Journal of Intelligent & Robotic Systems

, Volume 63, Issue 3–4, pp 417–446 | Cite as

Real-Time 3D Stereo Tracking and Localizing of Spherical Objects with the iCub Robotic Platform

  • Nicola Greggio
  • Alexandre Bernardino
  • Cecilia Laschi
  • José Santos-Victor
  • Paolo Dario


Visual pattern recognition is a basic capability of many species in nature. The skill of visually recognizing and distinguishing different objects in the surrounding environment gives rise to the development of sensory-motor maps in the brain, with the consequent capability of object reaching and manipulation. This paper presents the implementation of a real-time tracking algorithm for following and evaluating the 3D position of a generic spatial object. The key issue of our approach is the development of a new algorithm for pattern recognition in machine vision, the Least Constrained Square-Fitting of Ellipses (LCSE), which improves the state of the art ellipse fitting procedures. It is a robust and direct method for the least-square fitting of ellipses to scattered data. In this work we applied it to the iCub humanoid robotics platform simulator and real robot. We used it as a base for a circular object localization within the 3D surrounding space. We compared its performance with the Hough Transform and the state of the art ellipse fitting algorithms, in terms of robustness (succes/failure in the object detection) and fitting precision. Our experiments involve robustness against noise, occlusion, and computational complexities analyses.


Machine vision Pattern recognition Least-square fitting 3D tracking Humanoid robotics 


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Nicola Greggio
    • 1
    • 2
  • Alexandre Bernardino
    • 2
  • Cecilia Laschi
    • 1
  • José Santos-Victor
    • 2
  • Paolo Dario
    • 3
  1. 1.ARTS Lab—Scuola Superiore S.Anna, Polo S.Anna ValderaPontederaItaly
  2. 2.Instituto de Sistemas e RobóticaInstituto Superior TécnicoLisboaPortugal
  3. 3.ARTS & CRIM Lab—Scuola Superiore S.Anna, Polo S.Anna ValderaPontederaItaly

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