Towards the exploitation of formal methods for information fusion

When an autonomous system has to act in or interact with an environment, a suitable representation of it is required. In the past decades, many different representation forms - especially spacial ones - have been proposed and even more information fusion techniques were developed in order to build these representations from multiple information sources. However, most of these algorithms do not exploit the full potential of the available information. This is caused by the fact that they are not able to handle the full complexity of all possible solutions compatible with the information and that they rely on restrictive assumptions (i.e. independencies) in order to make the computation feasible. In this work, a new methodology is envisioned that utilizes formal methods, in particular solvers for Pseudo-Boolean Optimization, to drop some of these assumptions. In order to illustrate the ideas, information fusion based on belief functions and occupancy grid maps are considered. It is shown that this approach allows for considering dependencies among multiple cells and thus significantly reduces the uncertainty in the resulting representation.