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Multi-Sensor Fusion Using Evidential SLAM for Navigating a Probe through Deep Ice

  • Joachim Clemens
  • Thomas Reineking
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8764)

Abstract

We present an evidential multi-sensor fusion approach for navigating a maneuverable ice probe designed for extraterrestrial sample analysis missions. The probe is equipped with a variety of sensors and has to estimate its own position within the ice as well as a map of its surroundings. The sensor fusion is based on an evidential SLAM approach which produces evidential occupancy grid maps that contain more information about the environment compared to probabilistic grid maps. We describe the different sensor models underlying the algorithm and we present empirical results obtained under controlled conditions in order to analyze the effectiveness of the proposed multi-sensor fusion approach. In particular, we show that the localization error is significantly reduced by combining multiple sensors.

Keywords

SLAM Mulit-Sensor Fusion Evidence Theory Navigation Mapping 

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References

  1. 1.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with applications to tracking and navigation: theory algorithms and software. John Wiley & Sons (2004)Google Scholar
  2. 2.
    Dachwald, B., Feldmann, M., Espe, C., Plescher, E., Xu, C.: The Enceladus Explorer Collaboration: Development and testing of a maneuverable subsurface probe that can navigate autonomously through deep ice. In: Proceedings of the 9th International Planetary Probe Workshop (2012)Google Scholar
  3. 3.
    Doucet, A., De Freitas, N., Murphy, K., Russell, S.: Rao-Blackwellised particle filtering for dynamic Bayesian networks. In: Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 176–183 (2000)Google Scholar
  4. 4.
    Dubois, D., Denœux, T.: Conditioning in dempster-shafer theory: Prediction vs. Revision. In: Denœux, T., Masson, M.-H. (eds.) Belief Functions: Theory & Appl. AISC, vol. 164, pp. 385–392. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part i. IEEE Robotics & Automation Magazine 13(2), 99–110 (2006)CrossRefGoogle Scholar
  6. 6.
    Niedermeier, H., Clemens, J., Kowalski, J., Macht, S., Heinen, D., Hoffmann, R., Linder, P.: Navigation system for a research ice probe for antarctic glaciers. In: IEEE/ION PLANS 2014. IEEE (2014)Google Scholar
  7. 7.
    Pagac, D., Nebot, E., Durrant-Whyte, H.: An evidential approach to map-building for autonomous vehicles. IEEE Transactions on Robotics and Automation 14(4), 623–629 (1998)CrossRefGoogle Scholar
  8. 8.
    Reineking, T.: Belief Functions: Theory and Algorithms. Ph.D. thesis, University of Bremen (February 2014), http://nbn-resolving.de/urn:nbn:de:gbv:46-00103727-16
  9. 9.
    Reineking, T., Clemens, J.: Evidential FastSLAM for grid mapping. In: 16th International Conference on Information Fusion (FUSION), pp. 789–796 (July 2013)Google Scholar
  10. 10.
    Smets, P.: Belief functions on real numbers. International Journal of Approximate Reasoning 40(3), 181–223 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Smets, P.: Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate Reasoning 9, 1–35 (1993)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Joachim Clemens
    • 1
  • Thomas Reineking
    • 1
  1. 1.Cognitive NeuroinformaticsUniversity of BremenBremenGermany

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