{"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/lidar-camera-calibration-using-3d-3d-point","title":"LiDAR-Camera Calibration using 3D-3D Point correspondences","arxiv_id":"1705.09785","date":"2017-05-27","proceeding":null,"authors":["Ankit Dhall","Kunal Chelani","Vishnu Radhakrishnan","K. M. Krishna"],"abstract":"With the advent of autonomous vehicles, LiDAR and cameras have become an\nindispensable combination of sensors. They both provide rich and complementary\ndata which can be used by various algorithms and machine learning to sense and\nmake vital inferences about the surroundings. We propose a novel pipeline and\nexperimental setup to find accurate rigid-body transformation for extrinsically\ncalibrating a LiDAR and a camera. The pipeling uses 3D-3D point correspondences\nin LiDAR and camera frame and gives a closed form solution. We further show the\naccuracy of the estimate by fusing point clouds from two stereo cameras which\nalign perfectly with the rotation and translation estimated by our method,\nconfirming the accuracy of our method's estimates both mathematically and\nvisually. Taking our idea of extrinsic LiDAR-camera calibration forward, we\ndemonstrate how two cameras with no overlapping field-of-view can also be\ncalibrated extrinsically using 3D point correspondences. The code has been made\navailable as open-source software in the form of a ROS package, more\ninformation about which can be sought here:\nhttps://github.com/ankitdhall/lidar_camera_calibration .","url_abs":"http://arxiv.org/abs/1705.09785v1","url_pdf":"http://arxiv.org/pdf/1705.09785v1.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":"lidar-camera-calibration-using-3d-3d-point","repo_url":"https://github.com/ankitdhall/lidar_camera_calibration","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"lidar-camera-calibration-using-3d-3d-point","repo_url":"https://github.com/agarwa65/lidar_camera_calibration","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"lidar-camera-calibration-using-3d-3d-point","repo_url":"https://github.com/eric-erki/-lidar_camera_calibration","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"lidar-camera-calibration-using-3d-3d-point","repo_url":"https://github.com/zgxsin/lidar_camera_calibration","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"lidar-camera-calibration-using-3d-3d-point","repo_url":"https://github.com/zlbing/camera_lidar_calibrate","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"camera-calibration","task_name":"Camera Calibration"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}