1A Bayesian Framework for Active Artificial Perception

Abstract—In this text, we present a Bayesian framework for active multimodal perception of 3D structure and motion. The design of this framework finds its inspiration in the role of the dorsal perceptual pathway of the human brain. Its composing models build upon a common egocentric spatial configuration that is naturally fitting for the integration of readings from multiple sensors using a Bayesian approach. In the process, we will contribute with efficient and robust probabilistic solutions for cyclo-pean geometry-based stereovision and auditory perception based only on binaural cues, modelled using a consistent formalisation that allows their hierarchical use as building blocks for the multimodal sensor fusion framework. We will explicitly or implicitly address the most important challenges of sensor fusion using this framework, for vi-sion, audition and vestibular sensing. Moreover, interaction and navigation requires maximal awareness of spatial surroundings, which in turn is obtained through active attentional and behavioural exploration of the environ-ment. The computational models described in this text will support the construction of a simultaneously flexible and powerful robotic implementation of multimodal active perception to be used in real-world applications, such as human-machine interaction or mobile robot navigation