[{"data":1,"prerenderedAt":627},["ShallowReactive",2],{"method-mourikis2007msckf":3},{"method":4,"reference":48,"equipment":67,"figures":87,"results":88},{"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":20,"limitations":26,"sensors":32,"platform":35,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":41,"mapRepresentation":42,"prior":43,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":40,"relatedVersions":47},"mourikis2007msckf","Mourikis & Roumeliotis, 2007","MSCKF","A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation",2007,"classic","C03","odometry","MSCKF 是以擴展卡爾曼濾波（EKF）為基礎的視覺輔助慣性導航演算法。其核心是推導一種量測模型：當靜態特徵被多個相機位姿觀測時，直接以這些位姿間的幾何約束更新濾波器，而不必把三維特徵座標放進狀態向量。因此計算量只與特徵數呈線性關係，狀態中只保留有限數量的過去相機位姿副本。作者以車載相機與 IMU 在都市住宅區 3.2 km 的行駛資料驗證。","An EKF that augments the state with past camera poses and uses multi-view constraints of static features without estimating feature positions, giving complexity linear in the number of features.","full_text_reviewed","peer_reviewed_published","background","未在營建場域驗證；本文唯一呈現的實驗為明尼亞波利斯住宅區街道上的車載相機與 IMU 資料（模擬結果因篇幅未收錄），沒有 GPS 真值，以地圖疊合與起終點停車位推估終點誤差（Sec. IV）。",[],[21,22,23,24,25],"Optimal up to linearization errors while avoiding 3D features in the state (abstract; Sec. V)","Complexity linear in the number of features (abstract; Sec. III-F)","Delayed linearization and inverse-depth triangulation improve robustness to linearization errors (Sec. III-F)","Processed the 3 Hz dataset at 14 Hz on one core with 142903 features used along 3.2 km (Sec. IV)","Outliers from moving objects (cars, pedestrians, trees) were discarded in the reported run (Sec. IV)",[27,28,29,30,31],"No ground-truth trajectory was available; accuracy judged from a map overlay and known parking spots (Sec. IV)","No loop closing (Sec. IV)","Update cost is at most cubic in the number of states in the window, so the number of cloned poses dominates the cost (Sec. III-F)","Simulation results are not included in the paper for space reasons (Sec. IV)","(inference) As an EKF, it does not relinearize past measurements, a limitation discussed for filters by strasdat2012whyfilter",[33,34],"monocular camera","IMU",[36],"vehicle","EKF whose state holds the evolving IMU state (quaternion, gyro and accelerometer biases, velocity, position; 15-dimensional error state) plus up to Nmax cloned camera poses; each feature's stacked residual is projected onto the left nullspace of its feature Jacobian with Givens rotations, residuals of all features are compressed by QR decomposition before the update; when Nmax is reached, Nmax\u002F3 evenly spaced poses starting from the second oldest are removed after processing their features, and the oldest pose is always kept; Nmax = 30 in the experiment","SIFT feature extraction and matching; each feature triangulated by Gauss-Newton with an inverse-depth parameterization while camera poses are treated as known; multi-view geometric constraints of static features; simple Mahalanobis distance test to discard features on moving objects","discrete poses at camera rate (3 Hz images); IMU propagation at IMU rate (100 Hz) with 5th-order Runge-Kutta integration in an Earth-centered, Earth-fixed frame including the planet's rotation","not_applicable","none","no persistent map (features not kept in state)","none (no nonholonomic constraints or street map used)","IMU pose and velocity trajectory with covariance","dataset processed at 14 Hz on a single core of an Intel T7200 2 GHz; processing done offline on recorded data (Sec. IV)",null,[],{"id":5,"kind":49,"shortName":7,"title":8,"authors":50,"year":9,"venue":53,"venueType":54,"publisher":55,"volumeIssuePages":56,"doi":57,"arxivId":46,"url":58,"firstPublicDate":59,"publicationStatus":16,"metadataStatus":60,"fulltextStatus":15,"era":10,"classicReason":61,"codeUrl":46,"cluster":11,"topics":62,"mdpi":63,"verification":64,"label":6,"fulltextRoute":65,"versionRead":66,"addedByCensus":63},"method",[51,52],"Anastasios I. Mourikis","Stergios I. Roumeliotis","Proceedings 2007 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 3565-3572","10.1109\u002Frobot.2007.364024","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FROBOT.2007.364024","2007-04","metadata_verified","principle reused: sliding-window EKF with stochastic cloning of past poses and landmark-free multi-view constraints, a basis of later filter-based visual-inertial estimators.",[11],false,"corrected","author copy","Authors' copy hosted at the University of Minnesota (ICRA07-MSCKF.pdf, 8 pages, same page count as ICRA 2007 pp. 3565-3572); the IEEE Xplore version of record was not opened",[68,74,78,82],{"category":69,"model":70,"canonical":70,"role":71,"dataset":46,"specs":72,"locator":73},"camera","Pointgrey FireFly","method input","640 x 480 pixels at 3 Hz","Sec. IV",{"category":75,"model":76,"canonical":76,"role":71,"dataset":46,"specs":77,"locator":73},"imu","Inertial Science ISIS IMU","100 Hz",{"category":79,"model":80,"canonical":80,"role":71,"dataset":46,"specs":81,"locator":73},"platform","car","camera\u002FIMU system placed on a car driving in a residential area of Minneapolis",{"category":83,"model":84,"canonical":84,"role":85,"dataset":46,"specs":86,"locator":73},"compute","Intel T7200","compute for runtime","single core, 2 GHz; processing done off-line on recorded data",[],{"totalRows":89,"groupCount":90,"groups":91,"others":565},40,14,[92,267,471,530],{"slug":93,"group":94,"sourceId":95,"sourceLabel":96,"table":97,"selfRows":98,"metrics":99,"seqs":105,"entrants":130,"cells":145,"outcomes":260,"locators":262,"hardware":263,"wordings":264,"notes":265},"kimera2020-table-ii","kimera2020:Table II","kimera2020","Rosinol et al., 2020","Table II",11,[100],{"label":101,"unit":102,"statistic":103,"alignment":104},"RMSE ATE [m]","m","RMSE","not_reported",[106,110,112,114,116,118,120,122,124,126,128],{"dataset":107,"sequence":108,"environment":109},"EuRoC MAV","MH_01","EuRoC MAV sequences (micro aerial vehicle dataset, ref. [19]); environments not described in this paper",{"dataset":107,"sequence":111,"environment":109},"MH_02",{"dataset":107,"sequence":113,"environment":109},"MH_03",{"dataset":107,"sequence":115,"environment":109},"MH_04",{"dataset":107,"sequence":117,"environment":109},"MH_05",{"dataset":107,"sequence":119,"environment":109},"V1_01",{"dataset":107,"sequence":121,"environment":109},"V1_02",{"dataset":107,"sequence":123,"environment":109},"V1_03",{"dataset":107,"sequence":125,"environment":109},"V2_01",{"dataset":107,"sequence":127,"environment":109},"V2_02",{"dataset":107,"sequence":129,"environment":109},"V2_03",[131,135,136,138,141,143],{"name":132,"methodId":133,"linkable":134,"proposed":63,"self":63},"OKVIS","okvis2015",true,{"name":7,"methodId":5,"linkable":134,"proposed":63,"self":134},{"name":137,"methodId":46,"linkable":63,"proposed":63,"self":63},"ROVIO",{"name":139,"methodId":140,"linkable":134,"proposed":63,"self":63},"VINS-Mono","vinsmono2018",{"name":142,"methodId":46,"linkable":63,"proposed":63,"self":63},"SVO-GTSAM 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closure)",[146,150,153,156,159,162,165,168,170,173,175,178,180,182,184,186,188,189,190,192,194,195,197,199,201,202,204,206,207,208,210,212,213,214,216,217,218,220,222,224,226,228,229,230,232,234,236,237,238,239,241,242,243,244,245,246,247,248,249,250,251,253,254,256,257,258],[147,147,147,148,149,147,149,149,147],0,0.16,-1,[147,147,151,152,149,147,149,149,147],1,0.22,[147,147,154,155,149,147,149,149,147],2,0.24,[147,147,157,158,149,147,149,149,147],3,0.34,[147,147,160,161,149,147,149,149,147],4,0.47,[147,147,163,164,149,147,149,149,147],5,0.09,[147,147,166,167,149,147,149,149,147],6,0.2,[147,147,169,155,149,147,149,149,147],7,[147,147,171,172,149,147,149,149,147],8,0.13,[147,147,174,148,149,147,149,149,147],9,[147,147,176,177,149,147,149,149,147],10,0.29,[151,147,147,179,149,147,149,149,147],0.42,[151,147,151,181,149,147,149,149,147],0.45,[151,147,154,183,149,147,149,149,147],0.23,[151,147,157,185,149,147,149,149,147],0.37,[151,147,160,187,149,147,149,149,147],0.48,[151,147,163,158,149,147,149,149,147],[151,147,166,167,149,147,149,149,147],[151,147,169,191,149,147,149,149,147],0.67,[151,147,171,193,149,147,149,149,147],0.1,[151,147,174,148,149,147,149,149,147],[151,147,176,196,149,147,149,149,147],1.13,[154,147,147,198,149,147,149,149,147],0.21,[154,147,151,200,149,147,149,149,147],0.25,[154,147,154,200,149,147,149,149,147],[154,147,157,203,149,147,149,149,147],0.49,[154,147,160,205,149,147,149,149,147],0.52,[154,147,163,193,149,147,149,149,147],[154,147,166,193,149,147,149,149,147],[154,147,169,209,149,147,149,149,147],0.14,[154,147,171,211,149,147,149,149,147],0.12,[154,147,174,209,149,147,149,149,147],[154,147,176,209,149,147,149,149,147],[157,147,147,215,149,147,149,149,147],0.15,[157,147,151,215,149,147,149,149,147],[157,147,154,152,149,147,149,149,147],[157,147,157,219,149,147,149,149,147],0.32,[157,147,160,221,149,147,149,149,147],0.3,[157,147,163,223,149,147,149,149,147],0.08,[157,147,166,225,149,147,149,149,147],0.11,[157,147,169,227,149,147,149,149,147],0.18,[157,147,171,223,149,147,149,149,147],[157,147,174,148,149,147,149,149,147],[157,147,176,231,149,147,149,149,147],0.27,[160,147,147,233,149,147,149,149,147],0.05,[160,147,151,235,149,147,149,149,147],0.03,[160,147,154,211,149,147,149,149,147],[160,147,157,172,149,147,149,149,147],[160,147,160,148,149,147,149,149,147],[160,147,163,240,149,147,149,149,147],0.07,[160,147,166,225,149,147,149,149,147],[160,147,169,46,147,147,149,149,147],[160,147,171,240,149,147,149,149,147],[160,147,174,46,147,147,149,149,147],[160,147,176,46,147,147,149,149,147],[163,147,147,211,149,147,149,149,147],[163,147,151,211,149,147,149,149,147],[163,147,154,172,149,147,149,149,147],[163,147,157,227,149,147,149,149,147],[163,147,160,198,149,147,149,149,147],[163,147,163,252,149,147,149,149,147],0.06,[163,147,166,223,149,147,149,149,147],[163,147,169,255,149,147,149,149,147],0.19,[163,147,171,223,149,147,149,149,147],[163,147,174,148,149,147,149,149,147],[163,147,176,259,149,147,149,149,147],1.39,[261],"failed (x in table)",[97],[],[],[266],"EuRoC ATE RMSE grouped as fixed-lag smoothing, full smoothing and PGO with loop closure; comparator values taken from Delmerico and Scaramuzza [77] (Sim(3) alignment per text) and VINS-Mono [24]; comparators use a monocular camera while Kimera uses stereo; Kimera aligned with SE(3); loop threshold alpha = 0.001",{"slug":268,"group":269,"sourceId":270,"sourceLabel":271,"table":272,"selfRows":166,"metrics":273,"seqs":276,"entrants":290,"cells":331,"outcomes":462,"locators":466,"hardware":467,"wordings":468,"notes":469},"d3vo2020-table-6","d3vo2020:Table 6","d3vo2020","Yang et al., 2020a","Table 6",[274],{"label":275,"unit":104,"statistic":103,"alignment":104},"ATE (RMS)",[277,280,282,284,286,288],{"dataset":107,"sequence":278,"environment":279},"MH_03_medium","indoor machine hall and Vicon room (MAV)",{"dataset":107,"sequence":281,"environment":279},"MH_05_difficult",{"dataset":107,"sequence":283,"environment":279},"V1_03_difficult",{"dataset":107,"sequence":285,"environment":279},"V2_02_medium",{"dataset":107,"sequence":287,"environment":279},"V2_03_difficult",{"dataset":107,"sequence":289,"environment":279},"mean of evaluated sequences",[291,294,297,300,302,304,306,309,311,313,315,317,319,321,323,325,327,329],{"name":292,"methodId":293,"linkable":134,"proposed":63,"self":63},"DSO [ 16 ] (monocular)","dso2018",{"name":295,"methodId":296,"linkable":134,"proposed":63,"self":63},"ORB [ 52 ] (monocular)","orbslam2015",{"name":298,"methodId":299,"linkable":134,"proposed":63,"self":63},"VINS [ 57 ] (monocular-inertial)","vinsfusion2019",{"name":301,"methodId":133,"linkable":134,"proposed":63,"self":63},"OKVIS [ 44 ] (monocular-inertial)",{"name":303,"methodId":46,"linkable":63,"proposed":63,"self":63},"ROVIO [ 3 ] (monocular-inertial)",{"name":305,"methodId":5,"linkable":134,"proposed":63,"self":134},"MSCKF [ 51 ] (monocular-inertial)",{"name":307,"methodId":308,"linkable":134,"proposed":63,"self":63},"SVO [ 22 ] (monocular-inertial)","forster2017preint",{"name":310,"methodId":46,"linkable":63,"proposed":63,"self":63},"VI-ORB [ 54 ] (monocular-inertial)",{"name":312,"methodId":46,"linkable":63,"proposed":63,"self":63},"VI-DSO [ 72 ] (monocular-inertial)",{"name":314,"methodId":46,"linkable":63,"proposed":134,"self":63},"End-end VO (D3VO PoseNet only) (monocular)",{"name":316,"methodId":46,"linkable":63,"proposed":134,"self":63},"Dd (monocular)",{"name":318,"methodId":46,"linkable":63,"proposed":134,"self":63},"Dd+Dp (monocular)",{"name":320,"methodId":46,"linkable":63,"proposed":134,"self":63},"Dd+Du (monocular)",{"name":322,"methodId":46,"linkable":63,"proposed":134,"self":63},"D3VO (monocular)",{"name":324,"methodId":299,"linkable":134,"proposed":63,"self":63},"VINS [ 57 ] (stereo-inertial)",{"name":326,"methodId":133,"linkable":134,"proposed":63,"self":63},"OKVIS [ 44 ] (stereo-inertial)",{"name":328,"methodId":46,"linkable":63,"proposed":63,"self":63},"Basalt [ 71 ] (stereo-inertial)",{"name":330,"methodId":46,"linkable":63,"proposed":134,"self":63},"D3VO (monocular, listed in the stereo-inertial block)",[332,333,334,336,337,339,340,341,342,344,346,348,350,351,353,354,355,356,357,358,359,360,361,362,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,387,388,389,390,391,392,393,394,395,397,399,400,402,404,406,407,408,410,411,412,413,414,415,416,417,418,419,421,422,424,425,426,427,429,430,431,432,433,434,435,436,437,438,439,440,442,444,445,446,447,448,450,451,452,453,454,455,457,458,459,460,461],[147,147,147,227,149,147,149,149,147],[147,147,151,225,149,147,149,149,147],[147,147,154,335,149,147,149,149