【摘要】 通过分析基本矩阵的鲁棒估计方法的特点,提出了三点改进:在RANSAC(RANdomSAmplingConsensus)方法中采用了极小化再投影误差判别数据点的类别;给出再投影误差的一阶近似算法;由求出的基本矩阵和局内点数据采用LM算法对结果过一步求精,给出更好的基本矩阵估计值,使得再投影误差进一步减小,避免结果趋于局部极值。合成数据和真实图像实验均证明了该方法的有效性和可靠性。更多还原

【Abstract】 After analyzing the characteristics of methods for computing fundamental matrix,a method was presented for robustly estimating fundamental matrix with three improvement.The data set was discriminated into inliers or outliers by minimizing reprojection error.The computation of one order approximation for reprojection error was given.To avoid local minimum,LM algorithm was adopted in last steps of RANSAC(RANdom SAmpling Consensus) algorithm.A very good estimation of fundamental matrix was obtained and the reprojection error was smaller.Experiment results on synthetic and real images demonstrated that the new algorithm is valid and robust.更多还原