Heterogeneous Bayes Filters with Sparse Bayesian Models: Application to state estimation in robotics

This study introduces a new augmented Bayes filter model for time-varying, context-dependent emission noise. The envisaged ap-plication, robust state estimation for a robot, motivates the use of the Relevance Vector Machine to model the emission noise, as it provides sparsity and fast inference capabilities. Besides the introduction of this new model, this work also aims at comparing the final filter performance when discriminative training is used instead of the prevalent generative training. The theoretical foundations for training and running inference over the model are proposed.