基于PROSAC的视觉SLAM特征匹配方法

当前视觉即时定位与地图重构技术（SLAM）的过程中，通常采用随机采样一致性（RANSAC）的图像特征匹配算法，随机估计图像模型容易造成算法时间复杂度不确定，进而导致图像匹配时耗过大，难以满足视觉SLAM实时性的要求。为了改善这一问题，使用渐进采样一致性（PROSAC）的算法对图像特征进行筛选，剔除误匹配特征点，有效改善了图像特征匹配的效率与鲁棒性，进一步增强了视觉SLAM的稳定性与实时性。试验验证表明，相比于当前视觉SLAM特征匹配算法，计算效率明显提升。

Visual SLAM Feature Matching Algorithm Based on PROSAC

In the process of visual Simultaneous Localization and Mapping (SLAM), the image feature matching algorithm ofRandom Sampling Consensus (RANSAC) is usually used to estimate the image model randomly, which is easy to cause theuncertainty of algorithm time complexity, and then lead to excessive image matching time consumption. It is difficult to meet thereal-time requirements of visual SLAM. In order to improve the problem, the algorithm of Progressive Sampling Consensus(PROSAC) is used to screen image features and reject mismatched feature points, which effectively improves the efficiency androbustness of image feature matching, and further enhances the stability and real-time performance of visual SLAM. Experimentalverification shows that compared with the current visual SLAM feature matching algorithm, the computational efficiency issignificantly improved.