Semantically Guided Feature Matching for Visual SLAM

Oguzhan Ilter, Iro Armeni, Marc Pollefeys, Daniel Barath

Abstract: We introduce a new algorithm that utilizes semantic information to enhance feature matching in visual SLAM pipelines. The proposed method constructs a high-dimensional semantic descriptor for each detected ORB feature. When integrated with traditional visual ones, these descriptors aid in establishing accurate tentative point correspondences between consecutive frames. Additionally, our semantic descriptors enrich 3D map points, enhancing loop closure detection by providing deeper insights into the underlying map regions. Experiments on public large-scale datasets demonstrate that our technique surpasses the accuracy of established methods. Importantly, given its detector-agnostic nature, our algorithm also amplifies the efficacy of modern keypoint detectors, such as SuperPoint. The implementation of our algorithm can be found on Github 3 .