一种基于Kruppa方程的分步自标定方法

针对摄像机自标定中Kruppa方程求解的非线性优化问题和标定结果的欠鲁棒性,提出一种基于Kruppa方程的分步自标定方法。根据两图像匹配的特征点对采用8点算法求解相应的基本矩阵,其中待匹配图像选用摄像机对同一场景在不同焦距下拍摄的两帧图片,对图片的特征匹配点建立约束关系,采用最小二乘法求出摄像机的主点坐标,然后利用遗传算法优化Kruppa方程的比例因子,最后通过优化后的比例因子完成摄像机的标定。实验表明,该方法可提高标定精度,并通过对特征点坐标加入高斯噪声,验证了算法的鲁棒性。

Multi-step Self-calibration Method Based on Kruppa Equations

Most of the existing algorithms for camera self-calibration have following problems: non-linear optimization of Kruppa equations, and not robust In order to solve these problems, a multi-step self-calibration method was proposed. This method begins with getting the fundamental matrix between images by 8-point algorithm. I}he candidate matching images arc acquired by taking two pictures of one scene using different focal length. We established the constraint correspondence between feature points of two images. The coordinate of principle point was calculated by using least square method. Finally the genetic algorithm was adopted to optimize the scale factor of the Kruppa equations and accomplishing the task of camera calibration. Experiment results indicate that the proposed method can effectively improve the accuracy and robustness of self-calibration method based on Kruppa equations.