基于非线性优化的改进相机内参数标定方法  

Improved Calibration Method of Camera Internal Parameters Based on Nonlinear Optimization

作　　者：刘屹东 贾振堂 Liu Yidong;Jia Zhentang(College of Electronics and Information Engineering,Shanghai University of Electric Power,Shanghai 200090,China)

机构地区：[1]上海电力大学电子与信息工程学院,上海200090

出　　处：《激光与光电子学进展》2022年第18期337-345,共9页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61401269)。

摘　　要：为了实现空间物体三维重建,需要对相机参数进行标定,标定精度是其中关键问题。针对传统相机标定算法精度不高、收敛慢的问题,提出了一种基于动态调整和自适应变异相结合的改进粒子群优化相机参数算法。该算法以传统标定的结果为初始值,通过定义个体搜索能力来动态调整群体的惯性权重,避免了因惯性权重设置不合理对算法搜索能力的影响;并且根据粒子陷入局部最优的程度来自适应地调整最佳粒子的变异,从而提高算法的全局搜索能力。将所提相机参数标定算法与其他标定算法进行了比较,实验结果表明,所提算法具有优越性。To realize the threedimensional reconstruction of space objects,the camera parameters need to be calibrated,and the calibration accuracy is the primary concern.Due to the low precision and slow convergence of traditional camera calibration method,an improved particle swarm optimization camera parameter algorithm based on dynamic adjustment and adaptive variation is proposed.The method takes the traditional calibration results as the initial value and dynamically adjusted the inertia weight of the group by defining the individual search ability,avoiding the influence of unreasonable setting of inertia weight on the algorithm search ability.In addition,the optimal particle variation is adjusted adaptively based on the degree of particle falling into local optimal,in order to improve the global search ability of the algorithm.The proposed camera parameter calibration method is compared with other calibration methods,experimental results show that the proposed algorithm has advantages.

关 键 词：机器视觉 相机标定 非线性优化 动态权值 自适应变异 改进粒子群算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]