单像自标定的迭代优化方法

针对基于灭点的单像自标定方法精度不高的局限性,利用影像中的灭点和椭圆几何约束信息,提出一种迭代优化的单像自标定方法。根据极点-极线关系及其表示的正交性,由影像中的椭圆曲线及其所在平面的灭线确定一组正交共轭灭点对。利用这些正交共轭灭点对建立关于主距和主点的非线性模型,以主距的方差最小作为优化准则,并选用多个位置作为主点的初始值进行多次迭代优化估计,获得主距和主点的最优结果。仿真影像和真实影像实验结果表明,该方法能够有效地实现单像自标定。与基于灭点的摄像机标定方法相比,该方法能够获得更为满意的标定结果。

Iterative optimization for camera self-calibration from a single image

The method of camera calibration from vanishing points is easily distracted by noise in the image, leading to inaccurate results which are often inadmissible for camera calibration. To overcome the limitation, we present an iterative optimization approach for calibration from single image, which make full use of geometric constraints of vanishing points and ellipse in the image. According to the pole-polar relationship and the orthogonality represented by the pole-polar relationship, a set of orthogonal conjugate vanishing point pairs are calculated using the ellipse and the coplanar vanishing line. A nonlinear model of the principle distance and principle point is established based on these vanishing point pairs. The principle distance and principle point are iteratively optimized, according to the variance of principle distances and the multiple points set as the initial values of the principle point. The variance will converge toward the minimum, so the optimal principle distance and principle point are achieved. Experimental results from simulated and real data show the approach can effectively realize camera self-calibration from a single image. Compared with the camera calibration method using vanishing points, the approach achieves more satisfactory calibration results.