A Joint LiDAR and Camera Calibration Algorithm Based on an Original 3D Calibration Plate
An accurate extrinsic calibration between LiDAR and cameras is essential for effective sensor fusion, directly impacting the perception capabilities of autonomous driving systems. Although prior calibration approaches using planar and point features have yielded some success, they suffer from inhere...
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| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4558 |
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| author | Ziyang Cui Yi Wang Xiaodong Chen Huaiyu Cai |
| author_facet | Ziyang Cui Yi Wang Xiaodong Chen Huaiyu Cai |
| author_sort | Ziyang Cui |
| collection | DOAJ |
| description | An accurate extrinsic calibration between LiDAR and cameras is essential for effective sensor fusion, directly impacting the perception capabilities of autonomous driving systems. Although prior calibration approaches using planar and point features have yielded some success, they suffer from inherent limitations. Specifically, methods that rely on fitting planar contours using depth-discontinuous points are prone to systematic errors, which hinder the precise extraction of the 3D positions of feature points. This, in turn, compromises the accuracy and robustness of the calibration. To overcome these challenges, this paper introduces a novel 3D calibration plate incorporating the gradient depth, localization markers, and corner features. At the point cloud level, the gradient depth enables the accurate estimation of the 3D coordinates of feature points. At the image level, corner features and localization markers facilitate the rapid and precise acquisition of 2D pixel coordinates, with minimal interference from environmental noise. This method establishes a rigorous and systematic framework to enhance the accuracy of LiDAR–camera extrinsic calibrations. In a simulated environment, experimental results demonstrate that the proposed algorithm achieves a rotation error below 0.002 radians and a translation error below 0.005 m. |
| format | Article |
| id | doaj-art-a811b399a723432bb452749c2ffdd31d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a811b399a723432bb452749c2ffdd31d2025-08-20T03:36:33ZengMDPI AGSensors1424-82202025-07-012515455810.3390/s25154558A Joint LiDAR and Camera Calibration Algorithm Based on an Original 3D Calibration PlateZiyang Cui0Yi Wang1Xiaodong Chen2Huaiyu Cai3Key Laboratory of Opto-Electronics Information Technology of Ministry of Education, College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Opto-Electronics Information Technology of Ministry of Education, College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Opto-Electronics Information Technology of Ministry of Education, College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, ChinaKey Laboratory of Opto-Electronics Information Technology of Ministry of Education, College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, ChinaAn accurate extrinsic calibration between LiDAR and cameras is essential for effective sensor fusion, directly impacting the perception capabilities of autonomous driving systems. Although prior calibration approaches using planar and point features have yielded some success, they suffer from inherent limitations. Specifically, methods that rely on fitting planar contours using depth-discontinuous points are prone to systematic errors, which hinder the precise extraction of the 3D positions of feature points. This, in turn, compromises the accuracy and robustness of the calibration. To overcome these challenges, this paper introduces a novel 3D calibration plate incorporating the gradient depth, localization markers, and corner features. At the point cloud level, the gradient depth enables the accurate estimation of the 3D coordinates of feature points. At the image level, corner features and localization markers facilitate the rapid and precise acquisition of 2D pixel coordinates, with minimal interference from environmental noise. This method establishes a rigorous and systematic framework to enhance the accuracy of LiDAR–camera extrinsic calibrations. In a simulated environment, experimental results demonstrate that the proposed algorithm achieves a rotation error below 0.002 radians and a translation error below 0.005 m.https://www.mdpi.com/1424-8220/25/15/4558calibrationcamera and LiDARfeature pointintelligent vehicle |
| spellingShingle | Ziyang Cui Yi Wang Xiaodong Chen Huaiyu Cai A Joint LiDAR and Camera Calibration Algorithm Based on an Original 3D Calibration Plate Sensors calibration camera and LiDAR feature point intelligent vehicle |
| title | A Joint LiDAR and Camera Calibration Algorithm Based on an Original 3D Calibration Plate |
| title_full | A Joint LiDAR and Camera Calibration Algorithm Based on an Original 3D Calibration Plate |
| title_fullStr | A Joint LiDAR and Camera Calibration Algorithm Based on an Original 3D Calibration Plate |
| title_full_unstemmed | A Joint LiDAR and Camera Calibration Algorithm Based on an Original 3D Calibration Plate |
| title_short | A Joint LiDAR and Camera Calibration Algorithm Based on an Original 3D Calibration Plate |
| title_sort | joint lidar and camera calibration algorithm based on an original 3d calibration plate |
| topic | calibration camera and LiDAR feature point intelligent vehicle |
| url | https://www.mdpi.com/1424-8220/25/15/4558 |
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