V. Mladenov et al. (Eds.): ICANN 2013, LNCS 8131, pp. 240–247, 2013. © Springer-Verlag Berlin Heidelberg 2013 Cortically Inspired Sensor Fusion Network for Mobile Robot Heading Estimation Cristian Axenie and Jörg Conradt Fachgebiet Neurowissenschaftliche Systemtheorie, Fakultät für Elektro- und Informationstechnik, Technische Universität München, 80290 München, Germany {cristian.axenie,conradt}@tum.de Abstract. All physical systems must reliably extract information from their noisily and partially observable environment, such as distances to objects. Biology has developed reliable mechanisms to combine multi-modal sensory information into a coherent belief about the underlying environment that caused the percept; a process called sensor fusion. Autonomous technical systems (such as mobile robots) employ compute-intense algorithms for sensor fusion, which hardly work in real-time; yet their results in complex unprepared environments are typically inferior to human performance. Despite the little we know about cortical computing principles for sensor fusion, an obvious difference between biological and technical information processing lies in the way information flows: computer algorithms are typically designed as feed- forward filter-banks, whereas in Cortex we see vastly recurrent connected networks with intertwined information processing, storage, and exchange. In this paper we model such information processing as distributed graphical network, in which independent neural computing nodes obtain and represent sensory information, while processing and exchanging exclusively local data. Given various external sensory stimuli, the network relaxes into the best possible explanation of the underlying cause, subject to the inferred reliability of sensor signals. We implement a simple test-case scenario with a 4 dimensional sensor fusion task on an autonomous mobile robot and demonstrate its performance. We expect to be able to expand this sensor fusion principle to vastly more complex tasks. Keywords: Cortical inspired sensor fusion, graphical network, local processing, mobile robotics. 1 Introduction Environmental perception enables a physical system to acquire and build an internal representation of significant information within its environment. As an example of such an internal state, accurate self-motion perception is an essential component for spatial orientation, navigation and motor planning for both real and artificial systems. A system can build its spatial knowledge using a combination of multiple sources of information, conveyed from self-motion related signals (e.g. odometry or vestibular signals), but also from static external environmental cues (e.g. visual or auditory) [1].