Formal methods for reasoning and uncertainty reduction in evidential grid maps

Information fusion is the task of combining data collected from different sources into a unified representation. Here, a main challenge is to deal with the inherent uncertainty contained in the information, such as sensor noise, conflicting information, or incomplete knowledge. In current approaches, one usually employs independence assumptions in order to reduce the complexity. Because of this, the full potential of the gathered data is often not fully exploited and the fusion may lead to additional uncertainty. In order to reduce this uncertainty, further information in form of background and expert knowledge can be utilized, which is often available for real-world scenarios. However, reasoning on this knowledge is a computational complex task. In this work, we propose a methodology which utilizes formal methods for that reasoning, which allows to relax some of the independence assumptions. We demonstrate the proposed methodology using evidential grid maps - a belief function-based environment representation, in which different kinds of uncertainty are represented explicitly. Our methodology is evaluated based on basic structures as well as on real-world data sets. The results show that the uncertainty in the maps is significantly reduced by considering dependencies among cells.