Advertisement

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

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

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.

Notes

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.

References

  1. 1.
    Balaram, B., et al.: Mars helicopter technology demonstrator. In: 2018 AIAA Atmospheric Flight Mechanics Conference (2018)Google Scholar
  2. 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. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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. 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. 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. 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)CrossRefGoogle Scholar
  9. 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. 10.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. ACM Trans. Graph. (TOG) 25(3), 835–846 (2006)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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. 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. 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)CrossRefGoogle Scholar
  15. 15.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 17.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, no. 50 (1988)Google Scholar
  18. 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. 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. 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)CrossRefGoogle Scholar
  21. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.NASA Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA

Personalised recommendations