基本矩阵随机采样鲁棒估计

提出一种新的基本矩阵鲁棒估计算法:随机采样算法,对含有大量出格点的数据点集,利用7个对应匹配点的最小子集来估计参数;然后在不同的子集重复多次,确保任何一个子集都含有一个好的数据点的机率达到95%.最优估计值是残差低于门限值点数最多的子集,一旦从数据点集剔去出格点,利用没有出格点的数据就可以得到最终估计值.用真实图像测试表明该算法鲁棒性好,精度高.

A Random Sampling Algorithm for Fundamental Matrix Robust Estimation

Epipolar geometry is the intrinsic projective geometry between two views of a static scene. It is independent of scene structure, and only depends on the camera's internal parameters and relative pose. The fundamental matrix encapsulates the whole epipolar geometry and accurate and robust estimation of the fundamental matrix is very important for many computer vision applications. In this paper, a robust algorithm is used to estimate the fundamental matrix——the random sample algorithm. Given that a large proportion of the data (the set of corner point pairs) may be useless, a small subset of the data (seven correspondences for a fundamental matrix) is feasible to estimate the parameters, and this process is repeated enough times on different subsets to ensure that there is a 95% chance that one of the subsets will contain only good data points. The best solution is that which maximizes the number of points whose residual is below a threshold. Once the outliers are removed, the set of points identified as non-outliers may be combined to give a final solution. Experiment with real images verifies that the random sample algorithm is both accurate and robust.