{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/reflectance-intensity-assisted-automatic-and","title":"Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard","arxiv_id":"1708.05514","date":"2017-08-18","proceeding":null,"authors":["Weimin Wang","Ken Sakurada","Nobuo Kawaguchi"],"abstract":"This paper presents a novel method for fully automatic and convenient\nextrinsic calibration of a 3D LiDAR and a panoramic camera with a normally\nprinted chessboard. The proposed method is based on the 3D corner estimation of\nthe chessboard from the sparse point cloud generated by one frame scan of the\nLiDAR. To estimate the corners, we formulate a full-scale model of the\nchessboard and fit it to the segmented 3D points of the chessboard. The model\nis fitted by optimizing the cost function under constraints of correlation\nbetween the reflectance intensity of laser and the color of the chessboard's\npatterns. Powell's method is introduced for resolving the discontinuity problem\nin optimization. The corners of the fitted model are considered as the 3D\ncorners of the chessboard. Once the corners of the chessboard in the 3D point\ncloud are estimated, the extrinsic calibration of the two sensors is converted\nto a 3D-2D matching problem. The corresponding 3D-2D points are used to\ncalculate the absolute pose of the two sensors with Unified Perspective-n-Point\n(UPnP). Further, the calculated parameters are regarded as initial values and\nare refined using the Levenberg-Marquardt method. The performance of the\nproposed corner detection method from the 3D point cloud is evaluated using\nsimulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR\nand a Ladybug3 camera under the proposed re-projection error metric,\nqualitatively and quantitatively demonstrate the accuracy and stability of the\nfinal extrinsic calibration parameters.","url_abs":"http://arxiv.org/abs/1708.05514v1","url_pdf":"http://arxiv.org/pdf/1708.05514v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"reflectance-intensity-assisted-automatic-and","repo_url":"https://github.com/mfxox/ILCC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}