Skip to main content

Fuzzy-Based Automatic Landmark Recognition in Aerial Images Using ORB for Aerial Auto-localization

  • Conference paper
Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

Included in the following conference series:

Abstract

Aerial navigation based on computer vision is a subject in constant development. It aims to identify the localization of an Unmanned Aerial Vehicle based on aerial images captured during flight. This paper employs a fuzzy-based application to identify landmarks, using the ORB algorithm, which uses descriptors for the neighborhood of keypoints to identify specific registered objects on a scene. In Addition to the keypoint matching from ORB, a fuzzy system is used to analyze each match, in order to guarantee the proper identification of the landmark.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Suzuki, T., Amano, Y., Hashizume, T.: Development of a SIFT based monocular EKF-SLAM algorithm for a small unmanned aerial vehicle. In: SICE Annual Conference, pp. 1656–1659 (2011)

    Google Scholar 

  2. Blumenau, A., Ishak, A., Limone, B., Mintz, Z., Russell, C., Sudol, A., Linton, R., Lai, L., Padir, T., Van Hook, R.: Design and implementation of an intelligent portable aerial surveillance system (ipass). In: Technologies for Practical Robot Applications (TePRA), pp. 1–6 (2013)

    Google Scholar 

  3. Suzuki, T., Amano, Y., Hashizume, T.: Vision based localization of a small UAV for generating a large mosaic image. In: SICE Annual Conference, pp. 2960–2964 (2010)

    Google Scholar 

  4. Liu, Y.C., Dai, Q.H.: Vision aided unmanned aerial vehicle autonomy: An overview. In: Image and Signal Processing (CISP), pp. 417–421 (2010)

    Google Scholar 

  5. Zhao, X., Fei, Q., Geng, Q.: Vision based ground target tracking for rotor uav. In: Control and Automation (ICCA), pp. 1907–1911 (2013)

    Google Scholar 

  6. Dumble, S., Gibbens, P.: Efficient terrain-aided visual horizon based attitude estimation and localization. Journal of Inteligent and Robotic Systems (2014)

    Google Scholar 

  7. Rady, S., Kandil, A., Badreddin, E.: A hybrid localization approach for UAV in GPS denied areas. In: International Symposium on System Integration (SII), pp. 1269–1274 (2011)

    Google Scholar 

  8. Guan, X., Bai, H.: A GPU accelerated real-time self-contained visual navigation system for UAVs. In: International Conference on Information and Automation (ICIA), pp. 578–581 (2012)

    Google Scholar 

  9. Bodensteiner, C., Hübner, W., Jüngling, K., Solbrig, P., Arens, M.: Monocular camera trajectory optimization using LiDAR data. In: Computer Vision Workshops (ICCV), pp. 2018–2025 (2011)

    Google Scholar 

  10. Lee, L., An, S., Oh, S.: Effective visual salient object landmark extraction and recognition. IEEE Systems, Man, and Cybernetics, 1351–1357 (2011)

    Google Scholar 

  11. Kwon, H., Sharma, R., Yoder, J., Pack, D.: Robust mobile ground target localization using ground image features with UAV position compensation techniques. In: International Conference on Control, Automation and Systems (ICCAS), pp. 454–458 (2012)

    Google Scholar 

  12. Rublee, E., Garage, W., Park, M., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: International Conference on Computer Vision (ICCV), pp. 2564–2571 (2011)

    Google Scholar 

  13. Zimmermann, J.: Fuzzy set theory and its applications, 4th edn. (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Filho, P.S., Rodrigues, M., Saotome, O., Shiguemori, E.H. (2014). Fuzzy-Based Automatic Landmark Recognition in Aerial Images Using ORB for Aerial Auto-localization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14249-4_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics