【摘要】 2维激光雷达和摄像机的最小解标定方法存在精度较差、解缺失等不足，为此，提出了一种新的可靠最小解标定方法。首先，利用透视相似三角形(perspective similar triangle, PST)算法求解3个棋盘格构建的P3P(perspective three points, P3P)问题，并通过多项式极值点获取缺失的解，提高了算法对噪声的抗干扰能力。其次，提出基于两类激光点约束的误差度量模型来评估多解的误差程度，从而更准确地从标定结果的多解中选取最优解。实验结果表明，该文算法相比于文献中的FRANCISCO算法和HU算法，能明显提高有效解概率和标定精度；在不同噪声水平下，该文算法的有效解概率提高了5%—20%和5%—13%,旋转矩阵精度提高了46%—63%和41%—47%,平移向量精度提高了170—430mm和120—170mm,性能提高明显。更多还原

【Abstract】 The minimum solution calibration method of 2D lidar and camera has shortcomings such as poor accuracy and missing solution.Therefore, a new reliable minimum solution calibration method is proposed in this paper.Firstly, the perspective similar triangle(PST) algorithm is used to solve the perspective three points(P3P) problem constructed by three checkerboards, and the missing solution is obtained with the polynomial extreme points, which improves the anti-noise interference ability of the algorithm.Secondly, an error measurement model based on two types of laser point constraints is proposed to evaluate the error degree of multiple solutions, so as to select the optimal solution from the multiple solutions of the calibration results.Experimental results show that the proposed algorithm can significantly improve the valid solution probability and the calibration accuracy.Under different noise levels, compared with FRANCISCO method and HU method, the probability of valid solution is improved by 5%—20% and 5%—13%,the rotation matrix accuracy is improved by 46%—63% and 41%—47%,the translation vector accuracy is improved by 170—430 mm and 120—170 mm, so the performance is improved obviously.更多还原