激光与光电子学进展, 2020, 57 (4): 041514, 网络出版: 2020-02-20
基于改进粒子群算法的相机内参优化方法 下载: 1076次
Optimization Method for Camera Intrinsic Parameters Based on Improved Particle Swarm Algorithm
机器视觉 相机标定 粒子群优化 内参 自适应调整 machine vision camera calibration particle swarm optimization intrinsic parameters adaptive adjustment
摘要
相机标定是机器人视觉系统中实现精确定位的重要前提,针对传统相机标定精度不高的问题,提出基于改进粒子群算法的相机标定优化方法。该方法以张正友标定方法获得相机内参初始值,在不同迭代阶段实现对惯性参数非线性自适应调整,以平衡局部和全局搜索能力;对社会和自身学习率采用不同迭代阶段正余弦变化的动态自调整策略,进一步提高全局搜索能力与后期搜索精度;在粒子群快要陷入局部最优时,采用驱散机制扩大粒子群所在空间范围,避免算法过早收敛。实验结果表明,所提相机标定方法与传统标定方法相比具有较高的精度和较好的可重复性。
Abstract
Camera calibration is an important premise for accurate positioning in robot machine vision systems. To solve the problem of low accuracy of traditional camera calibration, this paper proposes a camera calibration optimization method based on an improved particle swarm optimization algorithm. This method uses Zhang Zhengyou calibration method to obtain the initial value of camera intrinsic parameters and realizes the nonlinear self-adaptive adjustment of inertial weight parameters in different iteration stages, balancing the local and global search capabilities. Dynamic self-adjusting strategies of sines and cosines changes in different iteration stages are adopted for global and local learning factors to further improve the global search ability further and late search accuracy. When a particle swarm is about to fall into the local optimum, the dispersing mechanism is used to enlarge the spatial range of the particle swarm to avoid premature convergence of the algorithm. Experimental results show that the proposed method has better precision and repeatability as compared with the traditional methods.
徐呈艺, 刘英, 肖轶, 曹健. 基于改进粒子群算法的相机内参优化方法[J]. 激光与光电子学进展, 2020, 57(4): 041514. Chengyi Xu, Ying Liu, Yi Xiao, Jian Cao. Optimization Method for Camera Intrinsic Parameters Based on Improved Particle Swarm Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041514.