Camera–LiDAR Calibration Using Iterative Random Sampling and Intersection Line-Based Quality Evaluation

Abstract

This paper proposes a novel camera–LiDAR calibration method that utilizes an iterative random sampling and intersection line-based quality evaluation using a foldable plane pair. Firstly, this paper suggests using a calibration object consisting of two small planes with ChArUco patterns, which is easy to make and convenient to carry. Secondly, the proposed method adopts an iterative random sampling to make the calibration procedure robust against sensor data noise and incorrect object recognition. Lastly, this paper proposes a novel quality evaluation method based on the dissimilarity between two intersection lines of the plane pairs from the two sensors. Thus, the proposed method repeats random sampling of sensor data, extrinsic parameter estimation, and quality evaluation of the estimation result in order to determine the most appropriate calibration result. Furthermore, this method can also be used for the LiDAR–LiDAR calibration with a slight modification. In experiments, the proposed method was quantitively evaluated using simulation data and qualitatively assessed using real-world data. The experimental results show that the proposed method can successfully perform both camera–LiDAR and LiDAR–LiDAR calibrations while outperforming the previous approaches.