Skip to main content
SpringerLink
Log in

3D Point Cloud Registration for GNSS-denied Aerial Localization over Forests

  • Conference paper
  • First Online:
Image Analysis (SCIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13885))

Included in the following conference series:

  • 447 Accesses

Abstract

This paper presents a vision-based localization approach for Unmanned Aerial Vehicles (UAVs) flying at low altitude over forested areas. We address the task as a point cloud registration problem using local 3D features with the intention to exploit the shape and relative arrangement of the trees. We propose a 3D descriptor called SHOT-N which is an adaptation of the state-of-the-art SHOT 3D descriptor. SHOT-N leverages constraints in the extrinsic parameters of a gimballed, nadir-looking camera. Extensive experiments were performed with semi-simulated point cloud data based on real aerial images over four forested areas. SHOT-N is shown to outperform two state-of-the-art 3D descriptors in terms of the rate of successful registrations. The results suggest a high potential of the approach for aerial localization over forested areas.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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.

    Keystone software, http://spacemetric.com.

References

  1. Aoki, Y., Goforth, H., Srivatsan, R.A., Lucey, S.: PointNetLK: robust & efficient point cloud registration using PointNet. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 7156–7165 (2019). https://doi.org/10.1109/CVPR.2019.00733

  2. Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975). https://doi.org/10.1145/361002.361007

    Article  MATH  Google Scholar 

  3. Campos, C., Elvira, R., Rodriguez, J.J., Montiel, J.M., Tardos, J.D.: ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap SLAM. IEEE Trans. Rob. 37(6), 1874–1890 (2021). https://doi.org/10.1109/TRO.2021.3075644

    Article  Google Scholar 

  4. Couturier, A., Akhloufi, M.A.: A review on absolute visual localization for UAV. Robot. Auton. Syst. 135, 103666 (2021). https://doi.org/10.1016/j.robot.2020.103666. https://doi.org/10.1016/j.robot.2020.103666

  5. Dovis, F.: GNSS interference threats and countermeasures. Artech House Publishers, Norwood, MA, United States (2015)

    Google Scholar 

  6. Elbaz, G., Avraham, T., Fischer, A.: 3D point cloud registration for localization using a deep neural network auto-encoder. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017, pp. 2472–2481 (2017). https://doi.org/10.1109/CVPR.2017.265

  7. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981). https://doi.org/10.1145/358669.358692

  8. Grelsson, B., Felsberg, M., Isaksson, F.: Efficient 7D aerial pose estimation. In: 2013 IEEE Workshop on Robot Vision, WORV 2013, pp. 88–95 (2013). https://doi.org/10.1109/WORV.2013.6521919

  9. Hemann, G., Singh, S., Kaess, M.: Long-range GPS-denied Aerial Inertial Navigation with LIDAR Localization. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (May), pp. 1659–1666 (2016). https://doi.org/10.1109/IROS.2016.7759267

  10. Holz, D., Ichim, A.E., Tombari, F., Rusu, R.B., Behnke, S.: Registration with the point cloud library: a modular framework for aligning in 3-D. IEEE Robot. Autom. Mag. 22(4), 110–124 (2015). https://doi.org/10.1109/MRA.2015.2432331

    Article  Google Scholar 

  11. Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions. J. Opt. Soc. Am. 4, 629–642 (1987)

    Article  Google Scholar 

  12. Huang, X., Mei, G., Zhang, J., Abbas, R.: A comprehensive survey on point cloud registration, pp. 1–17 (2021). http://arxiv.org/abs/2103.02690

  13. Jafarnia-Jahromi, A., Broumandan, A., Nielsen, J., Lachapelle, G.: GPS vulnerability to spoofing threats and a review of antispoofing techniques. Int. J. Navig. Observ. 2012, 127072 (2012). https://doi.org/10.1155/2012/127072

  14. Khan, S.Z., Mohsin, M., Iqbal, W.: On GPS spoofing of aerial platforms: a review of threats, challenges, methodologies, and future research directions. PeerJ Computer Science 7, 1–35 (2021). https://doi.org/10.7717/PEERJ-CS.507

    Article  Google Scholar 

  15. Patel, B., Barfoot, T.D., Schoellig, A.P.: Visual localization with google earth images for robust global pose estimation of UAVs. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 6491–6497 (2020). https://doi.org/10.1109/ICRA40945.2020.9196606

  16. Petrelli, A., Di Stefano, L.: On the repeatability of the local reference frame for partial shape matching. Proceedings of the IEEE International Conference on Computer Vision, pp. 2244–2251 (2011). https://doi.org/10.1109/ICCV.2011.6126503

  17. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 2017-Janua, pp. 77–85 (2017). https://doi.org/10.1109/CVPR.2017.16

  18. Rusu, R.B.: Semantic 3D object maps for everyday manipulation in human living environments. Doctoral thesis, Technische Universität München (2009). https://doi.org/10.1007/s13218-010-0059-6

  19. Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation, pp. 3212–3217 (2009). https://doi.org/10.1109/robot.2009.5152473

  20. Saiti, E., Theoharis, T.: An application independent review of multimodal 3D registration methods. Comput. Graph. (Pergamon) 91, 153–178 (2020). https://doi.org/10.1016/j.cag.2020.07.012

    Article  Google Scholar 

  21. Salti, S., Tombari, F., Di Stefano, L.: SHOT: unique signatures of histograms for surface and texture description. Comput. Vis. Image Understanding 125, 251–264 (2014). https://doi.org/10.1016/j.cviu.2014.04.011

  22. Sattler, T., et al.: Benchmarking 6DoF urban visual localization in changing conditions. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8601–8610 (2018). http://arxiv.org/abs/1707.09092

  23. Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15558-1_26

    Chapter  Google Scholar 

  24. Zhang, Z., Dai, Y., Sun, J.: Deep learning based point cloud registration: an overview. Virt. Real. Intell. Hardware 2(3), 222–246 (2020). https://doi.org/10.1016/j.vrih.2020.05.002

Download references

Acknowledgements

This work was carried out in the project “Autonomous Navigation Support from Real-Time Visual Mapping”, which was funded by Sweden’s National Strategic Innovation Programme for Aeronautics (Innovair) through Sweden’s Innovation Agency (Vinnova) (project nr. 2019-02746).

Author information

Authors and Affiliations

  1. Spacemetric AB, Sollentuna, Sweden

    Daniel Sabel & Torbjörn Westin

  2. KTH Royal Institute of Technology, Stockholm, Sweden

    Daniel Sabel & Atsuto Maki

Authors
  1. Daniel Sabel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Torbjörn Westin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atsuto Maki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Sabel .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Rikke Gade

  2. Linköping University, Linköping, Sweden

    Michael Felsberg

  3. Tampere University, Tampere, Finland

    Joni-Kristian Kämäräinen

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sabel, D., Westin, T., Maki, A. (2023). 3D Point Cloud Registration for GNSS-denied Aerial Localization over Forests. In: Gade, R., Felsberg, M., Kämäräinen, JK. (eds) Image Analysis. SCIA 2023. Lecture Notes in Computer Science, vol 13885. Springer, Cham. https://doi.org/10.1007/978-3-031-31435-3_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-31435-3_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31434-6

  • Online ISBN: 978-3-031-31435-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation