Robust Real-Time Visual SLAM Using Scale Prediction and Exemplar Based Feature Description

Two major limitations of real-time visual SLAM algorithms are the restricted range of views over which they can operate and their lack of robustness when faced with erratic camera motion or severe visual occlusion. In this paper we describe a visual SLAM algorithm which addresses both of these problems. The key component is a novel feature description method which is both fast and capable of repeat-able correspondence matching over a wide range of viewing angles and scales. This is achieved in real-time by using a SIFT-like spatial gradient descriptor in conjunction with efficient scale prediction and exemplar based feature representation. Results are presented illustrating robust realtime SLAM operation within an office environment.