4D radar has emerged as a critical sensor for autonomous driving, primarily due to its enhanced capabilities in elevation measurement and higher resolution compared to traditional 3D radar. Effective integration of 4D radar with cameras requires accurate extrinsic calibration, and the development of radar-based perception algorithms demands large-scale annotated datasets. However, existing calibration methods often employ separate targets optimized for either visual or radar modalities, complicating correspondence establishment. Furthermore, manually labeling sparse radar data is labor-intensive and unreliable. To address these challenges, we propose 4D-CAAL, a unified framework for 4D radar-camera calibration and auto-labeling. Our approach introduces a novel dual-purpose calibration target design, integrating a checkerboard pattern on the front surface for camera detection and a corner reflector at the center of the back surface for radar detection. We develop a robust correspondence matching algorithm that aligns the checkerboard center with the strongest radar reflection point, enabling accurate extrinsic calibration. Subsequently, we present an auto-labeling pipeline that leverages the calibrated sensor relationship to transfer annotations from camera-based segmentations to radar point clouds through geometric projection and multi-feature optimization. Extensive experiments demonstrate that our method achieves high calibration accuracy while significantly reducing manual annotation effort, thereby accelerating the development of robust multi-modal perception systems for autonomous driving.

翻译：4D雷达因其相较于传统3D雷达在俯仰角测量和更高分辨率方面的增强能力，已成为自动驾驶的关键传感器。要实现4D雷达与相机的有效融合，需要精确的外参标定；而开发基于雷达的感知算法则需要大规模标注数据集。然而，现有标定方法通常采用为视觉或雷达模态单独优化的标定靶，这增加了对应关系建立的复杂性。此外，手动标注稀疏的雷达数据既费力又不可靠。为应对这些挑战，我们提出了4D-CAAL——一个用于4D雷达-相机标定与自动标注的统一框架。我们的方法引入了一种新颖的双用途标定靶设计：前表面集成棋盘格图案用于相机检测，后表面中心放置角反射器用于雷达检测。我们开发了一种鲁棒的对应关系匹配算法，将棋盘格中心与最强的雷达反射点对齐，从而实现精确的外参标定。随后，我们提出了一种自动标注流程，该流程利用已标定的传感器关系，通过几何投影和多特征优化，将基于相机的分割标注迁移到雷达点云上。大量实验表明，我们的方法在实现高标定精度的同时，显著减少了人工标注工作量，从而加速了面向自动驾驶的鲁棒多模态感知系统的开发。