Skip to main content

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

  • Conference paper

Part of the book series: Advances in Intelligent Systems and Computing ((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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chatterjee, A., Rakshit, A., Singh, N.N.: Vision Based Autonomous Robot Navigation. SCI, vol. 455. Springer, Heidelberg (2013)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. Se, S., Lowe, D., Little, J.: Vision-based global localization and mapping for mobile robots. IEEE Trans. Robotics 21(3), 364–375 (2005)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. Irani, M., Peleg, S.: Improving resolution by image registration. Graphical Models and Image Processing 53(3), 231–239 (1991)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  19. Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Krzysztof Okarma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Okarma, K., Tecław, M., Lech, P. (2015). Application of Super-Resolution Algorithms for the Navigation of Autonomous Mobile Robots. In: Choraś, R. (eds) Image Processing & Communications Challenges 6. Advances in Intelligent Systems and Computing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-10662-5_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10662-5_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10661-8

  • Online ISBN: 978-3-319-10662-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics