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)


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.


Egomotion estimation Cross-modal learning Multisensory fusion Mobile robots 


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  1. 1.
    Gibson, E.J.: Principles of Perceptual Learning and Development, pp. 369–394. ACC Press (1969)Google Scholar
  2. 2.
    Cook, M., Jug, F., Krautz, C., Steger, A.: Unsupervised learning of relations. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part I. LNCS, vol. 6352, pp. 164–173. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  3. 3.
    Weber, C., Wermter, S.: A self-organizing map of sigma-pi units. Neurocomputing 50, 2552–2560 (2007)CrossRefGoogle Scholar
  4. 4.
    Mandal, A., Cichoki, A.: Non-Linear Canonical Correlation Analysis Using Alpha-Beta Divergence. Entropy 15, 2788–2804 (2013)CrossRefGoogle Scholar
  5. 5.
    Westermann, G., Mareschal, D., Johnson, M.H., Sirois, S., Spratling, M.W., Thomas, M.S.: Neuroconstructivism. Dev. Sci. 10, 75–83 (2007)CrossRefGoogle Scholar
  6. 6.
    Ganguli, D., Simoncelli, E.P.: Efficient Sensory Encoding and Bayesian Inference with Heterogeneous Neural Populations. Neural Computation 26, 2103–2134 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Hyon, L., Park, J., Lee, D., Kim, H.J.: Build your own quadrotor. IEEE Robotics and Automation Magazine, 33–45 (2012)Google Scholar
  8. 8.
    Lee, J.K., Park, E.J., Robinovich, S.N.: Estimation of attitude and external acceleration using inertial sensor measurement during various dynamic conditions. IEEE Transactions on Instrumentation and Measurement 61, 2262–2273 (2012)CrossRefGoogle Scholar
  9. 9.
    Brent, R.P.: An Algorithm with Guaranteed Convergence for Finding a Zero of a Function. Algorithms for Minimization without Derivatives. Dover Books on Mathematics, pp. 47–58 (2013)Google Scholar
  10. 10.
    Axenie, C., Conradt, J.: Cortically inspired sensor fusion network for mobile robot egomotion estimation. Robotics and Autonomous Systems (2014)Google Scholar

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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