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

TL;DR

This paper introduces a novel 3D uncertainty-aware PnP calibration method for 4D radar-camera systems, improving accuracy and robustness in multimodal perception for robotics.

Contribution

It proposes a spatial 3D uncertainty-aware PnP algorithm that explicitly models radar measurement noise, enhancing calibration precision over existing methods.

Findings

Significant performance improvements over baseline in simulations.

Enhanced calibration accuracy in physical experiments.

Improved consistency and robustness in sensor fusion.

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.