Line Feature Based Extrinsic Calibration of LiDAR and Camera

Abstract

Reliable extrinsic calibration is a crucial first step for multi-sensor data fusion, which is the key part of the autonomous vehicle to perceive the environment carefully and effectively. In this paper, we propose an effective extrinsic calibration pipeline to establish the transformation between camera and LiDAR and update the decalibration online on an autonomous driving platform. We obtain rotation extrinsic parameters using parallel lines features in road scene, and infer translation extrinsic parameters by an online search approach based on selective edge alignment of point cloud and image. In order to evaluate our calibration system, it is first validated on KITTI benchmark and compared with the baseline algorithm. After that, the proposed method is tested on our own data. The results show that our method has a better rotation accuracy and demonstrate the necessity of error correction online.