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.