A Hybrid Topological and Geometrical Robot Mapping Approach

Abstract

Robotic mapping systems are traditionally separated into the metric and the topological paradigm. The metric approach provides geometrical accuracy, but is fragile because it is bounded in an absolute coordinate system and depends on the use of odometry for navigation. The topological paradigm provides a compact presentation and navigation free of accumulated error. In this thesis the topological and the metric paradigm is combined into a hybrid representation where a topological map joins a set of local maps. Each local map contains a pair of self-organizing maps, one that maps the metric space of the local map, and one that maps the perceptual space of the local map. Local navigation is performed over the SOM mapping the metric space and position correction is performed over the SOM mapping the perceptual space. A boundary-tracing behavior is used for navigation within the global map, while the local metric maps allow for more precise navigation and is navigated by performing path-integration.