Skip to main content
SpringerLink
Log in

Monocular 6-DoF Pose Estimation for Non-cooperative Spacecrafts Using Riemannian Regression Network

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13801))

Included in the following conference series:

Abstract

As it is closely related to spacecraft in-orbit servicing, space debris removal, and other proximity operations, on-board 6-DoF pose estimation of non-cooperative spacecraft is an essential task in on-going and planned space mission design. Spaceborne navigation cameras, on the other hand, face the challenges of rapidly changing light conditions, low signal-to-noise ratio in space imagery, and real-time demand, which are not present in terrestrial applications. To address this issue, we propose an EfficientNet-based method that regresses position and orientation. The rotation regression loss function is converted into the Riemannian geodesic distance between the predicted values and ground-truth labels, which speeds up the rotation regression and limits the error to a desirable range. Moreover, several data augmentation techniques were proposed to address the overfitting issue caused by the small scale of the spacecraft dataset in this paper. In the SPARK2022 challenge, our method achieves state-of-the-art pose estimation accuracy.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

References

  1. Cai, M., Shen, C., Reid, I.D.: A hybrid probabilistic model for camera relocalization. In: BMVC (2018)

    Google Scholar 

  2. D’Amico, S., Benn, M., Jørgensen, J.L.: Pose estimation of an uncooperative spacecraft from actual space imagery. Int. J. Space Sci. Eng. 2(2), 171–189 (2014)

    Article  Google Scholar 

  3. Forshaw, J.I., et al.: RemoveDEBRIS: an in-orbit active debris removal demonstration mission. Acta Astronaut. 127, 448–463 (2016). https://doi.org/10.1016/j.actaastro.2016.06.018

    Article  Google Scholar 

  4. Henshaw, C.G.: The DARPA phoenix spacecraft servicing program: overview and plans for risk reduction. In: International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS). European Space Agency (2014)

    Google Scholar 

  5. Hu, Y., Speierer, S., Jakob, W., Fua, P., Salzmann, M.: Wide-depth-range 6d object pose estimation in space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15870–15879 (2021)

    Google Scholar 

  6. Kendall, A., Cipolla, R.: Modelling uncertainty in deep learning for camera relocalization. In: 2016 IEEE iInternational Conference on Robotics and Automation (ICRA), pp. 4762–4769. IEEE (2016)

    Google Scholar 

  7. Kendall, A., Cipolla, R.: Geometric loss functions for camera pose regression with deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5974–5983 (2017)

    Google Scholar 

  8. Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for Real-Time 6-DOF Camera Relocalization. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2938–2946. IEEE, Santiago, Chile (December 2015). DOIurl10.1109/ICCV.2015.336

    Google Scholar 

  9. Kisantal, M., Sharma, S., Park, T.H., Izzo, D., Märtens, M., D’Amico, S.: Satellite pose estimation challenge: dataset, competition design, and results. IEEE Trans. Aerosp. Electron. Syst. 56(5), 4083–4098 (2020)

    Article  Google Scholar 

  10. Levinson, J., et al.: An analysis of SVD for deep rotation estimation. Adv. Neural. Inf. Process. Syst. 33, 22554–22565 (2020)

    Google Scholar 

  11. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

  12. Mahendran, S., Lu, M.Y., Ali, H., Vidal, R.: Monocular object orientation estimation using Riemannian regression and classification networks (2018). 10.48550/ARXIV.1807.07226

    Google Scholar 

  13. Park, T.H., Märtens, M., Lecuyer, G., Izzo, D., D’Amico, S.: SPEED+: next-generation dataset for spacecraft pose estimation across domain gap. arXiv preprint arXiv:2110.03101 (2021)

  14. Proença, P.F., Gao, Y.: Deep learning for spacecraft pose estimation from photorealistic rendering. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 6007–6013. IEEE (2020)

    Google Scholar 

  15. Rathinam, A., et al.: SPARK 2022 dataset : spacecraft detection and trajectory estimation (June 2022). https://doi.org/10.5281/ZENODO.6599762, type: dataset

  16. Reed, B.B., Smith, R.C., Naasz, B.J., Pellegrino, J.F., Bacon, C.E.: The Restore-L Servicing Mission. In: AIAA SPACE 2016. American Institute of Aeronautics and Astronautics, Long Beach, California (September 2016). https://doi.org/10.2514/6.2016-5478

  17. Sharma, S., Beierle, C., D’Amico, S.: Pose estimation for non-cooperative spacecraft rendezvous using convolutional neural networks. In: 2018 IEEE Aerospace Conference, pp. 1–12. IEEE (2018)

    Google Scholar 

  18. Sharma, S., D’Amico, S.: Neural network-based pose estimation for noncooperative spacecraft rendezvous. IEEE Trans. Aerosp. Electron. Syst. 56(6), 4638–4658 (2020). https://doi.org/10.1109/TAES.2020.2999148

    Article  Google Scholar 

  19. Sharma, S., Ventura, J., D’Amico, S.: Robust model-based monocular pose initialization for noncooperative spacecraft rendezvous. J. Spacecr. Rocket. 55(6), 1414–1429 (2018)

    Article  Google Scholar 

  20. Shavit, Y., Ferens, R., Keller, Y.: Learning multi-scene absolute pose regression with transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2733–2742 (2021)

    Google Scholar 

  21. Shi, J.F., Ulrich, S., Ruel, S.: Spacecraft pose estimation using principal component analysis and a monocular camera. In: AIAA Guidance, Navigation, and Control Conference, p. 1034 (2017)

    Google Scholar 

  22. Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)

    Google Scholar 

  23. Tan, M., Le, Q.: Efficientnetv2: Smaller models and faster training. In: International Conference on Machine Learning. pp. 10096–10106. PMLR (2021)

    Google Scholar 

  24. Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., Cremers, D.: Image-based localization using LSTMS for structured feature correlation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 627–637 (2017)

    Google Scholar 

  25. Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5745–5753 (2019)

    Google Scholar 

  26. Zhu, X., Song, X., Chen, X., Lu, H.: Flying spacecraft detection with the earth as the background based on superpixels clustering. In: 2015 IEEE International Conference on Information and Automation, pp. 518–523. IEEE, Lijiang, China (August 2015). https://doi.org/10.1109/ICInfA.2015.7279342

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant No: U20B2054).

Author information

Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, 200240, China

    Sunhao Chu, Yuxiao Duan & Shufan Wu

  2. Julius-Maximilians-University Würzburg, Würzburg, Germany

    Klaus Schilling

Authors
  1. Sunhao Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuxiao Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Klaus Schilling
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shufan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sunhao Chu .

Editor information

Editors and Affiliations

  1. IBM Research AI and MIT-IBM Watson AI Lab, Haifa, Israel

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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

Chu, S., Duan, Y., Schilling, K., Wu, S. (2023). Monocular 6-DoF Pose Estimation for Non-cooperative Spacecrafts Using Riemannian Regression Network. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13801. Springer, Cham. https://doi.org/10.1007/978-3-031-25056-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-25056-9_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25055-2

  • Online ISBN: 978-3-031-25056-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation