Skip to main content

Can Speedup Assist Accuracy? An On-Board GPU-Accelerated Image Georeference Method for UAVs

  • Conference paper
  • First Online:
Computer Vision Systems (ICVS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9163))

Included in the following conference series:

Abstract

This paper presents a georeferenced map extraction method, for Medium-Altitude Long-Endurance UAVs. The adopted technique of projecting world points to an image plane is a perfect candidate for a GPU implementation. The achieved high frame rate leads to a plethora of measurements even in the case of a low-power mobile processing unit. These measurements can later be combined in order to refine the output and create a more accurate result.

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 EPUB and 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

Notes

  1. 1.

    http://tinyurl.com/CUDA-AerialImgGeoref.

References

  1. Amanatiadis, A., Karakasis, E., Bampis, L., Giitsidis, T., Panagiotou, P., Sirakoulis, G.C., Gasteratos, A., Tsalides, P., Goulas, A., Yakinthos, K.: The hcuav project: electronics and software development for medium altitude remote sensing. In: IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 1–5 (2014)

    Google Scholar 

  2. Amanatiadis, A., Bampis, L., Gasteratos, A.: Accelerating single-image super-resolution polynomial regression in mobile devices. IEEE Trans. Consum. Electron. 61(1), 63–71 (2015)

    Article  Google Scholar 

  3. Choi, K., Lee, I.: A UAV-based close-range rapid aerial monitoring system for emergency responses. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 38, 247–252 (2011)

    Google Scholar 

  4. Cudaâ„¢: Nvidia corp. http://www.nvidia.com/object/cuda_home_new.html

  5. Karakasis, E.G., Bampis, L., Amanatiadis, A., Gasteratos, A., Tsalides, P.: Digital elevation model fusion using spectral methods. In: IEEE International Conference on Imaging Systems and Techniques, pp. 340–345 (2014)

    Google Scholar 

  6. Küng, O., Strecha, C., Beyeler, A., Zufferey, J.C., Floreano, D., Fua, P., Gervaix, F.: The accuracy of automatic photogrammetric techniques on ultra-light uav imagery. In: UAV-g 2011-Unmanned Aerial Vehicle in Geomatics (2011)

    Google Scholar 

  7. The lidar DEM data website. http://b5m.gipuzkoa.net/

  8. Paull, L., Thibault, C., Nagaty, A., Seto, M., Li, H.: Sensor-driven area coverage for an autonomous fixed-wing unmanned aerial vehicle. IEEE Trans. Cybern. 44(9), 1605–1618 (2014)

    Article  Google Scholar 

  9. Qi, H., Moore, J.B.: Direct kalman filtering approach for gps/ins integration. IEEE Trans. Aerosp. Electron. Syst. 38(2), 687–693 (2002)

    Article  Google Scholar 

  10. Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., Sarazzi, D.: Uav photogrammetry for mapping and 3d modeling-current status and future perspectives. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 38(1), C22 (2011)

    Google Scholar 

  11. Thomas, U., Kurz, F., Rosenbaum, D., Mueller, R., Reinartz, P.: Gpu-based orthorectification of digital airborne camera images in real time. In: Proceedings of the XXI ISPRS Congress (2008)

    Google Scholar 

  12. Wang, Y., Fevig, R., Schultz, R.R.: Super-resolution mosaicking of UAV surveillance video. In: IEEE International Conference on Image Processing, pp. 345–348 (2008)

    Google Scholar 

  13. Xiang, H., Tian, L.: Method for automatic georeferencing aerial remote sensing (rs) images from an unmanned aerial vehicle (UAV) platform. Biosyst. Eng. 108(2), 104–113 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Loukas Bampis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Bampis, L., Karakasis, E.G., Amanatiadis, A., Gasteratos, A. (2015). Can Speedup Assist Accuracy? An On-Board GPU-Accelerated Image Georeference Method for UAVs. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20904-3_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20903-6

  • Online ISBN: 978-3-319-20904-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics