解决雷达与相机间外参标定难题，提升自动驾驶感知精度
A 4D Radar Camera Extrinsic Calibration Tool Based on 3D Uncertainty Perspective N Points
- 基于3D不确定性视角的PnP算法，显式建模雷达测量噪声传播
- 在仿真和实测中均显著优于现有最优方法，提升标定一致性与精度
- 适合自动驾驶、机器人感知等需融合雷达与视觉的场景
4D成像雷达是一种低成本毫米波雷达（成本仅为激光雷达的10%-20%），可提供距离、方位角、俯仰角和多普勒速度信息。雷达与相机系统间的精确外参标定对机器人鲁棒多模态感知至关重要，但因传感器固有噪声特性和复杂误差传播而极具挑战。本文提出一种系统性标定框架，通过空间三维不确定性感知的PnP算法（3DUPnP），显式建模雷达测量中球坐标系下的噪声传播，并在坐标变换过程中补偿非零误差期望。实验验证表明，该方法在仿真中显著提升一致性，在物理实验中增强精度，优于当前最优的CPnP基准。本研究为配备毫米波雷达与相机的机器人系统提供了可靠标定解决方案，尤其适用于自动驾驶与机器人感知应用。
原文摘要 · Abstract (English)
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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