基于粒子群算法的摄像机标定过程优化

摄像机标定为机器视觉在物体位姿与姿态的测量过程中最重要一环,其映射物体三维空间与二维图像之间关系是一个复杂非线性最优化问题。为了更好地解决这一复杂优化问题,阐述了利用粒子群优化(PSO)算法计算摄像机标定过程的一种优化方法,重点描述了PSO算法的原理,单目视觉测量系统,以及基于CMOS摄像机的成像模型及其原理和算法。通过图像软件提取靶体模型上特征控制点,及摄像机标定算法建立了相应的计算公式。结合PSO算法优化像机外参,实验结果表明,PSO算法计算准确、速度快,具有很强的工程应用价值。

Optimization of camera calibration process based on PSO algorithm

The camera calibration for machine vision plays an important role in measuring position and pose of objects, it maps the relationship from 3D space objects and 2D image. However, it is a difficult nonlinear optimization problem. In order to solve the complex optimiza- tion problems, particle swarm optimization（PSO） algorithm was put forward to solve the problem as a kind of optimization method. Themethod and the related theory of PSO were described, such as the principle of PSO algorithm, the monocular vision measurement system, the ima- ging model based on CMOS camera and its principle and algorithm. Combined with the PSO algorithm, the experiment results show that this algorithm is accurate, and it has a strong engineering application value.