An efficient visual loop closure detection method in a map of 20 million key locations

An important problem in robot simultaneous localization and mapping (SLAM) is loop closure detection. Recent studies of the problem have led to successful development of methods that are based on images captured by the robot. These methods tackle the issue of efficiency through data structures such as indexing and hierarchical (tree) organization of the image data that represent the robot map. In this paper, we offer an alternative approach and present a novel method for visual loop-closure detection. Our approach uses an extremely simple image representation, namely, a down-sampled binarized version of the original image, combined with a highly efficient image similarity measure - mutual information. As a result, our method is able to perform loop closure detection in a map with 20 million key locations in about 2.38 seconds on a commodity computer. The excellent performance of our method in terms of its low complexity and accuracy in experiments establishes it as a promising solution to loop closure detection in large-scale robot maps.