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The proposed method is used to solve the problem of inaccurate edge estimation of LiDAR with different horizontal angle resolutions and low calibration efficiency. First, we design a novel calibration target, adding four hollow rectangles for fully automatic locating of the calibration target and increasing the number of corner points. Second, an edge refinement strategy based on background point clouds is proposed to estimate the target edge more accurately. Third, a two-step method of automatically matching between the calibration target in 3D point clouds and the 2D image is proposed. Through this method, i.e., locating firstly and then fine processing, corner points can be automatically obtained, which can greatly reduce the manual operation. Finally, a joint optimization equation is established to optimize the camera\u2019s intrinsic and extrinsic parameters of LiDAR and camera. According to our experiments, we prove the accuracy and robustness of the proposed method through projection and data consistency verifications. The accuracy can be improved by at least 15.0% when testing on the comparable traditional methods. The final results verify that our method is applicable to LiDAR with large horizontal angle resolutions.<\/jats:p>","DOI":"10.3390\/rs14246385","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T08:41:41Z","timestamp":1671439301000},"page":"6385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Calibration between Multi-Lines LiDAR and Visible Light Camera Based on Edge Refinement and Virtual Mask Matching"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0040-4905","authenticated-orcid":false,"given":"Chengkai","family":"Chen","sequence":"first","affiliation":[{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528300, China"},{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhui","family":"Lan","sequence":"additional","affiliation":[{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528300, China"},{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2537-6138","authenticated-orcid":false,"given":"Haoting","family":"Liu","sequence":"additional","affiliation":[{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528300, China"},{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0585-5262","authenticated-orcid":false,"given":"Shuai","family":"Chen","sequence":"additional","affiliation":[{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528300, China"},{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohan","family":"Wang","sequence":"additional","affiliation":[{"name":"Shunde Innovation School, University of Science and Technology Beijing, Foshan 528300, China"},{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Goian, A., Ashour, R., Ahmad, U., Taha, T., Almoosa, N., and Seneviratne, L. (2019). Victim Localization in USAR Scenario Exploiting Multi-Layer Mapping Structure. Remote Sens., 11.","DOI":"10.3390\/rs11222704"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hong, Z., Zhong, H., Pan, H., Liu, J., Zhou, R., Zhang, Y., Han, Y., Wang, J., Yang, S., and Zhong, C. (2022). Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images. Sensors, 22.","DOI":"10.3390\/s22155920"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Raman, M., Carlos, E., and Sankaran, S. (2022). Optimization and Evaluation of Sensor Angles for Precise Assessment of Architectural Traits in Peach Trees. Sensors, 22.","DOI":"10.3390\/s22124619"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhu, W., Sun, Z., Peng, J., Huang, Y., Li, J., Zhang, J., Yang, B., and Liao, X. (2019). Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sens., 11.","DOI":"10.3390\/rs11222678"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, D., Xing, S., He, Y., Yu, J., Xu, Q., and Li, P. (2022). Evaluation of a new Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection. Sensors, 22.","DOI":"10.3390\/s22041379"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, S., Nian, Y., He, Z., and Che, M. (2022). Measuring the Tree Height of Picea Crassifolia in Alpine Mountain Forests in Northwest China Based on UAV-LiDAR. Forests, 13.","DOI":"10.3390\/f13081163"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Song, J., Qian, J., Li, Y., Liu, Z., Chen, Y., and Chen, J. (2022). Automatic Extraction of Power Lines from Aerial Images of Unmanned Aerial Vehicles. Sensors, 22.","DOI":"10.3390\/s22176431"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/TPAMI.2004.21","article-title":"Camera Calibration with One-dimensional Objects","volume":"26","author":"Zhang","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1016\/j.patcog.2004.11.005","article-title":"Camera