Image matching plays an important role in Augmented Reality, Simultaneous Localization and Map ping (SLAM), and unmanned. The key issue of image matching is the accuracy of feature matching between adjacent images. Due to the complicated environment, matching between images through geometry feature runs into bottlenecks. In this paper, we focus on improving the accuracy of feature matching by incorporating instance-aware semantic segmentation into Oriented FAST and Rotated BRIEF (ORB) feature matching, which is broadly utilized in image registration and Visual SLAM. We segment the objects between the adjacent images on pixel level and qualify the features matching procedure using the semantic information. Experiment results show that our novel proposed method increases the accuracy of feature matching.
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