Unified fusion system based on Bayesian networks for autonomous mobile robots

Abstract

A multisensor fusion system that is used for estimating the location of a robot and the state of the objects around it is presented. The whole fusion system has been implemented as a dynamic Bayesian network (DBN) with the purpose of having a homogenous and formalized way of capturing the dependencies that exist between robot location, the state of the environment, and all sensorial data. At this stage of research it consists of two independent DBNs, one for estimating robot location and another for building an occupancy probabilistic map of the environment, which are the basis of a unified fusion system. The dependencies of the variables and information in the two DBNs are captured by a unique DBN constructed by adding arcs (and nodes if necessary) between the two DBNs. The DBN implemented so far can be used in robots with different sets of sensors.