Advances in autonomous driving are inseparable from sensor fusion. Heterogeneous sensors are widely used for sensor fusion due to their complementary properties, with radar and camera being the most equipped sensors. Intrinsic and extrinsic calibration are essential steps in sensor fusion. The extrinsic calibration, independent of the sensor's own parameters, and performed after the sensors are installed, greatly determines the accuracy of sensor fusion. Many target-based methods require cumbersome operating procedures and well-designed experimental conditions, making them extremely challenging. To this end, we propose a flexible, easy-to-reproduce and accurate method for extrinsic calibration of 3D radar and camera. The proposed method does not require a specially designed calibration environment, and instead places a single corner reflector (CR) on the ground to iteratively collect radar and camera data simultaneously using Robot Operating System (ROS), and obtain radar-camera point correspondences based on their timestamps, and then use these point correspondences as input to solve the perspective-n-point (PnP) problem, and finally get the extrinsic calibration matrix. Also, RANSAC is used for robustness and the Levenberg-Marquardt (LM) nonlinear optimization algorithm is used for accuracy. Multiple controlled environment experiments as well as real-world experiments demonstrate the efficiency and accuracy (AED error is 15.31 pixels and Acc up to 89\%) of the proposed method.

翻译：自动驾驶技术的进步离不开传感器融合。异构传感器因其互补特性被广泛应用于传感器融合，其中雷达与相机是最常见的传感器配置。内参标定与外参标定是传感器融合的关键步骤。外参标定独立于传感器自身参数，在传感器安装后执行，其准确性极大决定了传感器融合的精度。现有多种基于目标的标定方法需要繁琐的操作流程和精心设计的实验条件，实施难度极大。为此，我们提出一种灵活、易于复现且精确的3D雷达与相机外参标定方法。该方法无需专门设计的标定环境，只需将单个角反射器放置于地面，通过机器人操作系统（ROS）同步采集雷达与相机数据，基于时间戳获取雷达-相机点对应关系，并将这些点对应作为输入求解透视n点（PnP）问题，最终得到外参标定矩阵。同时，采用RANSAC算法增强鲁棒性，并利用Levenberg-Marquardt（LM）非线性优化算法提升精度。多组受控环境实验及真实场景实验验证了所提方法的效率与准确性（平均欧氏距离误差为15.31像素，精确度高达89%）。