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KI - Künstliche Intelligenz

, Volume 24, Issue 3, pp 199–206 | Cite as

Lifelong Map Learning for Graph-based SLAM in Static Environments

  • Henrik Kretzschmar
  • Giorgio Grisetti
  • Cyrill Stachniss
Fachbeitrag

Abstract

In this paper, we address the problem of lifelong map learning in static environments with mobile robots using the graph-based formulation of the simultaneous localization and mapping problem. The pose graph, which stores the poses of the robot and spatial constraints between them, is the central data structure in graph-based SLAM. The size of the pose graph has a direct influence on the runtime and the memory complexity of the SLAM system and typically grows over time. A robot that performs lifelong mapping in a bounded environment has to limit the memory and computational complexity of its mapping system. We present a novel approach to prune the pose graph so that it only grows when the robot acquires relevant new information about the environment in terms of expected information gain. As a result, our approach scales with the size of the environment and not with the length of the trajectory, which is an important prerequisite for lifelong map learning. The experiments presented in this paper illustrate the properties of our method using real robots.

Keywords

SLAM Mapping Expected information gain 

Notes

Acknowledgements

We would like to thank Dirk Hähnel for providing the Intel Research Lab dataset. This work has partly been supported by the German Research Foundation (DFG) under contract number SFB/TR-8 and by the European Commission under contract number FP7-ICT-231888-EUROPA.

References

  1. 1.
    Biber P, Duckett T (2005) Dynamic maps for long-term operation of mobile service robots. In: Proc of robotics: science and systems (RSS), pp 17–24 Google Scholar
  2. 2.
    Duckett T, Marsland S, Shapiro J (2002) Fast, on-line learning of globally consistent maps. Auton Robots 12(3):287–300 zbMATHCrossRefGoogle Scholar
  3. 3.
    Eustice R, Singh H, Leonard J (2005) Exactly sparse delayed-state filters. In: Proc of the IEEE int conf on robotics & automation (ICRA), pp 2428–2435 Google Scholar
  4. 4.
    Eustice R, Singh H, Leonard J (2006) Exactly sparse delayed-state filters for view-based SLAM. IEEE Trans Robot 22(6):1100–1114 CrossRefGoogle Scholar
  5. 5.
    Folkesson J, Christensen H (2004) Graphical slam—a self-correcting map. In: Proc of the IEEE int conf on robotics & automation (ICRA) Google Scholar
  6. 6.
    Frese U, Larsson P, Duckett T (2005) A multilevel relaxation algorithm for simultaneous localisation and mapping. IEEE Trans Robot 21(2):1–12 CrossRefGoogle Scholar
  7. 7.
    Garrison WL, Marble DF (1965) A prolegomenon to the forecasting of transportation development. United States Army Aviation Material Labs Technical Report, Office of Technical Services, United States Department of Commerce Google Scholar
  8. 8.
    Grisetti G, Stachniss C, Burgard W (2007a) Improved techniques for grid mapping with Rao-blackwellized particle filters. IEEE Trans Robot 23(1):34–46 CrossRefGoogle Scholar
  9. 9.
    Grisetti G, Stachniss C, Grzonka S, Burgard W (2007b) A tree parameterization for efficiently computing maximum likelihood maps using gradient descent. In: Proc of robotics: science and systems (RSS) Google Scholar
  10. 10.
    Grisetti G, Rizzini DL, Stachniss C, Olson E, Burgard W (2008) Online constraint network optimization for efficient maximum likelihood map learning. In: Proc of the IEEE int conf on robotics & automation (ICRA) Google Scholar
  11. 11.
    Julier S, Uhlmann J, Durrant-Whyte H (1995) A new approach for filtering nonlinear systems. In: Proc of the American control conference, pp 1628–1632 Google Scholar
  12. 12.
    Konolige K, Agrawal M (2008) Frameslam: from bundle adjustment to real-time visual mapping. IEEE Trans Robot 24(5):1066–1077 CrossRefGoogle Scholar
  13. 13.
    Leonard J, Durrant-Whyte H (1991) Mobile robot localization by tracking geometric beacons. IEEE Trans Robot Automat 7(4):376–382 CrossRefGoogle Scholar
  14. 14.
    Lu F, Milios E (1997) Globally consistent range scan alignment for environment mapping. Auton Robots 4:333–349 CrossRefGoogle Scholar
  15. 15.
    Montemerlo M, Thrun S (2003) Simultaneous localization and mapping with unknown data association using FastSLAM. In: Proc of the IEEE int conf on robotics & automation (ICRA), pp 1985–1991 Google Scholar
  16. 16.
    Moravec H, Elfes A (1985) High resolution maps from wide angle sonar. In: Proc of the IEEE int conf on robotics & automation (ICRA), St Louis, MO, USA, pp 116–121 Google Scholar
  17. 17.
    Olson E (2008) Robust and efficient robotic mapping. PhD thesis, MIT, Cambridge, MA, USA Google Scholar
  18. 18.
    Olson E, Walter M, Leonard J, Teller S (2005) Single cluster graph partitioning for robotics applications. In: Proceedings of robotics science and systems, pp 265–272 Google Scholar
  19. 19.
    Olson E, Leonard J, Teller S (2006) Fast iterative optimization of pose graphs with poor initial estimates. In: Proc of the IEEE int conf on robotics & automation (ICRA), pp 2262–2269 Google Scholar
  20. 20.
    Stachniss C, Burgard W (2005) Mobile robot mapping and localization in non-static environments. In: Proc of the national conference on artificial intelligence (AAAI), pp 1324–1329 Google Scholar
  21. 21.
    Stachniss C, Grisetti G, Burgard W (2005) Information gain-based exploration using Rao-blackwellized particle filters. In: Proc of robotics: science and systems (RSS), Cambridge, MA, USA, pp 65–72 Google Scholar
  22. 22.
    Thrun S, Montemerlo M (2006) The graph SLAM algorithm with applications to large-scale mapping of urban structures. Int J Robot Res 25(5–6):403 CrossRefGoogle Scholar
  23. 23.
    Thrun S, Liu Y, Koller D, Ng A, Ghahramani Z, Durrant-Whyte H (2004) Simultaneous localization and mapping with sparse extended information filters. Int J Robot Res 23(78):693–716 CrossRefGoogle Scholar
  24. 24.
    Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT Press, Cambridge zbMATHGoogle Scholar
  25. 25.
    Tipaldi GD, Grisetti G, Burgard W (2007) Approximated covariance estimation in graphical approaches to slam. In: Proc of the IEEE/RSJ int conf on intelligent robots and systems (IROS) Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Henrik Kretzschmar
    • 1
  • Giorgio Grisetti
    • 1
  • Cyrill Stachniss
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburgGermany

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