Skip to main content
SpringerLink
Log in

Robust GNSS-denied localization for UAV using particle filter and visual odometry

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Conventional autonomous unmanned air vehicle (UAV) autopilot systems use global navigation satellite system (GNSS) signal for navigation. However, autopilot systems fail to navigate due to lost or jammed GNSS signal. To solve this problem, information from other sensors such as optical sensors are used. Monocular simultaneous localization and mapping (SLAM) algorithms have been developed over the last few years and achieved state-of-the-art accuracy (e.g., visual SLAM algorithms achieve centimeter-level precision in an in-doors environment). Also, map matching localization approaches are used for UAV localization relatively to imagery from static maps such as Google Maps. Unfortunately, the accuracy and robustness of these algorithms are very dependent on up-to-date maps. The purpose of this research is to improve the accuracy and robustness of map-relative particle filter-based localization using a downward-facing optical camera mounted on an autonomous aircraft. This research shows how image similarity to likelihood conversion function impacts the results of particle filter localization algorithm. Two parametric image similarity to likelihood conversion functions (logistic and rectifying) are proposed. A dataset of simulated aerial imagery is used for experiments. The experiment results are shown, that the particle filter localization algorithm using the logistic function was able to surpass the accuracy of state-of-the-art ORB-SLAM2 algorithm by 2.6 times. The algorithm is shown to be able to navigate using up-to-date maps more accurately and with an average decrease in precision by 30% using out-of-date maps.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://github.com/jureviciusr/particle-match

  2. https://doi.org/10.5281/zenodo.1211729

References

  1. Golden, J.P.: Terrain contour matching (tercom): a cruise missile guidance aid. In: Image Processing for Missile Guidance, vol. 238, pp. 10–19. International Society for Optics and Photonics (1980)

  2. Kendoul, F.: Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J. Field Robot. 29(2), 315–378 (2012)

    Article  Google Scholar 

  3. Weiss, S., Scaramuzza, D., Siegwart, R.: Monocular-slam-based navigation for autonomous micro helicopters in gps-denied environments. J. Field Robot. 28(6), 854–874 (2011)

    Article  Google Scholar 

  4. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: Orb-slam: a versatile and accurate monocular slam system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)

    Article  Google Scholar 

  5. Engel, J., Schöps, T., Cremers, D.: Lsd-slam: large-scale direct monocular slam. In: European Conference on Computer Vision, pp. 834–849. Springer (2014)

  6. Shan, M., Wang, F., Lin, F., Gao, Z., Tang, Y.Z., Chen, B.M.: Google map aided visual navigation for UAVS in GPS-denied environment. In: 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 114–119. IEEE (2015)

  7. Pearson, K.: Mathematical contributions to the theory of evolution. iii. regression, heredity, and panmixia. Philos. Trans. R. Soc. Lond. Ser. A Contain. Pap. Math. Phys. Character 187, 253–318 (1896)

    Article  Google Scholar 

  8. Fox, D.: KLD-sampling: adaptive particle filters. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14 (NIPS), pp. 713–720. MIT Press, Cambridge (2002)

    Google Scholar 

  9. Jurevicius, R., Marcinkevicius, V., Taujanskas, V.: Comparison of image similarity functions and sampling algorithms in vision-based particle filter for UAV localization. In: Proceedings of the International Congress in Computer Science: Information Systems and Technologies (CSIST’16), pp. 109–114. BSU, Minsk (2016)

  10. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press (2005)

  11. Forster, C., Pizzoli, M., Scaramuzza, D.: Svo: fast semi-direct monocular visual odometry. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 15–22. IEEE (2014)

  12. Mur-Artal, R., Tardós, J.D.: Orb-slam2: an open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017)

    Article  Google Scholar 

  13. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–257. IEEE (2011)

  14. NAIP Digital Ortho Photo Image. Salt Lake City, Utah: USDA-FSA-APFO Aerial Photography Field Office, 2009, 2010, 2012, 2014

  15. Nasrabadi, N.M.: Pattern recognition and machine learning. J. Electron. Imaging 16(4), 049901 (2007)

    Article  MathSciNet  Google Scholar 

  16. Richards, F.: A flexible growth function for empirical use. J. Exp. Bot. 10(2), 290–301 (1959)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Data Science and Digital Technologies, Vilnius University, Akademijos str. 4, 08663, Vilnius, Lithuania

    Rokas Jurevičius & Virginijus Marcinkevičius

  2. Faculty of Mathematics and Informatics, Vilnius University, Didlaukio g. 47, 08303, Vilnius, Lithuania

    Justinas Šeibokas

Authors
  1. Rokas Jurevičius
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Virginijus Marcinkevičius
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Justinas Šeibokas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rokas Jurevičius.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jurevičius, R., Marcinkevičius, V. & Šeibokas, J. Robust GNSS-denied localization for UAV using particle filter and visual odometry. Machine Vision and Applications 30, 1181–1190 (2019). https://doi.org/10.1007/s00138-019-01046-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-019-01046-4

Keywords

Search

Navigation