Statistical habitat maps for robot localisation in unstructured environments

We present an approach to mobile robot navigation in unstructured environments. Natural scenes can very often be considered as random fields where a large number of individual objects appear to be randomly scattered. This randomness can be described by statistical models. We consider that a natural scene can be interpreted as realisations of random closed sets, whose global characteristics are mapped. Contrary to the feature based approach, this environment representation does not require the existence of outstanding objects in the workspace, and is robust with respect to small dynamic changes. We address the problem of estimating the position of a mobile robot, assuming that a statistical model, serving as a map of the environment, is available to it a priori. Simulation results demonstrate the feasibility of our approach.