Traditional radar-camera calibration requires manual intervention and excessive computational resources, resulting in high labor costs for maintenance in roadside perception scenarios. Thus, we propose a continuous online calibration method for roadside integrated radar-camera device. The method is based on azimuth angle and multi frame tracking. Firstly, the radar-camera corresponding points are matched by the target azimuth angle and its rate of change, thus achieving coarse calibration. It doesn't need manual roadside parameter measurement, only need the camera intrinsic parameters obtained in the laboratory. Secondly, the Hungarian tracking algorithm is used to match camera-radar point pairs with over a larger range and the fine calibration matrix is obtained. Additionally, the validation criterion is established, which ensures the fine calibration can operate continuously and timely adjust when the device pose changes. To verify the efficiency of the proposed method, the real roadside experiments are conducted in the traffic-dense scenario. The results show that the purposed method reduces the reprojection error by 25% comparing to manual calibration, by 55% comparing to other automatic calibration method. This approach significantly enhances calibration accuracy and robustness in complex environments, it can provide reliable technical support for intelligent transportation systems.
Zhu et al. (Fri,) studied this question.