Incremental Topological Modeling using Local Vorono

In the eld of mobile robotics, one important issue is to allow the robot to navigate in an a priori unknown and non speciic large scale environment. Large dimensions raise strong limitations of geometric model-ing, and topological or mixed metric-topological models are now studied to better t the problem. In this paper, we present a new method for incre-mentally 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 Vorono-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 rst encouraging results on real data.