中文摘要：

在增强现实中,为了把虚拟信息和真实信息融合起来,需要知道关于真实手术场景的信息,然后对其进行三维重建,而基础矩阵的估计是其中关键的一步。提出一种MAPSACNL方法,把MAPSAC方法所得到的值作为基础矩阵的初始值,再用非线性Levenberg-Marquardt方法进行优化,并与线性方法和最小二乘法所得到的结果进行比较。结果表明,用MAPSACNL方法估计基础矩阵,对于迭代次数、角点数量、ζ值的变化,都具有更强的鲁棒性和更小的Sampson误差值。

英文摘要：

In augmented reality, the real surgical scene information is required for three dimensional reconstructions in order to merge real and virtual information. The estimation of the fundamental matrix is one of the key steps. In this paper we proposed a MAPSACNL algorithm which integrates the MAPSAC algorithm and nonlinear method by using the results of MAPSAC as the initial value of the fundamental matrix and then optimizing it by Levenberg- Marquardt method. We compared the results of our methods and the results of linear method and the iterative least squares, which showed MAPSACNL method estimating the fundamental matrix was more robust and had less Sampson errors for the variance of the iterative numbers, the comer points numbers and the ζ value.