Calibration between a panoramic LiDAR and a limited field-of-view depth camera
Abstract Depth cameras and LiDARs are commonly used sensing devices widely applied in fields such as autonomous driving, navigation, and robotics. Precise calibration between the two is crucial for accurate environmental perception and localization. Methods that utilize the point cloud features of b...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01710-x |
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| _version_ | 1832571188451213312 |
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| author | Weijie Tang Bin Wang Longxiang Huang Xu Yang Qian Zhang Sulei Zhu Yan Ma |
| author_facet | Weijie Tang Bin Wang Longxiang Huang Xu Yang Qian Zhang Sulei Zhu Yan Ma |
| author_sort | Weijie Tang |
| collection | DOAJ |
| description | Abstract Depth cameras and LiDARs are commonly used sensing devices widely applied in fields such as autonomous driving, navigation, and robotics. Precise calibration between the two is crucial for accurate environmental perception and localization. Methods that utilize the point cloud features of both sensors to estimate extrinsic parameters can also be extended to calibrate limited Field-of-View (FOV) LiDARs and panoramic LiDARs, which holds significant research value. However, calibrating the point clouds from two sensors with different fields of view and densities presents challenges. This paper proposes methods for automatic calibration of the two sensors by extracting and registering features in three scenarios: environments with one plane, two planes, and three planes. For the one-plane and two-plane scenarios, we propose constructing feature histogram descriptors based on plane constraints for the remaining points, in addition to planar features, for registration. Experimental results on simulation and real-world data demonstrate that the proposed methods in all three scenarios achieve precise calibration, maintaining average rotation and translation calibration errors within 2 degrees and 0.05 meters respectively for a $$360^{\circ }$$ 360 ∘ linear LiDAR and a depth camera with a field of view of $$100^{\circ }$$ 100 ∘ vertically and $$70^{\circ }$$ 70 ∘ degrees horizontally. |
| format | Article |
| id | doaj-art-a1ca524969384d3397a503e52e054ed5 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-a1ca524969384d3397a503e52e054ed52025-02-02T12:50:08ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111111610.1007/s40747-024-01710-xCalibration between a panoramic LiDAR and a limited field-of-view depth cameraWeijie Tang0Bin Wang1Longxiang Huang2Xu Yang3Qian Zhang4Sulei Zhu5Yan Ma6The College of Information, Mechanical, and Electrical Engineering, Shanghai Normal UniversityThe College of Information, Mechanical, and Electrical Engineering, Shanghai Normal UniversityShenzhen Guangjian Technology Co., LtdShenzhen Guangjian Technology Co., LtdThe College of Information, Mechanical, and Electrical Engineering, Shanghai Normal UniversityThe College of Information, Mechanical, and Electrical Engineering, Shanghai Normal UniversityThe College of Information, Mechanical, and Electrical Engineering, Shanghai Normal UniversityAbstract Depth cameras and LiDARs are commonly used sensing devices widely applied in fields such as autonomous driving, navigation, and robotics. Precise calibration between the two is crucial for accurate environmental perception and localization. Methods that utilize the point cloud features of both sensors to estimate extrinsic parameters can also be extended to calibrate limited Field-of-View (FOV) LiDARs and panoramic LiDARs, which holds significant research value. However, calibrating the point clouds from two sensors with different fields of view and densities presents challenges. This paper proposes methods for automatic calibration of the two sensors by extracting and registering features in three scenarios: environments with one plane, two planes, and three planes. For the one-plane and two-plane scenarios, we propose constructing feature histogram descriptors based on plane constraints for the remaining points, in addition to planar features, for registration. Experimental results on simulation and real-world data demonstrate that the proposed methods in all three scenarios achieve precise calibration, maintaining average rotation and translation calibration errors within 2 degrees and 0.05 meters respectively for a $$360^{\circ }$$ 360 ∘ linear LiDAR and a depth camera with a field of view of $$100^{\circ }$$ 100 ∘ vertically and $$70^{\circ }$$ 70 ∘ degrees horizontally.https://doi.org/10.1007/s40747-024-01710-xCalibrationLiDARDepth cameraThree scenariosFeature histogram descriptor |
| spellingShingle | Weijie Tang Bin Wang Longxiang Huang Xu Yang Qian Zhang Sulei Zhu Yan Ma Calibration between a panoramic LiDAR and a limited field-of-view depth camera Complex & Intelligent Systems Calibration LiDAR Depth camera Three scenarios Feature histogram descriptor |
| title | Calibration between a panoramic LiDAR and a limited field-of-view depth camera |
| title_full | Calibration between a panoramic LiDAR and a limited field-of-view depth camera |
| title_fullStr | Calibration between a panoramic LiDAR and a limited field-of-view depth camera |
| title_full_unstemmed | Calibration between a panoramic LiDAR and a limited field-of-view depth camera |
| title_short | Calibration between a panoramic LiDAR and a limited field-of-view depth camera |
| title_sort | calibration between a panoramic lidar and a limited field of view depth camera |
| topic | Calibration LiDAR Depth camera Three scenarios Feature histogram descriptor |
| url | https://doi.org/10.1007/s40747-024-01710-x |
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