,209,209,207],15.71,[351,207,222,358,209,207,209,209,207],10.34,[351,207,143,360,209,207,209,209,207],5.93,[351,207,227,362,209,207,209,209,207],7.02,[364,207,214,193,207,207,209,209,207],10,[364,207,216,366,209,207,209,209,207],3.25,[364,207,219,193,207,207,209,209,207],[364,207,222,193,207,207,209,209,207],[364,207,143,193,207,207,209,209,207],[364,207,227,193,207,207,209,209,207],[372,207,214,373,209,207,209,209,207],11,3.14,[372,207,216,375,209,207,209,209,207],2.63,[372,207,219,193,207,207,209,209,207],[372,207,222,193,207,207,209,209,207],[372,207,143,193,207,207,209,209,207],[372,207,227,380,209,207,209,209,207],4.02,[382,207,214,383,209,207,209,209,207],12,0.96,[382,207,216,385,209,207,209,209,207],1.07,[382,207,219,387,209,207,209,209,207],3.03,[382,207,222,389,209,207,209,209,207],2.89,[382,207,143,391,209,207,209,209,207],1.33,[382,207,227,393,209,207,209,209,207],1.3,[395],"error above 5 m, not reported ('-')",[397],"Table 7; Sec. 7",[],[],[401],"UD Husky dataset (Clearpath Husky; indoor structured I1-I2 with OptiTrack GT, outdoor O1-O2 and unstructured T1-T4 with Emlid RTK GT); average (5 runs) position ATE in metres; '-' = error above 5 m not reported",{"slug":403,"group":404,"sourceId":5,"sourceLabel":6,"table":405,"selfRows":406,"metrics":407,"seqs":418,"entrants":422,"cells":436,"outcomes":480,"locators":481,"hardware":483,"wordings":485,"notes":486},"mins2025-table-5","mins2025:Table 5","Table 5",24,[408,412,414],{"label":409,"unit":410,"statistic":411,"alignment":79},"Orientation RMSE","deg","RMSE",{"label":413,"unit":153,"statistic":411,"alignment":79},"Position RMSE",{"label":415,"unit":416,"statistic":417,"alignment":79},"Run time for the whole simulated run","s","mean",[419],{"dataset":420,"sequence":421,"environment":21},"MINS simulation","simulated trajectory",[423,424,426,428,429,431,432,434],{"name":185,"methodId":5,"linkable":85,"proposed":85,"self":85},{"name":425,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,G)",{"name":427,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,W)",{"name":195,"methodId":5,"linkable":85,"proposed":85,"self":85},{"name":430,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,C,G)",{"name":187,"methodId":5,"linkable":85,"proposed":85,"self":85},{"name":433,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,C,L)",{"name":435,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS(I,C,G,W,L)",[437,439,441,443,445,447,448,450,452,453,455,457,459,461,463,464,465,467,468,470,472,474,476,478],[207,207,207,438,209,207,209,209,207],0.505,[207,211,207,440,209,207,209,209,207],0.139,[207,214,207,442,209,207,207,209,207],23.6,[211,207,207,444,209,207,209,209,207],1.244,[211,211,207,446,209,207,209,209,207],0.191,[211,214,207,244,209,207,207,209,207],[214,207,207,449,209,207,209,209,207],3.053,[214,211,207,451,209,207,209,209,207],0.636,[214,214,207,211,209,207,207,209,207],[216,207,207,454,209,207,209,209,207],0.474,[216,211,207,456,209,207,209,209,207],0.098,[216,214,207,458,209,207,207,209,207],22.3,[219,207,207,460,209,207,209,209,207],0.318,[219,211,207,462,209,207,209,209,207],0.057,[219,214,207,442,209,207,207,209,207],[222,207,207,438,209,207,209,209,207],[222,211,207,466,209,207,209,209,207],0.102,[222,214,207,406,209,207,207,209,207],[143,207,207,469,209,207,209,209,207],0.414,[143,211,207,471,209,207,209,209,207],0.084,[143,214,207,473,209,207,207,209,207],49.6,[227,207,207,475,209,207,209,209,207],0.261,[227,211,207,477,209,207,209,209,207],0.05,[227,214,207,479,209,207,207,209,207],49.9,[],[482],"Table 