A low-level framework for a probabilistic treatment of the topological description of a robot mission

This article describes a mathematical basis required to integrate features obtained for perception for topological navigation. It is intended for application to navigation in an environment that is not mapped, but in which a mission is described in the form of a semantic description of the perception stimulus that the robot is expected to encounter. The need to integrate features from different sensors led to the use of an uncertainty estimate employed in information theory; binary entropy. By using entropy, the features are ranked in order of decreasing uncertainty. This article describes the state of the work in an as yet preliminary stage, but appears promising for application to navigation using topological information. It also offers interesting perspectives on commonly used sensory data such as local intensity image features.