Cortically inspired sensor fusion network for mobile robot egomotion estimation

• A cortically inspired sensor fusion network for robotic applications is introduced.
• Distributed graphical network, with combined feed-forward and recurrent connectivity.
• No global supervisor, only local processing, storage and exchange of information.
• Given sensory data, network relaxes into the best explanation of an underlying cause.
• Extensible to learn sensor correlations and adapt connectivity from incoming data.

All physical systems must reliably extract information from their noisy and partially observable environment and build an internal representation of space to orient their behaviour. Precise egomotion estimation is important to keep external (i.e. environmental information) and internal (i.e. proprioception) cues coherent. The constructed representation subsequently defines the space of possible actions. Due to the multimodal nature of incoming streams of sensory information, egomotion estimation is a challenging sensor fusion problem. In this paper we present a distributed cortically inspired processing scheme for sensor fusion, which given various sensory inputs, and simple relations defining inter-sensory dependencies, relaxes into a solution which provides a plausible interpretation of the perceived environment. The proposed model has been implemented for egomotion estimation on an autonomous mobile robot. We demonstrate that the model provides a precise estimate of both robot position and orientation.