Information-theoretic environment modeling for efficient topological localization

Place recognition is a vital methodology for modeling environments and localizing autonomous mobile robots topologically. It can also be integrated in a hierarchical framework where it guides a fast and more precise metric position estimation. Especially for those hierarchical frameworks, it is crucial that the place recognition modules be highly accurate. In this paper, an information-theoretic approach that focuses on the efficiency of place recognition for topological environment modeling and localization is presented. The approach relies on a minimal discriminative feature set obtained from an entropy-based qualitative evaluation and a codebook compression. The generated environment feature map achieves a significant combination of high localization accuracy, speed and less memory storage.