,147],1.42,[147,147,157,211,149,147,149,149,147],[147,147,160,338,149,147,149,149,147],0.56,[147,147,163,187,149,147,149,149,147],[151,147,147,223,149,147,149,149,147],[151,147,151,148,149,147,149,149,147],[151,147,154,343,149,147,149,149,147],1.48,[151,147,157,345,149,147,149,149,147],1.72,[151,147,160,347,149,147,149,149,147],0.17,[151,147,163,349,149,147,149,149,147],0.72,[154,147,147,172,149,147,149,149,147],[154,147,151,352,149,147,149,149,147],0.35,[154,147,154,172,149,147,149,149,147],[154,147,157,223,149,147,149,149,147],[154,147,160,198,149,147,149,149,147],[154,147,163,227,149,147,149,149,147],[157,147,147,155,149,147,149,149,147],[157,147,151,161,149,147,149,149,147],[157,147,154,155,149,147,149,149,147],[157,147,157,148,149,147,149,149,147],[157,147,160,177,149,147,149,149,147],[157,147,163,363,149,147,149,149,147],0.28,[160,147,147,200,149,147,149,149,147],[160,147,151,205,149,147,149,149,147],[160,147,154,209,149,147,149,149,147],[160,147,157,209,149,147,149,149,147],[160,147,160,209,149,147,149,149,147],[160,147,163,155,149,147,149,149,147],[163,147,147,183,149,147,149,149,147],[163,147,151,187,149,147,149,149,147],[163,147,154,155,149,147,149,149,147],[163,147,157,148,149,147,149,149,147],[163,147,160,172,149,147,149,149,147],[163,147,163,200,149,147,149,149,147],[166,147,147,211,149,147,149,149,147],[166,147,151,148,149,147,149,149,147],[166,147,154,46,147,147,149,149,147],[166,147,157,46,147,147,149,149,147],[166,147,160,46,147,147,149,149,147],[166,147,163,46,151,147,149,149,147],[169,147,147,164,149,147,149,149,147],[169,147,151,223,149,147,149,149,147],[169,147,154,46,147,147,149,149,147],[169,147,157,386,149,147,149,149,147],0.04,[169,147,160,240,149,147,149,149,147],[169,147,163,46,154,147,149,149,147],[171,147,147,211,149,147,149,149,147],[171,147,151,211,149,147,149,149,147],[171,147,154,193,149,147,149,149,147],[171,147,157,252,149,147,149,149,147],[171,147,160,347,149,147,149,149,147],[171,147,163,225,149,147,149,149,147],[174,147,147,396,149,147,149,149,147],1.8,[174,147,151,398,149,147,149,149,147],0.88,[174,147,154,151,149,147,149,149,147],[174,147,157,401,149,147,149,149,147],1.24,[174,147,160,403,149,147,149,149,147],0.78,[174,147,163,405,149,147,149,149,147],1.14,[176,147,147,211,149,147,149,149,147],[176,147,151,225,149,147,149,149,147],[176,147,154,409,149,147,149,149,147],0.63,[176,147,157,240,149,147,149,149,147],[176,147,160,205,149,147,149,149,147],[176,147,163,177,149,147,149,149,147],[98,147,147,164,149,147,149,149,147],[98,147,151,164,149,147,149,149,147],[98,147,154,172,149,147,149,149,147],[98,147,157,252,149,147,149,149,147],[98,147,160,255,149,147,149,149,147],[98,147,163,225,149,147,149,149,147],[420,147,147,223,149,147,149,149,147],12,[420,147,151,164,149,147,149,149,147],[420,147,154,423,149,147,149,149,147],0.55,[420,147,157,223,149,147,149,149,147],[420,147,160,161,149,147,149,149,147],[420,147,163,200,149,147,149,149,147],[428,147,147,223,149,147,149,149,147],13,[428,147,151,164,149,147,149,149,147],[428,147,154,225,149,147,149,149,147],[428,147,157,233,149,147,149,149,147],[428,147,160,255,149,147,149,149,147],[428,147,163,193,149,147,149,149,147],[90,147,147,183,149,147,149,149,147],[90,147,151,255,149,147,149,149,147],[90,147,154,225,149,147,149,149,147],[90,147,157,193,149,147,149,149,147],[90,147,160,46,157,147,149,149,147],[90,147,163,347,149,147,149,149,147],[441,147,147,183,149,147,149,149,147