Incremental topological modeling using local Voronoi-like graphs

In the field of mobile robotics, one important issue is to allow the robot to navigate in an a priori unknown and non-specific large scale environment. Large dimensions raise strong limitations of geometric modeling, and topological or mixed metric-topological models are now studied to better fit the problem. We present a method for incrementally building a topological model of an indoor environment from sensor range data. The approach consists in merging each local perception of the topology with the current state of the global graph. This local topology is captured through the construction of a Voronoi-like graph that takes into account not only visible features but also visibility constraints (hidden regions, limited sensing ranges,...). We give the outline of the method and show first encouraging results on real data.