自动驾驶多传感器融合技术已成为提升系统感知能力与鲁棒性的关键。激光雷达与相机作为两种优势互补的传感器，其数据融合依赖于精确的联合标定，即确定两者之间的相对位置关系。文章分析了基于标定板的激光雷达与相机联合标定方法，依据标定板类型与特征提取方式，将其分为基于二维平面、基于三维立体和基于可重构混合标定板三大类，详述其原理、典型方法与性能特点。最后，对比分析不同方法在精度、鲁棒性、自动化程度等方面的优劣，总结当前研究存在的挑战。

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.