Trajectory-Driven Automatic Extrinsic Calibration for Roadside Radar-Camera Fusion

The integration of cameras and radar has gained significant attention in the context of roadside perception. However, existing calibration techniques face challenges when applied to roadside infrastructures due to unstable feature acquisition and large computational overhead. In this article, we propose an automatic calibration framework for camera-radar extrinsic calibration. The core of this framework lies in leveraging trajectory events from sensor measurement to eliminate modality disparity and achieve stable target representation at a low cost. By decomposing single events into multiple base trajectory behaviors, the extracted radar and camera events are associated with a multistage event association pipeline. Moreover, we use the proposed calibration framework to construct an automatic calibration system that can be integrated into prevalent roadside perception systems. The calibration system continuously monitors the spatial relationship between sensors and quickly eliminates any possible de-calibrations, which greatly reduces re-calibration costs. The experiments conducted on real-world scenarios in Beijing North Third Ring Middle Road have demonstrated the feasibility and quick response of the system, as well as an accuracy leap that surpasses other methods in camera-radar calibration.