Autonomous construction of indoor maps with a mobile robot

In this paper, we introduce an autonomous map-building technique for mobile robots, based on combinatorial maps. Existing representations of the environment traditionally fall into two distinct categories: metric or topological. Topological approaches are usually well-adapted to global planning and navigation tasks. However, metric maps are easier to read for a human operator and they are better suited to precise robot positioning. Among them, we can distinguish feature-based and area-based maps. Our model enables us to combine the orthogonal strengths of these various representations in a rather compact and efficient way, using an algebraic tool named combinatorial map. We propose a global framework to deal with topological and geometric uncertainties, and a whole strategy for the autonomous generation of 2D combinatorial maps of the environment. The main innovation lies in the way local free space is fused into the global model in order to correct both the position and the topology of obstacles. We extend the notion of discrete and regular occupancy grid to any kind of polygonal subdivision, with cells of variable shapes and dimensions. To conclude, we describe experiments conducted with a real-world robot moving about within a well-structured indoor environment.