Hierarchical localization using compact hybrid mapping for large-scale unstructured environments

Abstract

Hierarchical localization frameworks provide efficient means for localizing a robot topologically and geometrically. The metric localization performance is enhanced since the searchable space is confined to a previously identified topological place. This adapts well to large-scale environments. In this paper, a two-level hierarchical localization using a compact hybrid map is presented. The map preserves information set at two different abstractions and resolutions, and possesses both geometric and non-geometric properties. The coarse-resolution information enables global topological localization through place matching. The higher-resolution enables local metric localization through triangulation. The presented hierarchical localization is totally independent on the robot's motion model. For effectiveness in both mapping and localization, the map is constructed based on information-theoretic evaluation that selects only highly qualitative information. The approach is demonstrated using a vision sensor and the scale invariant feature transform. © 2011 IEEE.