In the context of autonomous driving, single sensors have the disadvantage of incomplete data and information, so accurate calibration of multiple sensors has gradually become an important foundation for later work. This article systematically reviews the joint calibration technology of LiDAR and camera. Based on a brief introduction to the internal parameter calibration methods of LiDAR and camera, the main methods of joint calibration are analyzed, including target based calibration methods and target free calibration methods. The target based calibration method has the characteristics of high accuracy and strong reliability, but it relies on the production of calibration objects and manual feature extraction; Objective free calibration methods have higher levels of automation and development potential, among which feature-based, information theory based, motion based, and deep learning based calibration methods each have their own advantages and limitations. By comparing and analyzing the principles, characteristics, and applicable scenarios of various methods, this article aims to provide technical selection references for researchers in related fields, and hopes to combine the advantages of multiple methods in the future to further improve the performance and practicality of LiDAR camera joint calibration technology, in order to meet the strict requirements of environmental perception accuracy and efficiency in applications such as autonomous driving.