Extrinsic calibration of a camera and a lidar based on decoupling the rotation from the translation

In this paper, we propose a novel robust algorithm for the extrinsic calibration of a camera and a lidar. This algorithm utilizes checkerboard as a calibration object. Since the interaction between the estimation errors of the plane parameters obtained from checkerboard images downgrades the quality of extrinsic calibration results, a new geometric constraint is presented to decouple the rotation from the translation so as to reduce the effect of such an interaction. Weights that represent uncertainty of the unit normal vector to the checkerboard plane are introduced to totally evaluate the quality of each pair of image and lidar scan. Furthermore, we analyze the configuration of checkerboard pose and give a formula that is used to assess the configuration. We compare the proposed algorithm with the previous ones. Simulation and experimental results show that our algorithm is able to achieve more accurate extrinsic parameters than the existing algorithms. Meanwhile, we also design experiments to validate the effectiveness and efficiency of the presented weight and the assessment formula.