Visual Memory Construction for Autonomous Humanoid Robot Navigation

  • A. López-MartínezEmail author
  • F. J. Cuevas
  • J. V. Sosa-Balderas
Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 233)


A visual memory (VM) is a topological map that represents an environment as a direct graph of key images. Thus, visual information acquired from cameras onboard the robot are the only data to construct the map. This work presents the construction of a VM suited for the humanoid robot navigation framework. Additionally, a genetic algorithm that estimates the epipolar geometry is proposed to tackle the problem of image matching used within the VM construction process. Experimental results using a humanoid robot dataset are presented to validate the efficacy of our approach. Further, the solution for image matching based on the proposed genetic algorithm was compared with RANSAC.


  1. 1.
    Z. Chen, S.T. Birchfield, Qualitative vision-based mobile robot navigation, in Proceeding of ICRA (2006), pp. 2686–2692Google Scholar
  2. 2.
    O. Booij, Z. Terwijn, Z. Zivkovic et al., Navigation using an appearance-based topological map, in Proceeding of ICRA (2007), pp. 3927–3932Google Scholar
  3. 3.
    J. Ido, Y. Shimizu, Y. Matsumoto et al., Indoor navigation for a humanoid robot using a view sequence. Int. J. Robot. Res. 28(2), 315–325 (2009)CrossRefGoogle Scholar
  4. 4.
    A. Diosi, S. Segvic, A. Remazeilles et al., Experimental evaluation of autonomous driving based on visual memory and image-based visual servoing. IEEE Trans. Intell. Transp. Syst. 12(3), 833–870 (2011)Google Scholar
  5. 5.
    H.M. Becerra, C. Sagues, Y. Mezouar et al., Visual navigation of wheeled mobile robots using direct feedback of a geometric constraint. Auton. Robots 37(2), 137–156 (2014)CrossRefGoogle Scholar
  6. 6.
    J. Courbon, Y. Mezouar, P. Martinet, Indoor navigation of a non-holonomic mobile robot using a visual memory. Auton. Robots 25, 253–266 (2008)CrossRefGoogle Scholar
  7. 7.
    J. Courbon, Y. Mezouar, P. Martinet, Autonomous navigation of vehicles from a visual memory using a generic camera model. IEEE Trans. Intell. Transp. Syst. 10(3), 392–402 (2009)CrossRefGoogle Scholar
  8. 8.
    N.G. Aldana-Murillo, J.B. Hayet, H.M. Becerra, Comparison of local descriptors for humanoid robots localization using a visual bag of words approach. Intell. Autom. Soft Co., 1–11 (2017)Google Scholar
  9. 9.
    J. Delfin, H.M. Becerra, G. Arechavaleta, Humanoid localization and navigation using a visual memory, in International Conference on Humanoid Robots (Humanoids) (2016), pp. 725–731Google Scholar
  10. 10.
    J. Delfin, H.M. Becerra, G. Arechavaleta, Humanoid navigation using a visual memory with obstacle avoidance. Rob. Auton. Sys. 109, 109–124 (2018)CrossRefGoogle Scholar
  11. 11.
    E. Ovalle, Generación de una memoria visual para la navegación autónoma de un robot humanoide (2016)Google Scholar
  12. 12.
    R.I. Hartley, A. Zisserman, Multiple View Geometry in Computer Visionm, 2nd edn. (Cambridge University Press, 2004)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • A. López-Martínez
    • 1
    Email author
  • F. J. Cuevas
    • 1
  • J. V. Sosa-Balderas
    • 1
  1. 1.Optical Metrology, Centro de Investigaciones en Óptica A. C.LeónMexico

Personalised recommendations