A 4D Radar Camera Extrinsic Calibration Tool Based on 3D Uncertainty Perspective N Points
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.
Cite
@article{arxiv.2507.19829,
title = {A 4D Radar Camera Extrinsic Calibration Tool Based on 3D Uncertainty Perspective N Points},
author = {Chuan Cao and Xiaoning Wang and Wenqian Xi and Han Zhang and Weidong Chen and Jingchuan Wang},
journal= {arXiv preprint arXiv:2507.19829},
year = {2025}
}