Expressing Bayesian fusion as a product of distributions: applications in robotics

More and more fields of applied computer science involve fusion of multiple data sources, such as sensor readings or model decision. However, incompleteness of the model prevents the programmer from having an absolute precision over their variables. Therefore Bayesian framework can be adequate fro such a process as it allows handling of uncertainty. We will be interested in the ability to express any fusion process as a product, for it can lead to reduction of complexity in time and space. We study in this paper various fusion schemes and propose to add consistency variable to justify the use of a product to compute distribution over the fused variable. We will then show application of this new fusion process to localization of a mobile robot and obstacle avoidance.