[{"data":1,"prerenderedAt":96},["ShallowReactive",2],{"method-openvins2020":3},{"method":4,"reference":55,"equipment":78,"figures":95,"results":68},{"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":33,"platform":36,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":44,"mapRepresentation":45,"prior":46,"outputGeometry":47,"compute":48,"codeUrl":49,"codeLicense":50,"relatedVersions":51},"openvins2020","Geneva et al., 2020","OpenVINS","OpenVINS: A Research Platform for Visual-Inertial Estimation",2020,"recent","C08","odometry_with_local_mapping","OpenVINS 是以研究平台定位的開源視覺慣性估測程式庫，核心為流形上的滑動視窗 EKF（MSCKF），採用首次估計 Jacobian（FEJ）維持一致性，並可把部分特徵作為 SLAM 地標保留在狀態中。系統支援相機內參、相機與 IMU 外參及時間偏移的線上校正，以型別化索引系統自動管理狀態與共變異數，另附以 SE(3) B-spline 產生量測的模擬器與軌跡評估工具。作者在模擬與 EuRoC 資料上與多個開源 VIO 比較，顯示其精度具競爭力。","OpenVINS is an open, documented research platform whose core is an on-manifold FEJ-MSCKF sliding-window filter with optional SLAM landmarks and online intrinsic, extrinsic and time-offset calibration, plus a B-spline visual-inertial simulator and evaluation toolbox.","full_text_reviewed","peer_reviewed_published","background","論文未報告營建工地測試；實測只用 EuRoC 室內 Vicon 房間序列，並有模擬實驗。其線上內外參與時間偏移校正、開源程式與模擬器，對施工現場以低成本相機與 IMU 組裝感測器並評估 VIO 一致性有參考價值；系統只輸出軌跡與稀疏地標，需另接建圖模組才能產生點雲（推論）。",[20,21],"simulation","public_benchmark",[23,24,25,26,27],"With online calibration the estimator stays consistent from bad initial calibration (ATE 0.139 m, position NEES 2.007), whereas disabling calibration with a bad guess gives 508.719 m ATE and often diverges (Table I; Sec. V-A)","Monocular OpenVINS with SLAM landmarks had the lowest average position ATE (0.079 m) among monocular methods on five EuRoC Vicon-room sequences (Table II)","Adding SLAM landmarks greatly reduces monocular drift (Sec. V-B; Table II)","Monocular variant best in RPE among compared open-source codes; stereo variant second to Basalt (Table III; Sec. V-B)","Documentation, simulator and evaluation toolbox are treated as main contributions (Secs. III-E, IV)",[29,30,31,32],"Per-frame runtime not rigorously evaluated; Basalt was faster, and OpenVINS was limited by its OpenCV front end and SLAM feature update (Sec. V-B)","SLAM landmarks bring a smaller gain in the stereo case (Sec. V-B)","Only the VIO part is evaluated; no loop closure or mapping back end (Sec. V-B)","Real-world evaluation limited to EuRoC Vicon-room sequences, with V2_03 excluded because some methods could not run on it (Tables II-III)",[34,35],"monocular or stereo camera (arbitrary number of cameras supported)","IMU",[37,38],"simulation (B-spline trajectories, 10 Hz camera, 400 Hz IMU)","UAV (EuRoC MAV Vicon room sequences)","modular on-manifold EKF over a sliding window of stochastic IMU pose clones (MSCKF) with First-Estimates Jacobians; optional SLAM landmarks kept in the state and initialised by QR (Givens) splitting of the linearised system; type-based index system manages state and covariance (Secs. II, III-A, III-B, V-A)","sparse visual feature tracking with an OpenCV-based front end; features within the window used in nullspace-projected MSCKF updates; SLAM landmarks in several parameterisations (global 3D, inverse MSCKF, full inverse depth, anchored 3D); errors on raw pixels to allow intrinsic calibration (Secs. II, III-C, V-B)","discrete IMU propagation; single camera-IMU time offset estimated online (Secs. II-A, III-D)","not_applicable","none (VIO only; authors note a pose-graph optimiser could be appended, Sec. V-B)","none","sliding window of IMU pose clones plus a bounded set of SLAM landmarks (up to 50 in the simulation setup) as 3D points in the state","none; calibration initial values can be poor and are refined online (Sec. V-A)","IMU pose trajectory with covariance, online camera intrinsics, camera-IMU extrinsics and time offset, and sparse landmark positions; no dense map","single-threaded Intel Xeon E3-1505M v6 at 3.00 GHz: 2.7x and 4.3x real time for monocular SLAM and VIO, 1.2x and 1.9x for stereo SLAM and VIO on the first EuRoC sequence; per-frame timing not rigorously evaluated and limited by the OpenCV front end and SLAM feature updates (Sec. V-B)","https:\u002F\u002Fgithub.com\u002Frpng\u002Fopen_vins","GPL-3.0 (LICENSE file)",[52],{"relation":53,"title":54,"doi_or_url":49},"code_release","rpng\u002Fopen_vins (ov_core, ov_eval, ov_msckf) with documentation at docs.openvins.com",{"id":5,"kind":56,"shortName":7,"title":8,"authors":57,"year":9,"venue":63,"venueType":64,"publisher":65,"volumeIssuePages":66,"doi":67,"arxivId":68,"url":69,"firstPublicDate":70,"publicationStatus":16,"metadataStatus":71,"fulltextStatus":15,"era":10,"classicReason":42,"codeUrl":49,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":77},"method",[58,59,60,61,62],"Patrick Geneva","Kevin Eckenhoff","Woosik Lee","Yulin Yang","Guoquan Huang","2020 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 4666-4672","10.1109\u002Ficra40945.2020.9196524",null,"https:\u002F\u002Fdoi.org\u002F10.1109\u002FICRA40945.2020.9196524","2020-05","metadata_verified",[11],false,"corrected","author copy","author-hosted PDF (created 2020-03-05; appears to be the accepted ICRA 2020 manuscript); IEEE version of record not read",true,[79,86,90],{"category":80,"model":81,"canonical":81,"role":82,"dataset":83,"specs":84,"locator":85},"stereo_camera","EuRoC MAV stereo camera (model not reported in this paper)","dataset sensor","EuRoC MAV","20 Hz stereo images","Sec. V-B",{"category":87,"model":88,"canonical":88,"role":82,"dataset":83,"specs":89,"locator":85},"imu","ADIS16448","MEMS IMU, 200 Hz",{"category":91,"model":92,"canonical":92,"role":93,"dataset":68,"specs":94,"locator":85},"compute","Intel Xeon E3-1505M v6 @ 3.00GHz","compute for runtime","single-threaded execution",[],1790510662395]