Calibration with Moving One-dimensional Objects","volume":"38","author":"Wu","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bai, Z., Jiang, G., and Xu, A. (2020). LiDAR-Camera Calibration Using Line Correspondences. Sensors, 20.","DOI":"10.3390\/s20216319"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Geiger, A., Moosmann, F., Car, \u00d6., and Schuster, B. (2012, January 14\u201318). Automatic Camera and Range Sensor Calibration Using a Single Shot. Proceedings of the 2012 IEEE International Conference on Robotics and Automation (IEEE ICRA), Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6224570"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cai, H., Pang, W., Chen, X., Wang, Y., and Liang, H. (2020). A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration. Sensors, 20.","DOI":"10.3390\/s20041130"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhou, L., Li, Z., and Kaess, M. (2018, January 1\u20135). Automatic Extrinsic Calibration of a Camera and a 3D LiDAR Using Line and Plane Correspondences. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593660"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guindel, C., Beltr\u00e1n, J., Martin, D., and Garcia, F. (2017, January 16\u201319). Automatic Extrinsic Calibration for Lidar-stereo Vehicle Sensor Setups. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317829"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.3390\/s130201902","article-title":"3D LIDAR-Camera Extrinsic Calibration Using an Arbitrary Trihedron","volume":"13","author":"Gong","year":"2013","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pusztai, Z., and Hajder, L. (2017, January 22\u201329). Accurate Calibration of Lidar-camera Systems Using Ordinary Boxes. Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy.","DOI":"10.1109\/ICCVW.2017.53"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"K\u00fcmmerle, J., and K\u00fchner, T. (August, January 31). Unified Intrinsic and Extrinsic Camera and LiDAR Calibration under Uncertainties. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197496"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5333","DOI":"10.3390\/s140305333","article-title":"Calibration between Color Camera and 3D LIDAR Instruments with a Polygonal Planar Board","volume":"14","author":"Park","year":"2014","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103776","DOI":"10.1016\/j.robot.2021.103776","article-title":"LiDAR\u2013camera Calibration Method Based on Ranging Statistical Characteristics and Improved RANSAC Algorithm","volume":"141","author":"Xu","year":"2021","journal-title":"Robot. Auton. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2122","DOI":"10.1364\/OE.381176","article-title":"Geometric Calibration for LiDAR-camera System Fusing 3D-2D and 3D-3D Point Correspondences","volume":"28","author":"An","year":"2020","journal-title":"Opt. Express"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ye, Q., Shu, L., and Zhang, W. (2019, January 4\u20137). Extrinsic Calibration of a Monocular Camera and a Single Line Scanning LiDAR. Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China.","DOI":"10.1109\/ICMA.2019.8816641"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liao, Q., Chen, Z., Liu, Y., Wang, Z., and Liu, M. (2018, January 12\u201315). Extrinsic Calibration of Lidar and Camera with Polygon. Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ROBIO.2018.8665256"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yao, Y., Huang, X., and Lv, J. (2021, January 11\u201313). A Space Joint Calibration method for LiDAR and Camera on Self-driving Car and Its Experimental Verification. Proceedings of the 6th International Symposium on Computer and Information Processing Technology (ISCIPT), Changsha, China.","DOI":"10.1109\/ISCIPT53667.2021.00084"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"134101","DOI":"10.1109\/ACCESS.2020.3010734","article-title":"Improvements to Target-Based 3D LiDAR to Camera Calibration","volume":"8","author":"Huang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, J., Wang, S., Ghaffari, M., and Grizzle, J. (2021, January 31). LiDARTag: A Real-Time Fiducial Tag System for Point Clouds. Proceedings of the IEEE Robot and Automation Letters.","DOI":"10.1109\/LRA.2021.3070302"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, W., Sakurada, K., and Kawaguchi, N. (2017). Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard. Remote Sens., 9.","DOI":"10.3390\/rs9080851"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1002\/rob.21542","article-title":"Automatic Extrinsic Calibration of Vision and LiDAR by Maximizing Mutual Information","volume":"32","author":"Pandey","year":"2014","journal-title":"J. Field Robot."