[{"data":1,"prerenderedAt":95},["ShallowReactive",2],{"method-yuan2021lidarcameracalib":3},{"method":4,"reference":53,"equipment":73,"figures":94,"results":78},{"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":21,"limitations":27,"sensors":32,"platform":35,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":41,"globalOptimization":42,"mapRepresentation":41,"prior":43,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":47,"relatedVersions":48},"yuan2021lidarcameracalib","Yuan et al., 2021","livox_camera_calib","Pixel-Level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments",2021,"recent","C13","sensing_calibration_sync_preprocessing","本法不用棋盤格，而以自然場景中的邊緣特徵對齊 LiDAR 與相機。作者依 LiDAR 量測原理分析：深度不連續邊緣受前景與背景混合影響不可靠，因此改以體素切分與平面擬合取得深度連續邊緣，並分析邊緣分布對校正精度的敏感度。報告在多種室內外場景達到像素級精度。","Targetless LiDAR-camera extrinsic calibration from plane-intersection edges, avoiding unreliable depth-discontinuity edges, reaching pixel-level accuracy.","full_text_reviewed","peer_reviewed_published","background","not_reported。作者以稠密點雲建圖、點雲著色與自動化 3D 測量為動機（Sec. I），但未在工地測試。文中量得棋盤格因雷射束發散造成的前景膨脹約 1.4 cm（6 m 距離，約 0.13°）（Sec. IV-B），提示深度不連續邊緣附近的尺寸量測需留意；外參誤差也直接影響著色點雲與影像量測（推論）。",[20],"controlled_experiment",[22,23,24,25,26],"Accuracy on par with target-based methods in natural scenes; robust to initial values (Sec. I, Sec. V)","Converged in all 7 scene settings from 20 random initial values each (±5°, ±10 cm) (Sec. IV-A1)","About 50% of residuals, including mean and median, within one pixel in all 36 cross-validation cases (Sec. IV-A3)","Accuracy similar to the checkerboard method of Zhou et al. while using one data pair instead of more than 36 board poses (Sec. IV-B)","Estimated covariances are consistent across scenes (Sec. IV-A2, Fig. 15)",[28,29,30,31],"Designed for high-resolution LiDAR; sparse spinning LiDAR needs scans accumulated by slight motion and LiDAR-inertial odometry (Sec. IV-C)","Degrades in scenes with only cylindrical objects, unevenly distributed edges or edges in one direction only (Sec. IV-A4, Fig. 17)","Fine calibration needs a good initial estimate; a rough grid search is used to widen convergence (Sec. III-E)","Needs about 20 s of stationary Avia data per scene; the 4 s ACSC recordings required three clouds (Sec. IV, IV-B)",[33,34],"Livox Avia solid-state LiDAR (non-repetitive scanning, 20 s accumulation) with Intel RealSense D435i camera (main suite)","Ouster OS2-64 spinning LiDAR with MV-CA013-21UC industrial camera (Sec. IV-C; detailed results in supplementary material)",[36,37],"static sensor suite fixed in a stable position","spinning LiDAR moved slightly and tracked with LiDAR-inertial odometry to densify scans","rough calibration by alternating grid search (0.5° rotation, 2 cm translation) maximising the percentage of edge correspondences, then iterative maximum-likelihood point-to-edge reprojection estimation on SE(3) weighting LiDAR range and bearing noise and 1.5-pixel image edge noise; calibration covariance from the inverse Hessian","depth-continuous LiDAR edges from voxel cutting (e.g., 1 m outdoor, 0.5 m indoor), repeated RANSAC plane fitting and intersection of connected plane pairs at 30° to 150°; points sampled on each edge projected and matched to the κ nearest Canny edge pixels in a 2-D k-d tree, with a direction-orthogonality check","not_applicable (static scenes)","not_applicable","none","initial extrinsic (e.g., from CAD)","LiDAR-camera extrinsic for point cloud colorization","offline; the entire pipeline (extraction, matching, rough and fine calibration) takes less than 60 s; hardware not reported","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002Flivox_camera_calib","GPL-2.0",[49],{"relation":50,"title":51,"doi_or_url":52},"preprint","Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments","https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.01627",{"id":5,"kind":54,"shortName":7,"title":8,"authors":55,"year":9,"venue":60,"venueType":61,"publisher":62,"volumeIssuePages":63,"doi":64,"arxivId":65,"url":52,"firstPublicDate":66,"publicationStatus":16,"metadataStatus":67,"fulltextStatus":15,"era":10,"classicReason":41,"codeUrl":46,"cluster":11,"topics":68,"mdpi":69,"verification":70,"label":6,"fulltextRoute":71,"versionRead":72,"addedByCensus":69},"method",[56,57,58,59],"Chongjian Yuan","Xiyuan Liu","Xiaoping Hong","Fu Zhang","IEEE Robotics and Automation Letters","journal","IEEE","6(4), pp. 7517-7524","10.1109\u002Flra.2021.3098923","2103.01627","2021-03-02","metadata_verified",[11],false,"confirmed","arXiv","arXiv v2 (2021-06-25), 8 pages; RA-L version of record 6(4):7517-7524 not compared; the GitHub supplementary material with the Ouster results was not read",[74,81,87,91],{"category":75,"model":76,"canonical":76,"role":77,"dataset":78,"specs":79,"locator":80},"lidar","Livox Avia","method input",null,"solid-state, non-repetitive scanning; 20 s accumulation per scene; vertical beam divergence angle 0.28° cited in Sec. IV-B","Fig. 10; Sec. IV, IV-B",{"category":82,"model":83,"canonical":84,"role":77,"dataset":78,"specs":85,"locator":86},"camera","Intel RealSense D435i","Intel RealSense D435I","written 'Intel Realsense-D435i'; intrinsics and distortion calibrated beforehand","Fig. 10; Sec. IV",{"category":75,"model":88,"canonical":88,"role":77,"dataset":78,"specs":89,"locator":90},"Ouster OS2-64","spinning multi-line LiDAR with lower resolution at stationary","Fig. 10; Sec. IV-C",{"category":82,"model":92,"canonical":92,"role":77,"dataset":78,"specs":93,"locator":90},"MV-CA013-21UC","industrial camera",[],1790510661649]