Qualitative modeling of indoor environments from visual landmarks and range data

This article describes the integration in a complete navigation system of an environment modeling method based on a Generalized Voronoi Graph (GVG), relying on laser data, on the one hand, and of a localization method based on monocular vision landmark learning and recognition framework, on the other hand. Such a system is intended to work in structured environments. It is shown that the two corresponding modules - laser GVG construction and visual landmarks learning and recognition - can cooperate to complete each other, as image processing can be enhanced by some structural knowledge about the scene, whereas the GVG is annotated, even as far as its edges are concerned, by qualitative visual information.