],15,[441,147,151,443,149,147,149,149,147],0.36,[441,147,154,172,149,147,149,149,147],[441,147,157,347,149,147,149,149,147],[441,147,160,46,157,147,149,149,147],[441,147,163,152,149,147,149,149,147],[449,147,147,252,149,147,149,149,147],16,[449,147,151,211,149,147,149,149,147],[449,147,154,193,149,147,149,149,147],[449,147,157,233,149,147,149,149,147],[449,147,160,46,157,147,149,149,147],[449,147,163,223,149,147,149,149,147],[456,147,147,223,149,147,149,149,147],17,[456,147,151,164,149,147,149,149,147],[456,147,154,225,149,147,149,149,147],[456,147,157,233,149,147,149,149,147],[456,147,160,46,157,147,149,149,147],[456,147,163,223,149,147,149,149,147],[463,464,465,104],"failed","other: 0.14+X","other: 0.07+X",[272],[],[],[470],"EuRoC MAV test sequences (all others used for training); RMS of ATE after aligning with ground truth (alignment type not stated); M+I values from Delmerico and Scaramuzza; stereo methods exclude V2_03; X = failure; Dd, Dp, Du = deep depth, pose, uncertainty ablations",{"slug":472,"group":473,"sourceId":474,"sourceLabel":475,"table":97,"selfRows":160,"metrics":476,"seqs":481,"entrants":493,"cells":500,"outcomes":524,"locators":525,"hardware":526,"wordings":527,"notes":528},"licfusion2019-table-ii","licfusion2019:Table II","licfusion2019","Zuo et al., 2019",[477],{"label":478,"unit":479,"statistic":480,"alignment":41},"average trajectory start-end error","m (unit implied, not printed)","mean",[482,486,488,491],{"dataset":483,"sequence":484,"environment":485},"self-collected indoor sequences","Indoor-A (39m)","indoor, handheld",{"dataset":483,"sequence":487,"environment":485},"Indoor-B (86m)",{"dataset":483,"sequence":489,"environment":490},"Indoor-C (55m)","indoor, handheld, violent shaking",{"dataset":483,"sequence":492,"environment":485},"Indoor-D (189m)",[494,495,497],{"name":7,"methodId":5,"linkable":134,"proposed":63,"self":134},{"name":496,"methodId":474,"linkable":134,"proposed":134,"self":63},"LIC-Fusion",{"name":498,"methodId":499,"linkable":134,"proposed":63,"self":63},"LOAM","loam2014",[501,503,505,507,509,511,513,515,516,518,520,522],[147,147,147,502,149,147,149,149,147],0.99,[151,147,147,504,149,147,149,149,147],0.98,[154,147,147,506,149,147,149,149,147],0.66,[147,147,151,508,149,147,149,149,147],1.55,[151,147,151,510,149,147,149,149,147],1.04,[154,147,151,512,149,147,149,149,147],0.46,[147,147,154,514,149,147,149,149,147],49.94,[151,147,154,508,149,147,149,149,147],[154,147,154,517,149,147,149,149,147],2.44,[147,147,157,519,149,147,149,149,147],46.03,[151,147,157,521,149,147,149,149,147],3.68,[154,147,157,523,149,147,149,149,147],5.99,[],[97],[],[],[529],"Indoor handheld sequences at chest height, normal to low light, slow to aggressive motion; no ground truth; average start-end error after returning to the start; unit not printed in the table (sequence lengths given in m)",{"slug":531,"group":532,"sourceId":5,"sourceLabel":6,"table":533,"selfRows":157,"metrics":534,"seqs":544,"entrants":549,"cells":551,"outcomes":556,"locators":558,"hardware":559,"wordings":561,"notes":562},"mourikis2007msckf-text-sec-iv","mourikis2007msckf:Text Sec.IV","Text Sec.IV",[535,538,541],{"label":536,"unit":102,"statistic":104,"alignment":537},"final position error","first-pose",{"label":539,"unit":540,"statistic":104,"alignment":537},"final position error as percentage of travelled distance","%",{"label":542,"unit":543,"statistic":104,"alignment":40},"processing