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Application of Super-Resolution Algorithms for the Navigation of Autonomous Mobile Robots

  • Krzysztof Okarma
  • Mateusz Tecław
  • Piotr Lech
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 313)

Abstract

In the paper the idea of using the super-resolution algorithms for the self-localization and vision based navigation of autonomous mobile robots is discussed. Since such task is often limited both by the limited resolution of the mounted video camera as well as the available computational resources, a typical approach for video based navigation of mobile robots, similarly as many small flying robots (drones), is using low resolution cameras equipped with average class lenses. The images captured by such video system should be further processed in order to extract the data useful for real-time control of robot’s motion. In some simplified systems such navigation, especially in the within an enclosed environment (interior), is based on the edge and corner detection and binary image analysis, which could be troublesome for low resolution images.

Considering the possibilities of obtaining higher resolution images from low resolution image sequences, the accuracy of such edge and corner detections may be improved by the application of super-resolution algorithms. In order to verify the usefulness of such approach some experiments have been conducted based on the processing of the captured sequences of the HD images further downsampled and reconstructed using the super-resolution algorithms. Obtained results have been reported in the last section of the paper.

Keywords

Mobile Robot Scale Invariant Feature Transform Autonomous Mobile Robot Mobile Robot Navigation Bicubic Interpolation 
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.

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References

  1. 1.
    Chatterjee, A., Rakshit, A., Singh, N.N.: Vision Based Autonomous Robot Navigation. SCI, vol. 455. Springer, Heidelberg (2013)zbMATHGoogle Scholar
  2. 2.
    DeSouza, G.N., Kak, A.C.: Vision for mobile robot navigation: A survey. IEEE Trans. Pattern Anal. Machine Intell. 24(2), 237–267 (2002)CrossRefGoogle Scholar
  3. 3.
    Dalgleish, F.R., Tetlow, S.W., Allwood, R.L.: Vision-based navigation of unmanned underwater vehicles: A survey. Part I: vision based cable-, pipeline- and fish tracking. Proc. Inst. Marine Engineering, Science and Technology. Part B, Journal of Marine Design and Operations B(7), 51–56 (2004)Google Scholar
  4. 4.
    Dalgleish, F.R., Tetlow, S.W., Allwood, R.L.: Vision-based navigation of unmanned underwater vehicles: A survey. Part II: vision based station keeping and positioning. Proc. Inst. Marine Engineering, Science and Technology. Part B, Journal of Marine Design and Operations B(8), 13–19 (2004)Google Scholar
  5. 5.
    Chang, C.-K., Siagian, C., Itti, L.: Mobile robot vision navigation & localization using Gist and Saliency. In: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), pp. 4147–4154 (2010)Google Scholar
  6. 6.
    Se, S., Lowe, D., Little, J.: Vision-based global localization and mapping for mobile robots. IEEE Trans. Robotics 21(3), 364–375 (2005)CrossRefGoogle Scholar
  7. 7.
    Bonon-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: A survey. Journal of Intelligent and Robotic Systems 53(3), 263–296 (2008)CrossRefGoogle Scholar
  8. 8.
    Vandewalle, P., Süsstrunk, S., Vetterli, M.: A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP Journal on Applied Signal Processing, Article ID 71459, 14 (2006)Google Scholar
  9. 9.
    Lucchese, L., Cortelazzo, G.M.: A noise-robust frequency domain technique for estimating planar roto-translations. IEEE Trans. Signal Process. 48(6), 1769–1786 (2000)CrossRefGoogle Scholar
  10. 10.
    Keren, D., Peleg, S., Brada, R.: Image sequence enhancement using sub-pixel displacement. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 742–746 (1988)Google Scholar
  11. 11.
    Irani, M., Peleg, S.: Super resolution from image sequences. In: Proc. IEEE Int. Conf. Pattern Recognition, vol. 2, pp. 115–120 (1990)Google Scholar
  12. 12.
    Irani, M., Peleg, S.: Improving resolution by image registration. Graphical Models and Image Processing 53(3), 231–239 (1991)CrossRefGoogle Scholar
  13. 13.
    Chatterjee, P., Mukherjee, S., Chaudhuri, S., Seetharaman, G.: Application of Papoulis-Gerchberg method in image super-resolution and inpainting. Comput. J. 52(1), 80–89 (2009)CrossRefGoogle Scholar
  14. 14.
    Zomet, A., Rav-Acha, A., Peleg, S.: Robust super-resolution. In: Proc. Int. Conf. Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 645–650 (2001)Google Scholar
  15. 15.
    Pham, T.Q., van Vliet, L.J., Schutte, K.: Robust fusion of irregularly sampled data using adaptive normalized convolution. EURASIP Journal on Applied Signal Processing, Article ID 83268, 12 p. (2006)Google Scholar
  16. 16.
    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error measurement to Structural Similarity. IEEE Trans. Image Proc. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  17. 17.
    Li, C., Bovik, A.: Three-component weighted Structural Similarity index. In: Proc. SPIE. Image Quality and System Performance VI, vol. 7242, p. 72420Q (2009)Google Scholar
  18. 18.
    Khursheed, K., Imran, M., Ahmad, N., O’Nils, M.: Bi-level video codec for machine vision embedded applications. Elektronika Ir Elektrotechnika 19(8), 93–96 (2013)CrossRefGoogle Scholar
  19. 19.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering Department of Signal Processing and Multimedia EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland

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