A 4D Radar Camera Extrinsic Calibration Tool Based on 3D Uncertainty Perspective N Points
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915410979323904 |
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| author | Cao, Chuan Wang, Xiaoning Xi, Wenqian Zhang, Han Chen, Weidong Wang, Jingchuan |
| author_facet | Cao, Chuan Wang, Xiaoning Xi, Wenqian Zhang, Han Chen, Weidong Wang, Jingchuan |
| contents | 4D imaging radar is a type of low-cost millimeter-wave radar(costing merely 10-20$\%$ of lidar systems) capable of providing range, azimuth, elevation, and Doppler velocity information. Accurate extrinsic calibration between millimeter-wave radar and camera systems is critical for robust multimodal perception in robotics, yet remains challenging due to inherent sensor noise characteristics and complex error propagation. This paper presents a systematic calibration framework to address critical challenges through a spatial 3d uncertainty-aware PnP algorithm (3DUPnP) that explicitly models spherical coordinate noise propagation in radar measurements, then compensating for non-zero error expectations during coordinate transformations. Finally, experimental validation demonstrates significant performance improvements over state-of-the-art CPnP baseline, including improved consistency in simulations and enhanced precision in physical experiments. This study provides a robust calibration solution for robotic systems equipped with millimeter-wave radar and cameras, tailored specifically for autonomous driving and robotic perception applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19829 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A 4D Radar Camera Extrinsic Calibration Tool Based on 3D Uncertainty Perspective N Points Cao, Chuan Wang, Xiaoning Xi, Wenqian Zhang, Han Chen, Weidong Wang, Jingchuan Robotics 4D imaging radar is a type of low-cost millimeter-wave radar(costing merely 10-20$\%$ of lidar systems) capable of providing range, azimuth, elevation, and Doppler velocity information. Accurate extrinsic calibration between millimeter-wave radar and camera systems is critical for robust multimodal perception in robotics, yet remains challenging due to inherent sensor noise characteristics and complex error propagation. This paper presents a systematic calibration framework to address critical challenges through a spatial 3d uncertainty-aware PnP algorithm (3DUPnP) that explicitly models spherical coordinate noise propagation in radar measurements, then compensating for non-zero error expectations during coordinate transformations. Finally, experimental validation demonstrates significant performance improvements over state-of-the-art CPnP baseline, including improved consistency in simulations and enhanced precision in physical experiments. This study provides a robust calibration solution for robotic systems equipped with millimeter-wave radar and cameras, tailored specifically for autonomous driving and robotic perception applications. |
| title | A 4D Radar Camera Extrinsic Calibration Tool Based on 3D Uncertainty Perspective N Points |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.19829 |