Extrinsic calibration between a stereoscopic system and a LIDAR with sensor noise models

Visual sensors and depth sensors, such as camera and LIDAR (Light Detection and Ranging) are more and more used together in current perception systems of intelligent vehicles. Fusing information obtained separately from these heterogeneous sensors always requires extrinsic calibration of vision sensors and LIDARs. In this paper, we propose an optimal extrinsic calibration algorithm between a binocular stereo vision system and a 2D LIDAR. The extrinsic calibration problem is solved by 3D reconstruction of a chessboard and geometric constraints between the views from the stereovision system and the LIDAR. The proposed approach takes sensor noise models into account that it provides optimal results under Mahalanobis distance constraints. Experiments based on both computer simulation and real data sets are presented and analyzed to evaluate the performance of the calibration method. A comparison with a popular camera/LIDAR calibration method is also proposed to show the benefits of our method.