Multi Sensor Fusion for Simultaneous Localization and Mapping on Autonomous Vehicles

Although many different sensors are nowadays available on autonomous vehicles, the full potential of techniques which integrate information coming from these different sensors to increase the ability of autonomous vehicles of avoiding accidents and, more generally, increase their safety levels remains untapped. Indeed, navigation techniques in static environments based on fusion of multiple sensors are well known, but it is not clear how such methods can cope with more realistic, dynamic environments which may include people, other moving vehicles, or mutating environment features. The problem goes under the name of Simultaneous Localization And Mapping with Moving Objects Tracking (SLAMMOT). Although several approaches were proposed in literature, so far they do not appear to be able to exploit the availability of multiple heterogeneous sensors. The aim of this project is therefore to investigate solutions to this problem that combine the (potentially conflicting) information coming from an array of heterogeneous sensors in order to accurately localize the vehicle and estimate the (evolving) configuration of the surrounding dynamic environment. The plan to pose the problem in a probabilistic framework. In particular, approaches based on imprecise probabilities have been proven effective in numerous applications involving sensor fusion in the presence of conflicting data [e.g. Pagac 1998, Ristic 2006].