LiDAR and camera are two of the most common sensors used in the fields of robot perception, autonomous driving, augmented reality, and virtual reality, where these sensors are widely used to perform various tasks such as odometry estimation and 3D reconstruction. Fusing the information from these two sensors can significantly increase the robustness and accuracy of these perception tasks. The extrinsic calibration between cameras and LiDAR is a fundamental prerequisite for multimodal systems. Recently, extensive studies have been conducted on the calibration of extrinsic parameters. Although several calibration methods facilitate sensor fusion, a comprehensive summary for researchers and, especially, non-expert users is lacking. Thus, we present an overview of extrinsic calibration and discuss diverse calibration methods from the perspective of calibration system design. Based on the calibration information sources, this study classifies these methods as target-based or targetless. For each type of calibration method, further classification was performed according to the diverse types of features or constraints used in the calibration process, and their detailed implementations and key characteristics were introduced. Thereafter, calibration-accuracy evaluation methods are presented. Finally, we comprehensively compare the advantages and disadvantages of each calibration method and suggest directions for practical applications and future research.
Wang et al. (Sun,) studied this question.