Advertisement

SPARTAN/SEXTANT/COMPASS: Advancing Space Rover Vision via Reconfigurable Platforms

  • George LentarisEmail author
  • Ioannis Stamoulias
  • Dionysios Diamantopoulos
  • Konstantinos Maragos
  • Kostas  Siozios
  • Dimitrios Soudris
  • Marcos Aviles Rodrigalvarez
  • Manolis Lourakis
  • Xenophon Zabulis
  • Ioannis Kostavelis
  • Lazaros Nalpantidis
  • Evangelos Boukas
  • Antonios Gasteratos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9040)

Abstract

Targeting enhanced navigational speed and autonomy for the space exploration rovers, researchers are gradually turning to reconfigurable computing and FPGAs. High-density space-grade FPGAs will enable the acceleration of high-complexity computer vision algorithms for improving the localization and mapping functions of the future Mars rovers. In the projects SPARTAN/SEXTANT/COMPASS of the European Space Agency, we study the potential use of FPGAs for implementing a variety of stereo correspondence, feature extraction, and visual odometry algorithms, all with distinct cost-performance tradeoffs. The most efficient of the developed accelerators will assist the slow space-grade CPU in completing the visual tasks of the rover faster, by one order of magnitude, and thus, will allow the future missions to visit larger areas on Mars. Our work bases on a custom HW/SW co-design methodology, parallel architecture design, optimization techniques, tradeoff analysis, and system tuning with Martian-like scenarios.

Keywords

Space rovers Computer vision Stereo correspondence Feature extraction Visual odometry HW/SW co-design FPGA acceleration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Matthies, L., Maimone, M., Johnson, A., Cheng, Y., Willson, R., Villalpando, C., Goldberg, S., Huertas, A., Stein, A., Angelova, A.: Computer Vision on Mars. International Journal of Computer Vision 75(1), 67–92 (2007)CrossRefGoogle Scholar
  2. 2.
    Howard, T.M., Morfopoulos, A., Morrison, J., Kuwata, Y., Villalpando, C., Matthies, L., McHenry, M.: Enabling continuous planetary rover navigation through FPGA stereo and visual odometry. In: IEEE Aerospace Conference (2012)Google Scholar
  3. 3.
    Johnson, A., Goldberg, S., Cheng, Y., Matthies, L.: Robust and efficient stereo feature tracking for visual odometry. In: IEEE International Conference on Robotics and Automation, ICRA 2008, pp. 39–46, May 2008Google Scholar
  4. 4.
    Poulakis, P., Joudrier, L., Wailliez, S., Kapellos, K.: 3DROV: a planetary rover system design, simulation and verification tool. In: Proc. of the 10th Int’l Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS-08) (2008)Google Scholar
  5. 5.
    Woods, M., Shaw, A., Tidey, E., Pham, B.V., Artan, U., Maddison, B., Cross, G.: SEEKER-autonomous long range rover navigation for remote exploration. In: Int’l Symp. on Artificial Intelligence, Robotics and Automation in Space, Italy (2012)Google Scholar
  6. 6.
    Furgale, P., Carle, P., Enright, J., Barfoot, T.D.: The Devon Island Rover Navigation Dataset. Int’l Journal Robotics Research 31(6), 707–713 (2012)CrossRefGoogle Scholar
  7. 7.
    George, L., Diamantopoulos, D., Siozios, K., Soudris, D., Rodrigalvarez, M.A.: Hardware implementation of stereo correspondence algorithm for the exomars mission. In: 2012 22nd International Conference on Field Programmable Logic and Applications (FPL), pp. 667–670. IEEE (2012)Google Scholar
  8. 8.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer (2010)Google Scholar
  9. 9.
    Scaramuzza, D., Fraundorfer, F.: Visual odometry [tutorial]. IEEE Robot. Automat. Mag. 18(4), 80–92 (2011)CrossRefGoogle Scholar
  10. 10.
    Bay, H., Ess, E., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Comp. Vision and Image Understanding 110(3), 346–359 (2008)CrossRefGoogle Scholar
  11. 11.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  12. 12.
    Rosten, E., Porter, R., Drummond, T.: Faster and better: A machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2010)CrossRefGoogle Scholar
  13. 13.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int’l Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • George Lentaris
    • 1
    Email author
  • Ioannis Stamoulias
    • 1
  • Dionysios Diamantopoulos
    • 1
  • Konstantinos Maragos
    • 1
  • Kostas  Siozios
    • 1
  • Dimitrios Soudris
    • 1
  • Marcos Aviles Rodrigalvarez
    • 2
  • Manolis Lourakis
    • 3
  • Xenophon Zabulis
    • 3
  • Ioannis Kostavelis
    • 4
  • Lazaros Nalpantidis
    • 4
  • Evangelos Boukas
    • 4
  • Antonios Gasteratos
    • 4
  1. 1.School of Electrical and Computer EngineeringNTUAAthensGreece
  2. 2.Advanced Space Systems and TechnologiesGMVTres CantosSpain
  3. 3.Institute of Computer ScienceFORTHHeraklionGreece
  4. 4.Department of Production and Management EngineeringDUTHXanthiGreece

Personalised recommendations