BRIEF-Gist - closing the loop by simple means

The ability to recognize known places is an essential competence of any intelligent system that operates autonomously over longer periods of time. Approaches that rely on the visual appearance of distinct scenes have recently been developed and applied to large scale SLAM scenarios. FAB-Map is maybe the most successful of these systems. Our paper proposes BRIEF-Gist, a very simplistic appearance-based place recognition system based on the BRIEF descriptor. BRIEF-Gist is much more easy to implement and more efficient compared to recent approaches like FAB-Map. Despite its simplicity, we can show that it performs comparably well as a front-end for large scale SLAM. We benchmark our approach using two standard datasets and perform SLAM on the 66 km long urban St. Lucia dataset.