针对原M-Estimators算法完全依赖由线性最小二乘法估计得到的矩阵初始值，精度较低稳定性差的缺点，提出了一种改进的M-Estimators算法。通过考虑匹配点与对应极线的距离，计算求得较原M-Estimators算法更加精确的矩阵初始值，再利用此初始值剔除掉原匹配点集中的错误匹配点及坏点，最后运用Torr-M-Estimators法对新的匹配点集进行非线性优化计算，得到了真正的匹配点对，精确恢复了对极几何关系。以大量的模拟数据和真实图像进行了实验，给出了该算法与其他鲁棒性算法的比较结果，实验结果表明，该算法在误匹配以及高斯噪声存在的情况下，提高了基础矩阵的估计精度，并且同时具有很好的鲁棒性。

Considering the dissatisfactory precision and stability of primary M-Estimators, which depends entirely on the original matrix obtained by the method of least squares, an improved M-Estimators algorithm for estimating the fundamental matrix was studied The new algorithm obtained a more precise original matrix by calculating the distances between the matching points and the corresponding epipolar lines Then the mismatch and outliers in the original matching points set were eliminated through the precise original matrix and the nonlinear optimization for the new matching points set was carried out with Torr-M-Estimators Finally the accurate matching points set and the epipolar geometry can be gained Through a mass of experiments on simulated data and real images in the case of mismatching and Gaussian noise, the comparing results between the algorithm and other robust methods indicate the algorithm not only improves the estimating precision but also shows the good robustness