FusionCalib: Automatic extrinsic parameters calibration based on road plane reconstruction for roadside integrated radar camera fusion sensors

Abstract

Calibrating the relative positional posture of the roadside sensor with respect to the road is crucial for target tracking and sensor fusion. However, most previous studies rely on prior information or require additional manual measurements, which increases calibration error and labor costs. This paper proposes an automatic extrinsic parameters calibration method for roadside integrated radar camera fusion sensors for the first time. In particular, we leverage the radar camera fusion framework to automatically reconstruct the road plane. Specifically, to integrate sensing abilities, a stable bidirectional selection association method is adopted to match radar and camera trajectories. Based on the association results, the Extended Kalman Filter (EKF) is adopted to fuse pixel positions and three-dimensional (3D) velocities of vehicles, and then a series of 3D positions of vehicle bottoms are obtained to reconstruct the road plane. Experimental results show that our proposed method achieves automatic and accurate calibration of extrinsic parameters.