Calibration for the Integration of 2D LiDAR and Camera Data

Aiming at the shortcomings of slow calibration speed and poor real-time performance between single-line LiDAR and camera extrinsics, a data fusion calibration method based on feature points was proposed. The pixel feature points required for camera external parameter calibration were extracted from a specially designed target, and then the necessary point cloud feature points were obtained by LiDAR angle calibration.The pixel feature points and point cloud feature points were jointly calibrated by the feature point method to obtain the coordinate sequence corresponding to the LiDAR data and the camera image pixel points. The two types of coordinate sequences were fused to calculate the fusion matrix. The feasibility and effectiveness of the proposed method were verified by calibration experiments. The results show that compared to traditional calibration targets, compared to traditional calibration targets. Combining the data fusion processing method, the calibration parameters of single-line LiDAR and camera can be obtained. The average reprojection error of data fusion is 1.9 pixel, with a standard deviation of 0.7 pixels. The LiDAR point cloud data can be projected well on the calibration target, which can successfully match the LiDAR point cloud with the target image. By the fusion matrix, fewer corresponding LiDAR point clouds and pixels are required, which can avoid the calculation of individual calibration parameters, thus making the calibration process simpler and faster.