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arXiv · 2507.19829

A 4D Radar Camera Extrinsic Calibration Tool Based on 3D Uncertainty Perspective N Points

Chuan Cao·Xiaoning Wang·Wenqian Xi·Han Zhang·Weidong Chen·Jingchuan Wang
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Abstract

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.

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Chuan Cao, Xiaoning Wang, Wenqian Xi, Han Zhang, Weidong Chen, Jingchuan Wang. 2025-07-26. A 4D Radar Camera Extrinsic Calibration Tool Based on 3D Uncertainty Perspective N Points. https://arxiv.org/abs/2507.19829

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