},{"key":"ref_28","unstructured":"Taylor, Z., and Nieto, J. (2012, January 3). A Mutual Information Approach to Automatic Calibration of Camera and LiDAR in Natural Environments. Proceedings of the Australasian Conference on Robotics and Automation (ACRA), Victoria University of Wellington, Wellington, New Zealand."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1109\/TRO.2016.2596771","article-title":"Motion-based Calibration of Multimodal Sensor Extrinsics and Timing Offset Estimation","volume":"32","author":"Taylor","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Taylor, Z., and Nieto, J. (2015, January 26\u201330). Motion-based Calibration of Multimodal Sensor Arrays. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139872"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Schneider, N., Piewak, F., Stiller, C., and Franke, U. (2017, January 11\u201314). RegNet: Multimodal Sensor Registration Using Deep Neural Networks. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995968"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Iyer, G., Ram, R., Murthy, J., and Krishna, K. (2018, January 1\u20135). CalibNet: Geometrically Supervised Extrinsic Calibration Using 3D Spatial Transformer Networks. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593693"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6956","DOI":"10.1109\/LRA.2020.3026958","article-title":"RGGNet: Tolerance Aware LiDAR-Camera Online Calibration with Geometric Deep Learning and Generative Model","volume":"5","author":"Yuan","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lv, X., Wang, B., Dou, Z., Ye, D., and Wang, S. (2021, January 19\u201325). LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00324"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rotter, P., Klemiato, M., and Skruch, P. (2022). Automatic Calibration of a LiDAR\u2013Camera System Based on Instance Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14112531"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6458","DOI":"10.1109\/LRA.2021.3093009","article-title":"Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor","volume":"6","author":"Lim","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Giyenko, A., and Cho, Y. (2016, January 16\u201319). Intelligent UAV in Smart Cities Using IoT. Proceedings of the 16th international Conference on Control,Automation and Systems (ICCAS\u201916), Gyeongju, Korea.","DOI":"10.1109\/ICCAS.2016.7832322"},{"key":"ref_38","unstructured":"Unnikrishnan, R., and Hebert, M. (2005). Fast Extrinsic Calibration of a Laser Rangefinder to a Camera, Robotics Institute. Technical Report, CMU-RI-TR-05-09."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11263-008-0152-6","article-title":"EPnP: An Accurate O(n) Solution to the PnP Problem","volume":"81","author":"Lepetit","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kneip, L., Li, H., and Seo, Y. (2014, January 6\u201312). UPnP: An optimal O(n) Solution to The Absolute Pose Problem with Universal Applicability. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_9"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Grammatikopoulos, L., Papanagnou, A., Venianakis, A., Kalisperakis, I., and Stentoumis, C. (2022). An Effective Camera-to-LiDAR Spatiotemporal Calibration Based on a Simple Calibration Target. Sensors, 22.","DOI":"10.3390\/s22155576"},{"key":"ref_42","unstructured":"Nnez, P., Jr, P.D., Rocha, R., and Dias, J. (2009, January 23\u201325). Data Fusion Calibration for a 3D Laser Range Finder and a Camera Using Inertial Data. Proceedings of the European Conference on Mobile Robots (ECMR), Dubrovnik, Croatia."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kim, E., and Park, S. (2020). Extrinsic Calibration between Camera and LiDAR Sensors by Matching Multiple 3D Planes. Sensors, 20.","DOI":"10.3390\/s20010052"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chai, Z., Sun, Y., and Xiang, Z. (2018, January 9\u201312). A Novel Method for LiDAR Camera Calibration by Plane Fitting. Proceedings of the 2018 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Auckland, New Zealand.","DOI":"10.1109\/AIM.2018.8452339"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6385\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:08Z","timestamp":1760146988000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,17]]},"references-count":44,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246385"],"URL":"https:\/\/doi.org\/10.3390\/rs14246385","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,17]]}}}