[{"data":1,"prerenderedAt":878},["ShallowReactive",2],{"method-svo2017":3},{"method":4,"reference":58,"equipment":80,"figures":129,"results":130},{"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":23,"limitations":26,"sensors":28,"platform":32,"estimator":35,"association":36,"timeModel":37,"deskew":38,"loopClosure":39,"globalOptimization":40,"mapRepresentation":41,"prior":42,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"svo2017","Forster et al., 2017b","SVO","SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems",2017,"recent","C08","odometry_with_local_mapping","SVO 採半直接法（semi-direct）：以直接法追蹤並三角化影像梯度高的像素（含弱角點與邊緣），再以成熟的特徵式方法聯合最佳化結構與運動，並用顯式建模離群值的機率深度濾波器估計深度。期刊版將方法擴充到多相機、邊緣特徵、運動先驗及魚眼等大視角鏡頭。其定位為速度優先的視覺里程計，沒有迴圈閉合。","SVO tracks and triangulates high-gradient pixels directly but optimises structure and motion with feature-based methods, using a robust depth filter; the journal version adds multi-camera, edgelet, motion-prior and wide-FoV support.","full_text_reviewed","peer_reviewed_published","background","論文未報告營建測試；作者提及四旋翼視覺飛行與手機 3D 掃描應用，但未提供工地證據。",[20,21,22],"public_benchmark","simulation","controlled_experiment",[24,25],"Significantly faster than state-of-the-art VO while competitive in accuracy (abstract)","Tracks weak corners and edgelets in low- or high-frequency texture (abstract)",[27],"keep; add: monocular SVO fails on several EuRoC Vicon room sequences because of abrupt illumination changes and on-spot rotations (Sec. XI-B-1, Table I); iSAM2 bundle adjustment could not be applied on ICL-NUIM because frequent on-spot rotations leave underconstrained variables (Sec. XI-B-3)",[29,30,31],"monocular camera","multi-camera","fisheye",[33,34],"UAV","handheld","three-step motion estimation: coarse-to-fine sparse image alignment on 4x4 patches with a robust cost for frame-to-frame motion, 2D alignment of 8x8 feature patches against the frame of first observation, then reprojection-error refinement of the latest pose and points, or full keyframe bundle adjustment with iSAM2 (Sec. V, X-B, X-C, XI-B)","semi-direct: direct alignment of high-gradient pixels (FAST corners, or the highest-gradient pixel as an edgelet in cells of 32x32 pixels without corners) plus feature alignment and refinement; depth by epipolar ZMSSD search on 8x8 patches feeding a Gaussian plus uniform depth filter with Beta inlier ratio; at most 180 matched features per frame (Sec. VI, X-C, X-D)","discrete poses","not_applicable","none (visual odometry)","none","sparse 3D points and edgelets with depth filters; only a small local map of the last five to ten keyframes is kept (Sec. XI-B-1)","optional relative translation prior (e.g. constant velocity) and relative rotation prior (e.g. integrated gyroscope) added to the alignment cost; known camera intrinsics and extrinsics from prior calibration (Sec. IV, IX)","camera trajectory and sparse points","laptop Intel Core i7-2760QM (2.80 GHz): SVO Mono 2.53 ms per frame and about 55 % CPU at 20 Hz input versus 29.81 ms for ORB-SLAM and 23.23 ms for LSD-SLAM (Table II); per-component timings also reported for an NVIDIA Jetson TX1 (Table III)","https:\u002F\u002Fgithub.com\u002Fuzh-rpg\u002Frpg_svo_pro_open","GPLv3 (LICENSE file of rpg_svo_pro_open)",[48,52,56],{"relation":49,"title":50,"doi_or_url":51},"conference_version","SVO: Fast semi-direct monocular visual odometry (ICRA 2014, pp. 15-22)","10.1109\u002FICRA.2014.6906584",{"relation":53,"title":54,"doi_or_url":55},"code_release","rpg_svo (GPLv3 per README; README cites the ICRA 2014 paper, i.e., the conference-version implementation)","https:\u002F\u002Fgithub.com\u002Fuzh-rpg\u002Frpg_svo",{"relation":53,"title":57,"doi_or_url":45},"rpg_svo_pro_open (SVO Pro; README calls it the newest SVO version, cites the T-RO 2017 paper, and adds a sliding-window VIO back-end modified from OKVIS, an iSAM2 global map and DBoW2 loop closure, i.e., a superset of the paper's VO)",{"id":5,"kind":59,"shortName":7,"title":8,"authors":60,"year":9,"venue":66,"venueType":67,"publisher":68,"volumeIssuePages":69,"doi":70,"arxivId":71,"url":72,"firstPublicDate":73,"publicationStatus":16,"metadataStatus":74,"fulltextStatus":15,"era":10,"classicReason":38,"codeUrl":45,"cluster":11,"topics":75,"mdpi":76,"verification":77,"label":6,"fulltextRoute":78,"versionRead":79,"addedByCensus":76},"method",[61,62,63,64,65],"Christian Forster","Zichao Zhang","Michael Gassner","Manuel Werlberger","Davide Scaramuzza","IEEE Transactions on Robotics","journal","IEEE","33(2):249-265","10.1109\u002Ftro.2016.2623335",null,"https:\u002F\u002Frpg.ifi.uzh.ch\u002Fdocs\u002FTRO16_Forster-SVO.pdf","2016-12-14","metadata_verified",[11],false,"corrected","NTU institutional (Chrome)","accepted manuscript (18 pages, UZH\u002FZORA copy TRO16_Forster-SVO.pdf) read in full; version of record IEEE T-RO 33(2):249-265 checked on IEEE Xplore (NTU access): Sec. XI to XIII text and Table I to III images read; VoR Table I differs from the manuscript (ORB-SLAM values replaced by values from the DSO paper and a real-time ORB-SLAM column added), so Table I values follow the VoR",[81,89,94,100,105,112,115,119,125],{"category":82,"model":83,"canonical":84,"role":85,"dataset":86,"specs":87,"locator":88},"stereo_camera","VI Sensor","VI-Sensor","dataset sensor","EuRoC","stereo images and inertial data; mounted on a micro aerial vehicle; 11 sequences, 19 min in three indoor environments","Sec. 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XI-B-2",{"category":106,"model":107,"canonical":107,"role":108,"dataset":109,"specs":110,"locator":111},"camera","down-facing perspective camera (model not reported)","method input","own circle dataset","circle flight on a micro aerial vehicle","Sec. XI-B-4",{"category":106,"model":113,"canonical":113,"role":108,"dataset":109,"specs":114,"locator":111},"wide fisheye camera (model not reported)","same circle trajectory flown again with a fisheye lens",{"category":116,"model":117,"canonical":117,"role":108,"dataset":109,"specs":118,"locator":111},"platform","micro aerial vehicle (model not reported)","flown in a motion capture room along a commanded circle",{"category":120,"model":121,"canonical":121,"role":122,"dataset":71,"specs":123,"locator":124},"compute","Intel Core i7-2760QM laptop","compute for runtime","2.80 GHz","Sec. 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