Skip to main content

Rover Localization in Mars Helicopter Aerial Maps: Experimental Results in a Mars-Analogue Environment

  • Conference paper
  • First Online:
Proceedings of the 2018 International Symposium on Experimental Robotics (ISER 2018)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 11))

Included in the following conference series:

Abstract

In a potential Mars sample return mission, a Mars rover is required to visit previously explored and mapped environments in order to retrieve previously collected samples for subsequent return to Earth. In such a mission, the rover needs to establish its position within a provided map to safely and efficiently plan a path toward the goal locations. In this work, we study the feasibility and performance of aerial-to-ground (A2G) localization of the Mars rover by registering rover’s ground imagery to an aerial map of a Mars analogue environment. Through empirical experiments at the Jet Propulsion Laboratory’s Mars Yard, we present performance, robustness and sensitivity analysis for A2G localization in varying lighting conditions, viewing angles, terrain types and using different image feature detectors and descriptors.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Balaram, B., et al.: Mars helicopter technology demonstrator. In: 2018 AIAA Atmospheric Flight Mechanics Conference (2018)

    Google Scholar 

  2. Viswanathan, A., Pires, B.R., Huber, D.: Vision based robot localization by ground to satellite matching in GPS-denied situations. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE (2014)

    Google Scholar 

  3. Tao, Y., Muller, J.-P., Poole, W.: Automated localisation of Mars rovers using co-registered HiRISE-CTX-HRSC orthorectified images and wide baseline Navcam orthorectified mosaics. Icarus 280, 139–157 (2016)

    Article  Google Scholar 

  4. Di, K., et al.: Mars rover localization based on feature matching between ground and orbital imagery. Photogram. Eng. Remote Sens. 77(8), 781–791 (2011)

    Article  Google Scholar 

  5. Li, R., et al.: MER spirit rover localization: comparison of ground image–and orbital image–based methods and science applications. J. Geophys. Res. Planets 116 (2011)

    Google Scholar 

  6. Kirk, R.L., et al.: Ultrahigh resolution topographic mapping of Mars with MRO HiRISE stereo images: meter-scale slopes of candidate Phoenix landing sites. J. Geophys. Res. Planets. 113(E3) (2008)

    Google Scholar 

  7. Forster, C., et al.: Air-ground localization and map augmentation using monocular dense reconstruction. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2013)

    Google Scholar 

  8. Majdik, A.L., et al.: Air-ground matching: appearance-based GPS-denied urban localization of micro aerial vehicles. J. Field Robot. 32(7), 1015–1039 (2015)

    Article  Google Scholar 

  9. Majdik, A.L., Albers-Schoenberg, Y., Scaramuzza, D.: Mav urban localization from google street view data. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2013)

    Google Scholar 

  10. Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. ACM Trans. Graph. (TOG) 25(3), 835–846 (2006)

    Article  Google Scholar 

  11. Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227–2240 (2014)

    Article  Google Scholar 

  12. Silpa-Anan, C., Hartley, R.: Optimised KD-trees for fast image descriptor matching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. IEEE (2008)

    Google Scholar 

  13. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in Computer Vision, pp. 726–740 (1987)

    Google Scholar 

  14. Fox, D., et al.: Particle filters for mobile robot localization. In: Doucet, A., de Freitas, N., Gordon, N. (eds.) Sequential Monte Carlo Methods in Practice, pp. 401–428. Springer, New York (2001)

    Chapter  Google Scholar 

  15. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  16. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) European Conference on Computer Vision, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, no. 50 (1988)

    Google Scholar 

  18. Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2 (2005)

    Google Scholar 

  19. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV). IEEE (2011)

    Google Scholar 

  20. Agha-Mohammadi, A., Chakravorty, S., Amato, N.M.: FIRM: sampling-based feedback motion-planning under motion uncertainty and imperfect measurements. Int. J. Robot. Res. 33(2), 268–304 (2014)

    Article  Google Scholar 

  21. Otsu, K., Agha-Mohammadi, A., Paton, M.: Where to look? Predictive perception with applications to planetary exploration. IEEE Robot. Autom. Lett. 3(2), 635–642 (2018)

    Article  Google Scholar 

Download references

Acknowledgement

The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. ©2018 California Institute of Technology.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kamak Ebadi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ebadi, K., Agha-Mohammadi, AA. (2020). Rover Localization in Mars Helicopter Aerial Maps: Experimental Results in a Mars-Analogue Environment. In: Xiao, J., Kröger, T., Khatib, O. (eds) Proceedings of the 2018 International Symposium on Experimental Robotics. ISER 2018. Springer Proceedings in Advanced Robotics, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-33950-0_7

Download citation

Publish with us

Policies and ethics