Cortically Inspired Sensor Fusion Network for Mobile Robot Heading Estimation
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.
KeywordsCortical inspired sensor fusion graphical network local processing mobile robotics
Unable to display preview. Download preview PDF.
- 4.Griffiths, T.L., Yuille, A.L.: A primer on probabilistic inference. Trends in Cognitive Sciences 10(7) (2006)Google Scholar
- 5.Körding, K.P., et al.: Causal Inference in Multisensory Perception. PLoS ONE (2007)Google Scholar
- 6.Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)Google Scholar
- 8.Swindale, N.V.: How different Feature Spaces may be Represented in Cortical Maps. Network: Computation in Neural Systems 15 (2005)Google Scholar
- 9.Sporns, O.: Networks of the Brain. MIT Press (2011)Google Scholar
- 10.Cook, M., Gugelmann, L., Jug, F., Krautz, C., Steger, A.: Interacting maps for fast visual interpretation. In: Proc. of International Joint Conference on Neural Networks, pp. 770–776 (2011)Google Scholar
- 11.Michler, F., Eckhorn, R., et al.: Using Spatiotemporal Correlations to Learn Topographic Maps for Invariant Object Recognition. J. of Neurophysiology 102, 955–964 (2009)Google Scholar
- 13.Averbeck, B.B., Latham, P.E., Pouget, A.: Neural correlations, population coding and computation. Nature Review Neuroscience (7), 358–366 (2006)Google Scholar