Advertisement

Depth Estimation during Fixational Head Movements in a Humanoid Robot

  • Marco Antonelli
  • Angel P. del Pobil
  • Michele Rucci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7963)

Abstract

Under natural viewing conditions, humans are not aware of continually performing small head and eye movements in the periods in between voluntary relocations of gaze. It has been recently shown that these fixational head movements provide useful depth information in the form of parallax. Here, we replicate this coordinated head and eye movements in a humanoid robot and describe a method for extracting the resulting depth information. Proprioceptive signals are interpreted by means of a kinematic model of the robot to compute the velocity of the camera. The resulting signal is then optimally integrated with the optic flow to estimate depth in the scene. We present the results of simulations which validate the proposed approach.

Keywords

Apparent Motion Humanoid Robot Depth Estimation Texture Region Motion Parallax 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aloimonos, Y., Duric, Z.: Estimating the heading direction using normal flow. International Journal of Computer Vision 13(1), 33–56 (1994)CrossRefGoogle Scholar
  2. 2.
    Ayache, N.: Artificial vision for mobile robots - stereo vision and multisensory perception. MIT Press (1991)Google Scholar
  3. 3.
    Aytekin, M., Rucci, M.: Motion parallax from microscopic head movements during visual fixation. Vision Research (August 2012)Google Scholar
  4. 4.
    Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–77 (1994)CrossRefGoogle Scholar
  5. 5.
    Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: Monoslam: Real-time single camera slam. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 1052–1067 (2007)CrossRefGoogle Scholar
  6. 6.
    Diankov, R., Kuffner, J.: Openrave: A planning architecture for autonomous robotics. Robotics Institute, Pittsburgh, PA, Tech. Rep. CMU-RI-TR-08-34 (2008)Google Scholar
  7. 7.
    Faugeras, O.D., Luong, Q.T., Papadopoulo, T.: The geometry of multiple images - the laws that govern the formation of multiple images of a scene and some of their applications. MIT Press (2001)Google Scholar
  8. 8.
    Higgins, L.H.C., Prazdny, K.: The Interpretation of a Moving Retinal Image. Proceedings of the Royal Society of London. Series B, Biological Sciences (1934-1990) 208(1173), 385–397 (1980)Google Scholar
  9. 9.
    Kuang, X., Gibson, M., Shi, B.E., Rucci, M.: Active vision during coordinated head/eye movements in a humanoid robot. IEEE Transactions on Robotics PP(99), 1–8 (2012)Google Scholar
  10. 10.
    Matthies, L., Kanade, T., Szeliski, R.: Kalman filter-based algorithms for estimating depth from image sequences. International Journal of Computer Vision 3(3), 209–238 (1989)CrossRefGoogle Scholar
  11. 11.
    Ramachandran, M., Veeraraghavan, A., Chellappa, R.: A fast bilinear structure from motion algorithm using a video sequence and inertial sensors. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 186–193 (2011)CrossRefGoogle Scholar
  12. 12.
    Rogers, B., Graham, M.: Motion parallax as an independent cue for depth perception. Perception 8(2), 125–134 (1979)CrossRefGoogle Scholar
  13. 13.
    Sandini, G., Tistarelli, M.: Active tracking strategy for monocular depth inference over multiple frames. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), 13–27 (1990), doi:10.1109/34.41380CrossRefGoogle Scholar
  14. 14.
    Santini, F., Rucci, M.: Active estimation of distance in a robotic system that replicates human eye movement. Robotics and Autonomous Systems 55(2), 107–121 (2007)CrossRefGoogle Scholar
  15. 15.
    Simoncelli, E., Adelson, E., Heeger, D.: Probability distributions of optical flow. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1991, pp. 310–315. IEEE (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marco Antonelli
    • 1
  • Angel P. del Pobil
    • 1
  • Michele Rucci
    • 2
  1. 1.Robotic Intelligence LabUniversitat Jaume ICastellónSpain
  2. 2.Department of Psychology and Graduate Program in NeuroscienceBoston UniversityBostonUSA

Personalised recommendations