A Research of Feature Matching Algorithm Based on Monocular Vision SLAM

In recent years, with the progress of image processing technology, the research on Simultaneous Localization and Mapping (SLAM) has gradually become a hot topic in the field of computer vision. Visual SLAM is mainly divided into front end and back end. In this study, aiming at the problem of low efficiency of feature point matching, an improved fast feature approximation nearest neighbor feature matching method is proposed, which can obtain higher precision image matching relationship in a short time. At the same time, an algorithm of Cluster Sampling Consensus (CLUSAC) algorithm is proposed, which reduces the probability of mismatching and makes camera pose estimation more accurate. Finally, a comparative experiment of the algorithm is performed. The results show that the improved feature matching algorithm and mismatching elimination algorithm can effectively improve the accuracy of feature matching and help to provide accurate and effective initial values for the back end.