rate of the dataset","Hz",[545],{"dataset":546,"sequence":547,"environment":548},"Minneapolis residential driving sequence","3.2 km","streets of a typical residential area in Minneapolis, MN (outdoor experiment)",[550],{"name":7,"methodId":5,"linkable":134,"proposed":134,"self":134},[552,553,555],[147,147,147,176,147,147,149,149,147],[147,151,147,554,149,147,149,149,147],0.31,[147,154,147,90,149,147,147,149,151],[557],"approximate (value stated as about 10 m)",[73],[560],"single core of an Intel T7200 (2 GHz); processed off-line on recorded data",[],[563,564],"Car-mounted camera and IMU in a residential area of Minneapolis, 1598 images (3 Hz) over about 9 min, 3.2 km; no GPS ground truth; final error inferred from known start and end parking spots (estimate [-7.92 13.14 -0.78] m vs approximately [0 7 0] m); no loop closing and no motion or map priors","Car-mounted camera and IMU in a residential area of Minneapolis, 1598 images (3 Hz) over about 9 min, 3.2 km; no GPS ground truth; final error inferred from known start and end parking spots (estimate [-7.92 13.14 -0.78] m vs approximately [0 7 0] m); no loop closing and no motion or map priors; maximum of 30 camera poses in the state",[566,573,579,585,591,596,603,609,614,621],{"group":567,"slug":568,"sourceLabel":569,"table":570,"selfRows":157,"datasets":571},"msckf2_2013:Table 2","msckf2-2013-table-2","Li & Mourikis, 2013","Table 2",[572],"simulation from 5.5 km urban vehicle dataset",{"group":574,"slug":575,"sourceLabel":569,"table":576,"selfRows":157,"datasets":577},"msckf2_2013:Table 3","msckf2-2013-table-3","Table 3",[578],"simulation from Cheddar Gorge dataset",{"group":580,"slug":581,"sourceLabel":475,"table":582,"selfRows":154,"datasets":583},"licfusion2019:Table I","licfusion2019-table-i","Table I",[584],"self-collected outdoor sequence",{"group":586,"slug":587,"sourceLabel":569,"table":588,"selfRows":154,"datasets":589},"msckf2_2013:Text Sec.9","msckf2-2013-text-sec-9","Text Sec.9",[590],"own vehicle dataset (Riverside, CA)",{"group":592,"slug":593,"sourceLabel":594,"table":582,"selfRows":151,"datasets":595},"dmvio2022:Table I","dmvio2022-table-i","von Stumberg & Cremers, 2022",[107],{"group":597,"slug":598,"sourceLabel":599,"table":600,"selfRows":151,"datasets":601},"forster2017preint:Text Sec.VIII-B2 (drift)","forster2017preint-text-sec-viii-b2-drift","Forster et al., 2017a","Text Sec.VIII-B2 (drift)",[602],"430 m indoor sequence recorded with a forward-looking VI-Sensor, with Vicon ground truth; dataset and OKVIS and MSCKF trajectories obtained from the OKVIS authors",{"group":604,"slug":605,"sourceLabel":606,"table":570,"selfRows":151,"datasets":607},"ghadimzadeh2025slamnde:Table 2","ghadimzadeh2025slamnde-table-2","Ghadimzadeh Alamdari et al., 2025",[608],"Luleå SubT tunnel dataset (Koval et al. 2022)",{"group":610,"slug":611,"sourceLabel":569,"table":612,"selfRows":151,"datasets":613},"msckf2_2013:Text Sec.3.4","msckf2-2013-text-sec-3-4","Text Sec.3.4",[572],{"group":615,"slug":616,"sourceLabel":617,"table":618,"selfRows":151,"datasets":619},"okvis2015:Text Sec.VII-B1","okvis2015-text-sec-vii-b1","Leutenegger et al., 2015","Text Sec.VII-B1",[620],"Vicon Loops (authors' dataset)",{"group":622,"slug":623,"sourceLabel":624,"table":97,"selfRows":151,"datasets":625},"orbslam3_2021:Table II","orbslam3-2021-table-ii","Campos et al., 2021",[626],"EuRoC",1790510663172]