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Learning Sensory Correlations for 3D Egomotion Estimation

  • Cristian Axenie
  • Jörg Conradt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9222)

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.

Keywords

Egomotion estimation Cross-modal learning Multisensory fusion Mobile robots 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Neuroscientific System Theory Group, Department of Electric and Computer EngineeringTechnische Universität MünchenMunichGermany

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