5; Sec. 6",[484],"Intel i7, single-threaded",[],[487],"Simulation with different sensor combinations (I IMU, C camera, G GNSS, W wheel, L LiDAR); orientation and position RMSE (mean over 10 runs, +- std in the table) and run time; single-threaded Intel i7",{"slug":489,"group":490,"sourceId":5,"sourceLabel":6,"table":491,"selfRows":492,"metrics":493,"seqs":500,"entrants":502,"cells":515,"outcomes":550,"locators":551,"hardware":553,"wordings":554,"notes":555},"mins2025-table-4","mins2025:Table 4","Table 4",18,[494,496,498],{"label":495,"unit":410,"statistic":411,"alignment":79},"Ori. RMSE",{"label":497,"unit":153,"statistic":411,"alignment":79},"Pos. RMSE",{"label":499,"unit":416,"statistic":79,"alignment":79},"Total computation time",[501],{"dataset":420,"sequence":421,"environment":21},[503,505,507,509,511,513],{"name":504,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS (fixed 30 Hz cloning)",{"name":506,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS (dynamic, coefficient 0.01 cloning)",{"name":508,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS (dynamic, coefficient 0.1 cloning)",{"name":510,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS (dynamic, coefficient 1 cloning)",{"name":512,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS (dynamic, coefficient 10 cloning)",{"name":514,"methodId":5,"linkable":85,"proposed":85,"self":85},"MINS (dynamic, coefficient 100 cloning)",[516,518,520,522,523,525,527,529,531,533,535,537,539,541,543,545,546,548],[207,207,207,517,209,207,209,209,207],0.178,[207,211,207,519,209,207,209,209,207],0.019,[207,214,207,521,209,207,209,209,207],80.5,[211,207,207,517,209,207,209,209,207],[211,211,207,524,209,207,209,209,207],0.021,[211,214,207,526,209,207,209,209,207],80.4,[214,207,207,528,209,207,209,209,207],0.175,[214,211,207,530,209,207,209,209,207],0.023,[214,214,207,532,209,207,209,209,207],61.1,[216,207,207,534,209,207,209,209,207],0.218,[216,211,207,536,209,207,209,209,207],0.028,[216,214,207,538,209,207,209,209,207],49.8,[219,207,207,540,209,207,209,209,207],0.38,[219,211,207,542,209,207,209,209,207],0.048,[219,214,207,544,209,207,209,209,207],45.1,[222,207,207,229,209,207,209,209,207],[222,211,207,547,209,207,209,209,207],0.062,[222,214,207,549,209,207,209,209,207],44.9,[],[552],"Table 4; Sec. 6",[],[],[556],"Simulation: pose RMSE (deg \u002F m) and total computation time (s) of fixed-rate 30 Hz cloning versus dynamic cloning with threshold coefficients 0.01 to 100",{"slug":558,"group":559,"sourceId":560,"sourceLabel":561,"table":562,"selfRows":336,"metrics":563,"seqs":568,"entrants":576,"cells":601,"outcomes":691,"locators":692,"hardware":694,"wordings":695,"notes":696},"holisticfusion2026-table-v","holisticfusion2026:Table V","holisticfusion2026","Nubert et al., 2026","Table V",[564,566],{"label":565,"unit":153,"statistic":154,"alignment":154},"ATE [m]",{"label":567,"unit":410,"statistic":154,"alignment":154},"ARE [deg]",[569,573],{"dataset":570,"sequence":571,"environment":572},"ANYmal hike missions (authors)","Forest","forest hike, degraded GNSS under vegetation",{"dataset":570,"sequence":574,"environment":575},"Mountain (Seealpsee)","alpine hike, unstable GNSS due to vegetation and elevation",[577,579,581,583,585,587,589,591,593,595,597,599],{"name":578,"methodId":193,"linkable":81,"proposed":81,"self":81},"TSIF - Odom",{"name":580,"methodId":193,"linkable":81,"proposed":81,"self":81},"Open3D SLAM - LR",{"name":582,"methodId":5,"linkable":85,"proposed":81,"self":85},"MINS - World (div.)",{"name":584,"methodId":5,"linkable":85,"proposed":81,"self":85},"MINS - World (split)",{"name":586,"methodId":560,"linkable":85,"proposed":85,"self":81},"HF GNSS+IMU - World",{"name":588,"methodId":560,"linkable":85,"proposed":85,"self":81},"HF GNSS+IMU - Odom",{"name":590,"methodId":560,"linkable":85,"proposed":85,"self":81},"HF (LR-between) - World",{"name":592,"methodId":560,"linkable":85,"proposed":85,"self":81},"HF (LR-between) - Odom",{"name":594,"methodId":560,"linkable":85,"proposed":85,"self":81},"HF - World",{"name":596,"methodId":560,"linkable":85,"proposed":85,"self":81},"HF - Odom",{"name":598,"methodId":560,"linkable":85,"proposed":85,"self":81},"HF (GNSS filtered) - World",{"name":600,"methodId":560,"linkable":85,"proposed":85,"self":81},"HF (GNSS filtered) - Odom",[602,604,606,608,610,612,614,615,617,619,621,623,625,626,627,629,631,633,635,637,639,641,643,645,647,649,651,653,655,657,659,661,663,665,667,668,669,671,673,675,677,678,680,681,683,685,687,689],[207,207,207,603,209,207,209,209,207],33.38,[207,211,207,605,209,207,209,209,207],15.42,[207,207,211,607,209,207,209,209,207],18.22,[207,211,211,609,209,207,209,209,207],9.09,[211,207,207,611,209,207,209,209,207],1.46,[211,211,207,613,209,207,209,209,207],2.53,[211,207,211,347,209,207,209,209,207],[211,211,211,616,209,207,209,209,207],4.47,[214,207,207,618,209,207,209,209,207],0.44,[214,211,207,620,209,207,209,209,207],2.14,[214,207,211,622,209,207,209,209,207],4.53,[214,211,211,624,209,207,209,209,207],29.86,[216,207,207,618,209,207,209,209,207],[216,211,207,620,209,207,209,209,207],[216,207,211,628,209,207,209,209,207],0.23,[216,211,211,630,209,207,209,209,207],4.1,[219,207,207,632,209,207,209,209,207],0.53,[219,211,207,634,209,207,209,209,207],6.47,[219,207,211,636,209,207,209,209,207],1.03,[219,211,211,638,209,207,209,209,207],11.25,[222,207,207,640,209,207,209,209,207],101.55,[222,211,207,642,209,207,209,209,207],104.17,[222,207,211,644,209,207,209,209,207],37.84,[222,211,211,646,209,207,209,209,207],21.61,[143,207,207,648,209,207,209,209,207],0.52,[143,211,207,650,209,207,209,209,207],5.56,[143,207,211,652,209,207,209,209,207],1.12,[143,211,211,654,209,207,209,209,207],13,[227,207,207,656,209,207,209,209,207],66,[227,211,207,658,209,207,209,209,207],29.17,[227,207,211,660,209,207,209,209,207],109.95,[227,211,211,662,209,207,209,209,207],41.79,[336,207,207,664,209,207,209,209,207],0.42,[336,211,207,666,209,207,209,209,207],1.42,[336,207,211,540,209,207,209,209,207],[336,211,211,375,209,207,209,209,207],[351,207,207,670,209,207,209,209,207],22.91,[351,211,207,672,209,207,209,209,207],11.55,[351,207,211,674,209,207,209,209,207],24.97,[351,211,211,676,209,207,209,209,207],13.62,[364,207,207,231,209,207,209,209,207],[364,211,207,679,209,207,209,209,207],1.23,[364,207,211,305,209,207,209,209,207],[364,211,211,682,209,207,209,209,207],1.22,[372,207,207,684,209,207,209,209,207],15.94,[372,211,207,686,209,207,209,209,207],8.87,[372,207,211,688,209,207,209,209,207],10.69,[372,211,211,690,209,207,209,209,207],6.11,[],[693],"Table V; Sec. VI-C1",[],[],[697],"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",[699,704],{"group":700,"slug":701,"sourceLabel":6,"table":702,"selfRows":222,"datasets":703},"mins2025:Table 8 (Total column)","mins2025-table-8-total-column","Table 8 (Total column)",[92],{"group":705,"slug":706,"sourceLabel":561,"table":707,"selfRows":219,"datasets":708},"holisticfusion2026:Table VI","holisticfusion2026-table-vi","Table VI",[570],1790510657338]