一种鲁棒的2D激光雷达和摄像机最小解标定方法

Robust minimum solution calibration method of 2D Lidar and camera

因为P3P问题固有的结构缺陷,二维激光雷达和摄像机的最小解标定方法存在数值稳定性差、精度较差等问题。针对上述问题,本文提出了一种鲁棒的最小解标定方法,对P3P问题的求解算法和最优解选择误差度量进行改进。首先,根据3个棋盘格构建的P3P问题,利用改进的RP3P算法进行求解,提高了解的稳定性。然后,基于可信度加权观测概率设计了一种的最优解选择策略,提高了所选解的准确性。根据实验结果可知,本文的算法在标定结果的精度和有效性上得到明显改善。仿真实验中,在不同噪声水平下,相比于Francisco方法和Hu方法,本文方法的有效解概率提高了5%~41%和2%~20%,旋转矩阵精度提高了2°~6°和1.5°~2°,平移向量精度提高了180~520 mm和150~180 mm,性能提高明显。

Because of the inherent structural defects of P3P problem,the minimum solution method for calibration of 2D Lidar and camera has some shortcoming,such as poor numerical stability and accuracy.Aiming at the above problem,this paper proposes a robust minimum solution calibration method,which improves algorithm of P3P problem and the error measurement of optimal solution selection.Firstly,according to the P3P problem constructed by three checkerboards,the enhanced RP3P algorithm is used which can improve the stability of the solution.Secondly,an optimal solution selection strategy based on observation probability with uncertainty weighted is designed to improve the accuracy of the optimal solution.Experimental results show that the proposed algorithm can significantly improve the probability of reasonable solution and calibration accuracy compared with the algorithms in the literature.Under different noise levels,compared with Francisco method and Hu method,the probability of reasonable solution is improved by 5%~41% and 2%~20%,the rotation matrix accuracy is improved by 2°~6°and 1.5°~2°,the translation vector accuracy is improved by 180~520 mm and 150~180 mm,so the performance is improved obviously.