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Global localization (or place recognition) is a method of finding the current location of a robot on a map generated by a mapping process, and it is an open field that has not yet been completely solved in the field of mobile robotics. Most existing approaches to global localization are based on extraction of interest point features and their descriptors whether from raw laser scans or 2D occupancy grid maps. In this letter, unlike most approaches, we propose a novel method of extracting a statistical signature of geometric and structural features from a submap. A boundary and free-space features can characterize a geometric shape, while a reflection symmetry can quantify a structural shape of the submap. Experiments using five pre-built map publicly available demonstrate that the proposed method outperforms the other state-of-the-art image-based methods by examining precision-recall curve especially when occupancy noise added to the submap is progressively increased.

Extracting Statistical Signatures of Geometry and Structure in 2D Occupancy Grid Maps for Global Localization