While automotive radar sensors are widely adopted and have been used for automatic cruise control and collision avoidance tasks, their application outside of vehicles is still limited. As they have the ability to resolve multiple targets in 3D space, radars can also be used for improving environment perception. This application, however, requires a precise calibration, which is usually a time-consuming and labor-intensive task. We, therefore, present an approach for automated and geo-referenced extrinsic calibration of automotive radar sensors that is based on a novel hypothesis filtering scheme. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. This location data is then combined with filtered sensor data to create calibration hypotheses. Subsequent filtering and optimization recovers the correct calibration. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner, thus enabling cooperative driving scenarios.

翻译：尽管车载雷达传感器已广泛应用于自适应巡航控制和碰撞避免任务，但其在车辆外部的应用仍然有限。由于雷达具备在三维空间中分辨多个目标的能力，因此也可用于提升环境感知性能。然而，这一应用需要精确的标定，这通常是一项耗时且劳动密集型的工作。为此，我们提出了一种基于新型假设滤波方案的自动化地理参考车载雷达外参标定方法。我们的方法无需对车辆进行外部改造，而是利用从自动驾驶车辆获取的定位数据。该定位数据随后与经过滤波的传感器数据相结合，生成标定假设。进一步通过滤波与优化，即可恢复正确的标定参数。基于真实测试场数据的评估表明，我们的方法能够自动正确标定基础设施传感器，从而支持协同驾驶场景。