Autonomous driving multi-sensor fusion technology has become a key factor in enhancing system perception capabilities and robustness. As two complementary sensors, LiDAR and cameras rely on precise joint calibration-determining their relative pose relationship-to achieve data fusion. This paper analyzes LiDAR-camera joint calibration methods based on calibration boards, categorizing them into three main types according to calibration board types and feature extraction approaches: 2D plane-based, 3D stereo-based, and reconfigurable hybrid calibration boards. It elaborates on their principles, typical methods, and performance characteristics. Finally, a comparative analysis is conducted regarding accuracy, robustness, and automation levels, summarizing the current research challenges.