Abstract
Learning processes which take place during the development of a biological nervous system enable it to extract mappings between external stimuli and its internal state. Precise egomotion estimation is essential to keep these external and internal cues coherent given the rich multisensory environment. In this paper we present a learning model which, given various sensory inputs, converges to a state providing a coherent representation of the sensory space and the cross-sensory relations. The developed model, implemented for 3D egomotion estimation on a quadrotor, provides precise estimates for roll, pitch and yaw angles.
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Axenie, C., Conradt, J. (2015). Learning Sensory Correlations for 3D Egomotion Estimation. In: Wilson, S., Verschure, P., Mura, A., Prescott, T. (eds) Biomimetic and Biohybrid Systems. Living Machines 2015. Lecture Notes in Computer Science(), vol 9222. Springer, Cham. https://doi.org/10.1007/978-3-319-22979-9_32
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DOI: https://doi.org/10.1007/978-3-319-22979-9_32
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