基于改进灰狼遗传算法的相机标定

Camera Calibration Based on Improved Grey-Wolf Genetic Algorithm

为解决传统相机标定中标定精度低、可重复性差、鲁棒性弱等问题,提出一种基于改进灰狼遗传算法的相机标定优化方法。对灰狼算法中的种群初始化、线性收敛因子及位置更新策略进行改进,并融入了基于维度学习的搜索策略和改进的选择、交叉、变异算子,以优化相机标定参数。首先,利用MATLAB标定工具箱对标定板图像进行角点提取,基于相机标定原理,建立标定板角点坐标与空间三维点坐标的对应关系,以获取相机内参及畸变系数的初估值,并据此设定待优化参数的寻优范围。接着,以初估值为基础在寻优范围内生成灰狼遗传算法的初始种群。然后,构建平均重投影误差方程,以最小化该误差为目标函数,利用改进的灰狼遗传算法对标定参数进行优化。最后,将该方法与其他优化方法进行实验对比。结果表明,基于改进灰狼遗传算法的相机标定方法在平均重投影误差最小(0.02054 pixel)的同时,也展现了最佳的可重复性和鲁棒性。

To solve the problems of low calibration accuracy,inferior repeatability,and weak robustness in conventional camera calibration,an optimized camera calibration method based on an improved grey-wolf optimization algorithm is proposed.This method improves the population initialization,linear convergence factor,and position update strategy of the grey-wolf algorithm,as well as integrates search strategies based on dimension learning and improved selection,crossover,and mutation operators to optimize camera calibration parameters.First,the MATLAB calibration toolbox is used to extract the corner points of the calibration board image.Based on the camera calibration principle,the corresponding relationship between the corner point coordinates of the calibration board and the coordinates of threedimensional points in space is established to obtain the initial estimation of the camera internal parameters and distortion coefficients.Accordingly,the optimization parameters to be optimized are set.Second,based on the initial estimation,an initial population for the grey-wolf genetic algorithm is generated within the optimization range.Next,an average reprojection-error equation is constructed,with the objective function of minimizing this error.The improved grey-wolf genetic optimization algorithm is used to optimize the calibration parameters.Finally,the method is experimentally compared with other optimization methods.The results show that the camera calibration method based on the improved grey-wolf genetic algorithm not only has the smallest average reprojection error but also exhibits the best repeatability and robustness.