Visual Loop Closing using Gist Descriptors in Manhattan World

We present an approach for detecting loop closures in a large sequence of omni-directional images of urban environments. In particular we investigate the efficacy of global gist descriptors computed for 360 cylindrical panoramas and compare it with the baseline vocabulary tree approach. In the context of loop closure detection, we describe a novel matching strategy for panoramic views, exploiting the fact that the vehicle travels in urban environments where heading of the vehicle at previously visited locations and loop closure points are related by multiple of 90 degrees. The performance of the presented approach is promising despite the simplicity of the descriptor.