Journal of Intelligent and Robotic Systems

, Volume 56, Issue 3, pp 301–318 | Cite as

VecSLAM: An Efficient Vector-Based SLAM Algorithm for Indoor Environments

Article

Abstract

In this paper, we present an efficient SLAM (Simultaneous Localization and Mapping) algorithm named VecSLAM, which localizes and builds a vector map for mobile robots in indoor environments. Compared to grid-mapping approaches, vector-based mapping algorithms require a relatively small amount of memory. Two essential operations for successful vector mapping are vector merging and loop closing. Merging methods used by traditional line segment-based mapping algorithms do not consider the sensor characteristics, which causes additional mapping error and makes it harder to close loops after navigation over a long distance. In addition, few line segment-based SLAM approaches contain loop closing methodology. We present a novel vector merging scheme based on a recursive least square estimation for robust mapping. An efficient loop closing method is also proposed, which effectively distributes the resultant mapping error throughout the loop to guarantee global map consistency. Simulation studies and experimental results show that VecSLAM is an efficient and robust online localization and mapping algorithm.

Keywords

SLAM Vector Line segment Laser range finder Mobile robot 

Mathematics Subject Classifications (2000)

68T40 93C85 

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References

  1. 1.
    Brunskill, E., Roy, N.: SLAM using incremental probabilistic PCA and dimensionality reduction. In: IEEE International Conference on Robotics and Automation pp. 344–349 (2005)Google Scholar
  2. 2.
    Castellanosm, J., Montiel, J., Neira, J., Tardós, J.: The SPmap: a probabilistic framework for simultaneous localization and map building. IEEE Trans. Robot. Autom. 15(5), 948–952 (1999)CrossRefGoogle Scholar
  3. 3.
    Castellanos, J., Neira, J., Tardós, J.: Multisensor fusion for simultaneous localization and map building. IEEE Trans. Robot. Autom. 17(6), 908–914 (2001)CrossRefGoogle Scholar
  4. 4.
    Choi, Y., Lee, T., Oh, S.: A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot. Auton. Robots 24(1), 13–27 (2008)CrossRefGoogle Scholar
  5. 5.
    Frese, U.: A discussion of simultaneous localization and mapping. Auton. Robots 20(1), 25–42 (2006)CrossRefGoogle Scholar
  6. 6.
    Grisetti, G., Tipaldi, G., Stachniss, C., Burgard, W., Nardi, D.: Fast and accurate SLAM with Rao-Blackwellized particle filter. Robot. Auton. Syst. 55(1), 30–38 (2007)CrossRefGoogle Scholar
  7. 7.
    Hähnel, D., Burgard, W., Fox, D., Thrun, S.: An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. In: IEEE International Conference on Intelligent Robots and Systems, pp. 206–211 (2003)Google Scholar
  8. 8.
    Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping. Auton. Robots 4(4), 333–349 (1997)CrossRefGoogle Scholar
  9. 9.
    Mázl, R., Přeučil, L.: Building a 2D environment map from laser range-finder data. In: IEEE Intelligent Vehicles Symposium, pp. 290–295. IEEE, Piscataway (2000)Google Scholar
  10. 10.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: Proceedings of the AAAI National Conference on Artificial Intelligence, Edmonton, 28 July–1 August 2002Google Scholar
  11. 11.
    Nguyen, V., Harati, A., Martinelli, A., Siegwart, R.: Orthogonal SLAM: a step toward lightweight indoor autonomous navigation. International Conference on Intelligent Robots and Systems pp. 5007–5012 (2006)Google Scholar
  12. 12.
    Schröter, D., Beetz, M., Gutmann, J.: RG mapping: learning compact and structured 2D line maps of indoor environments. In: IEEE International Workshop on Robot and Human Interactive Communication, pp. 282–287. IEEE, Piscataway (2002)CrossRefGoogle Scholar
  13. 13.
    Sohn, H., Kim, B.: An efficient localization algorithm based on vector matching for mobile robots using laser range finders. J. Intell. Robot. Syst. 51(4), 461–488 (2008)CrossRefGoogle Scholar
  14. 14.
    Stachniss, C., Hähnel, D., Burgard, W., Grisetti, G.: On actively closing loops in grid-based FastSLAM. Adv. Robot. 19(10), 1059–1080 (2005)CrossRefGoogle Scholar
  15. 15.
    Thrun, S.: Robotic mapping: a survey. In: International Joint Conference on Artificial Intelligence, pp. 1–36 (2003)Google Scholar
  16. 16.
    Zhang, L., Ghosh, B.: Line segment based map building and localization using 2D laser rangefinder. In: International Conference on Robotics and Automation, pp. 2538–2543. IEEE, Piscataway (2000)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea

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