Research on the Calibration Method of Laser Radar and Camera Extrinsic Fusion based on Checkerboard Features

Abstract

The external parameter calibration of LiDAR and camera is the core link of multimodal perception fusion technology, and its accuracy directly determines the effectiveness of cross modal information fusion. This article proposes a method for high-precision calibration of laser radar and camera extrinsic parameters using a checkerboard calibration board based on spatial registration theory. In response to the difficulty of feature extraction caused by the sparsity of LiDAR point clouds, this method enhances feature information by accumulating multi view point clouds and combines filtering algorithms to improve feature saliency; A standardized experimental platform was built based on ROS and Matlab, utilizing the reflection intensity distribution pattern of the checkerboard point cloud to achieve accurate extraction of its three-dimensional corner coordinates. At the same time, the corresponding two-dimensional checkerboard corner points are detected in the camera image, and the relative pose transformation matrix between the LiDAR and the camera is solved by establishing an accurate matching relationship between the two-dimensional and three-dimensional corner points. The experimental results show that this method has high-precision calibration results and good repeatability, which can provide a reliable extrinsic basis for multi-sensor fusion perception systems.