一种基于改进拟仿射变换的基础矩阵估计方法

以基础矩阵的估计为基础，使用计算机视觉的方法从一组不同角度、不同距离拍摄的同一场景所得的二维序列图像中还原出目标对象的三维空间信息，是实现基于图像的建模、即时定位与地图构建等前沿热点问题的主流解决方案。在基础矩阵估计问题中，准确性和效率是2个主要的衡量指标。准确性不够时，往往需要通过后端优化等方式花费高昂的代价对其进行修正，效率低则会影响系统的实时性。针对该问题，提出一种基于改进拟仿射变换的基础矩阵估计方法。具体来说，在QUATRE算法基础上，首先提出一种基于特定“基因-染色体”模式的种群协作方法。其次，重新定义齐次坐标系所表示的离散解空间中的种群初始化、变异和交叉等操作。此外，还提出一种基于置信度的迭代次数确定方式，用于加速本文方法。实验表明，该方法能有效剔除噪声和误匹配所产生的外点干扰，在准确性和效率方面优于LMedS、RANSAC和MSAC等方法，可有效解决基础矩阵估计问题。

A fundamental matrix estimation method based on improved quasi-affine transformation

On the basis of fundamental matrix estimation, computer vision methods are used to reveal the three-dimensional information of an object within a series of scene images captured from different angles and distances. They are the primary solutions for cutting-edge problems such as image-based modeling and simultaneous localization and mapping. Accuracy and efficiency are two major metrics of fundamental matrix estimation methods. When the accuracy is not enough, high-cost back-end optimization is required for the correction, and its low efficiency affects the real-time performance of the system. To solve these problems, an improved quasi-affine transformation method is proposed based on the QUATRE algorithm. Firstly, a specific “gene-chromosome” mode is used for the collaboration of the particles. Besides, the way of initialization, mutation, and crossover of the original QUATRE algorithm are redefined within the discrete solution space described by the homogeneous coordinates. In addition, a confidence coefficient based iteration termination method is presented for the acceleration. Experiments show that the proposed method is useful for fundamental matrix estimation. It can effectively get rid of the disturbance of outliers resulting from the noises and mismatches, and it outperforms the methods such as the LMedS, RANSAC, and MSAC in terms of accuracy and efficiency.