Uncertainty minimization in multi-sensor localization systems using model selection theory

Belief propagation methods are the state-of-the-art with multisensor state localization problems. However, when localization applications have to deal with multimodality sensors whose functionality depends on the environment of operation, we understand the need for an inference framework to identify confident and reliable sensors. Such a framework helps eliminate failed/non-functional sensors from the fusion process minimizing uncertainty while propagating belief. We derive a framework inspired from model selection theory and demonstrate results on real world multisensor robot state localization and multicamera target tracking applications.