[{"data":1,"prerenderedAt":124},["ShallowReactive",2],{"method-gvins2022":3},{"method":4,"reference":66,"equipment":87,"figures":123,"results":120},{"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":22,"limitations":28,"sensors":34,"platform":38,"estimator":42,"association":43,"timeModel":44,"deskew":45,"loopClosure":46,"globalOptimization":47,"mapRepresentation":48,"prior":49,"outputGeometry":50,"compute":51,"codeUrl":52,"codeLicense":53,"relatedVersions":54},"gvins2022","Cao et al., 2022","GVINS","GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation",2022,"recent","C08","odometry_with_local_mapping","GVINS 在 VINS-Mono 的滑動視窗非線性最佳化中，直接加入 GNSS 原始量測（碼偽距與都卜勒頻移）以及接收器時鐘偏差與漂移因子，與影像及 IMU 緊耦合，提供無漂移的全域六自由度位姿。系統先以單點定位得到粗略錨點，再用都卜勒量測校正區域座標與 ENU 座標間的偏航角，最後以偽距精修錨點，完成線上初始化。對低速、衛星少於 4 顆與完全無 GNSS 的退化情況分別處理，可在室內外轉換時衛星遺失與重新鎖定之間連續運作。","GVINS tightly fuses raw GNSS pseudorange and Doppler with visual-inertial data in a sliding-window factor graph, using a coarse-to-fine online initialisation of the local-to-ENU yaw and anchor, and explicit handling of degenerate cases, to give drift-free global 6-DoF estimates across indoor-outdoor transitions.","full_text_reviewed","peer_reviewed_published","supplementary","論文未在營建工地測試；實驗包括校園運動場、含室內樓梯、橋下與樹林的 3 km 室內外路線，以及香港 22.9 km 市區行車，並以 RTK 為參考。它能在衛星時有時無的室內外轉換中持續提供全域位姿，對把相機慣性軌跡或點雲地理參考到工地座標、以及在高樓遮蔽處維持定位有參考價值；但全域位置仍有公尺級偏差，不足以直接作為測量控制（推論）。",[20,21],"simulation","independent_reference",[23,24,25,26,27],"RMSE 0.806 m (sports field), 3.700 m (indoor-outdoor) and 4.508 m (22.9 km urban driving) versus 2.835 m, 6.036 m and 11.106 m for RTKLIB SPP (Table III)","Survives the whole urban sequence where VINS-Mono failed at 54% of the distance and VINS-Fusion oscillated with RMSE on the order of 10^5 m (Sec. VIII-B5)","Still benefits from fewer than four satellites, even a single satellite, compared with pure VIO (Sec. VIII-B2; Figs. 13-14)","Keeps global estimates indoors and on stairs where RTK showed errors up to 80 m during outages (Sec. VIII-B3; Figs. 1, 16)","Window optimisation 21.91 ms versus an average 404.83 ms (up to 1018.46 ms) for the VINS-Fusion pose-graph fusion (Sec. VIII-B5)",[29,30,31,32,33],"Global positions carry a bias from satellite orbit error, imperfect atmospheric delay models and multipath, especially in the up direction (Secs. VIII-B1, VIII-B3, VIII-B5)","With three or fewer satellites the up-direction drift is no longer removed; with two or one satellite horizontal and yaw drift appear (Sec. VIII-B2)","Low-speed or rotation-only motion cannot constrain the yaw offset, which is fixed below 0.3 m\u002Fs (Sec. VII-B1)","Initialisation needs at least four satellites (N+3 for N constellations) and about 4 m of travel, and can wait several seconds for ephemerides (Secs. VII-A, VIII-B3)","Doppler-only operation drifts and keeps the initialisation bias (Sec. VIII-B4)",[35,36,37],"monocular camera (left camera of a VI-Sensor)","IMU","GNSS receiver raw code pseudorange and Doppler (GPS, GLONASS, Galileo, BeiDou)",[39,40,41],"helmet-mounted (walking: sports field; indoor-outdoor route over 3 km with stairs)","urban driving (22.9 km in Hong Kong; vehicle mounting not described)","simulation (10 km trajectory in a 30 m cube)","tightly coupled sliding-window non-linear optimisation (window size 10) on a factor graph inherited from VINS-Mono: IMU preintegration, visual reprojection, GNSS code pseudorange and Doppler factors, receiver clock bias and drift factors; states include the yaw offset between local world and ENU frames and per-constellation clock biases; two-way marginalisation; robust norm on GNSS factors in urban driving (Secs. V, VI, VIII-B5)","strong corners tracked with iterative Lucas-Kanade optical flow (VINS-Mono front end); GNSS satellites filtered by elevation, health and continuous lock (Secs. V, VI-C)","discrete keyframe states; GNSS time aligned to local time beforehand via the receiver PPS signal triggering the VI-Sensor (Secs. III-A, VIII-B)","not_applicable","none (VINS-Mono and VINS-Fusion loop closure also disabled in the comparisons)","none; global drift-free estimation comes from GNSS raw-measurement factors inside the sliding window rather than from loop closure","sparse features with inverse depth in the sliding window","broadcast ephemeris; Saastamoinen tropospheric and Klobuchar ionospheric models; no base station needed for the estimator (RTK used only as ground truth) (Secs. IV-D, VIII-B)","global 6-DoF pose in ECEF and a local ENU frame, including global yaw; no dense map","Intel i7-8700K at 3.7 GHz, 32 GB: feature detection and tracking 7.28 ms per frame and window optimisation 21.91 ms on the urban sequence, real time with a 20 Hz camera (Sec. VIII-B5)","https:\u002F\u002Fgithub.com\u002FHKUST-Aerial-Robotics\u002FGVINS","GPL-3.0 (README licence section)",[55,59,62],{"relation":56,"title":57,"doi_or_url":58},"preprint","GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation (arXiv v1 to v3)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.07899",{"relation":60,"title":61,"doi_or_url":52},"code_release","HKUST-Aerial-Robotics\u002FGVINS",{"relation":63,"title":64,"doi_or_url":65},"dataset","HKUST-Aerial-Robotics\u002FGVINS-Dataset (well-synchronised camera, IMU and GNSS raw data with RTK ground truth, per the paper)","https:\u002F\u002Fgithub.com\u002FHKUST-Aerial-Robotics\u002FGVINS-Dataset",{"id":5,"kind":67,"shortName":7,"title":68,"authors":69,"year":9,"venue":73,"venueType":74,"publisher":75,"volumeIssuePages":76,"doi":77,"arxivId":78,"url":58,"firstPublicDate":79,"publicationStatus":16,"metadataStatus":80,"fulltextStatus":15,"era":10,"classicReason":45,"codeUrl":52,"cluster":11,"topics":81,"mdpi":82,"verification":83,"label":6,"fulltextRoute":84,"versionRead":85,"addedByCensus":86},"method","GVINS: Tightly Coupled GNSS–Visual–Inertial Fusion for Smooth and Consistent State Estimation",[70,71,72],"Shaozu Cao","Xiuyuan Lu","Shaojie Shen","IEEE Transactions on Robotics","journal","IEEE","38(4):2004-2021","10.1109\u002Ftro.2021.3133730","2103.07899","2021-03-14","metadata_verified",[11],false,"corrected","arXiv","arXiv v3 (2021-08-31); IEEE version of record not read",true,[88,95,100,104,108,111,116],{"category":89,"model":90,"canonical":90,"role":91,"dataset":92,"specs":93,"locator":94},"stereo_camera","VI-Sensor (Aptina MT9V034 camera sensors; left camera used)","method input","GVINS-Dataset","global shutter, 752 x 480, horizontal FOV 98 deg, vertical FOV 73 deg, 20 Hz; camera and IMU synchronised by the VI-Sensor and triggered by the GNSS PPS","Sec. VIII-B; Table IV",{"category":96,"model":97,"canonical":97,"role":91,"dataset":92,"specs":98,"locator":99},"imu","ADIS16448","200 Hz; gyroscope noise density 7.0e-3 deg\u002Fs\u002Fsqrt(Hz); accelerometer noise density 6.6e-4 m\u002Fs^2\u002Fsqrt(Hz)","Table IV",{"category":101,"model":102,"canonical":102,"role":91,"dataset":92,"specs":103,"locator":94},"gnss","u-blox ZED-F9P","low-cost multi-band, multi-constellation receiver; raw measurements at 10 Hz",{"category":101,"model":105,"canonical":105,"role":106,"dataset":92,"specs":107,"locator":94},"u-blox ZED-F9P internal RTK engine with RTCM from a nearby base station","reference or ground truth","RTK solution at 10 Hz; about 1 cm accuracy in open areas; fails indoors and under heavy blockage",{"category":101,"model":109,"canonical":109,"role":91,"dataset":92,"specs":110,"locator":99},"Tallysman TW3882 antenna","GNSS antenna of the receiver",{"category":112,"model":113,"canonical":113,"role":91,"dataset":92,"specs":114,"locator":115},"platform","helmet carrying the VI-Sensor and ZED-F9P","used in the real-world experiments","Fig. 10",{"category":117,"model":118,"canonical":118,"role":119,"dataset":120,"specs":121,"locator":122},"compute","Intel i7-8700K at 3.7 GHz, 32 GB","compute for runtime",null,"desktop PC used for all experiments","Sec. VIII",[],1790510657301]