Real-Time On-Board Image Processing Using an Embedded GPU for Monocular Vision-Based Navigation

  • Matías Alejandro Nitsche
  • Pablo De Cristóforis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

In this work we present a new image-based navigation method for guiding a mobile robot equipped only with a monocular camera through a naturally delimited path. The method is based on segmenting the image and classifying each super-pixel to infer a contour of navigable space. While image segmentation is a costly computation, in this case we use a low-power embedded GPU to obtain the necessary framerate in order to achieve a reactive control for the robot. Starting from an existing GPU implementation of the quick-shift segmentation algorithm, we introduce some simple optimizations which result in a speedup which makes real-time processing on board a mobile robot possible. Performed experiments using both a dataset of images and an online on-board execution of the system in an outdoor environment demonstrate the validity of this approach.

Keywords

monocular vision-based navigation image segmentation GPU 

References

  1. 1.
    DeSouza, G., Kak, A.: Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(2), 237–267 (2002)CrossRefGoogle Scholar
  2. 2.
    Bonin-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: a survey. Journal of Intelligent & Robotic Systems 53(3), 263–296 (2008)CrossRefGoogle Scholar
  3. 3.
    Ulrich, I., Nourbakhsh, I.: Appearance-based obstacle detection with monocular color vision. In: Proceedings of the National Conference on Artificial Intelligence, pp. 866–871. AAAI Press, MIT Press, Menlo Park, Cambridge (2000)Google Scholar
  4. 4.
    Santosh, D., Achar, S., Jawahar, C.: Autonomous image-based exploration for mobile robot navigation. In: IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 2717–2722. IEEE (2008)Google Scholar
  5. 5.
    Wang, Y., Fang, S., Cao, Y., Sun, H.: Image-based exploration obstacle avoidance for mobile robot. In: Control and Decision Conference, CCDC 2009, pp. 3019–3023. IEEE, Chinese (2009)CrossRefGoogle Scholar
  6. 6.
    Vedaldi, A., Soatto, S.: Quick Shift and Kernel Methods for Mode Seeking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 705–718. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)CrossRefGoogle Scholar
  8. 8.
    Fulkerson, B., Soatto, S.: Really quick shift: Image segmentation on a gpu. In: ECCV 2010 Workshop on Computer Vision on GPUs, CVGPU 2010 (2010)Google Scholar
  9. 9.
    Pedre, S., De Cristóforis, P., Caccavelli, J.: A mobile mini-robot architecture for research, education and popularization of science. Journal of Applied Computer Science Methods 2(1) (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matías Alejandro Nitsche
    • 1
  • Pablo De Cristóforis
    • 1
  1. 1.Faculty of Exact and Natural Sciences, Computer Science DepartmentBuenos Aires UniversityArgentina

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