Topological Map Merging

  • Wesley H. Huang
  • Kristopher R. Beevers


A key capability for teams of mobile robots is to cooperatively explore and map an environment. Maps created by one robot must be merged with those from another robot — a difficult problem when the robots do not have a common reference frame. This problem is greatly simplified when topological maps are used because they provide a concise description of the navigability of a space. In this paper, we formulate an algorithm for merging two topological maps that uses aspects of maximal subgraph matching and image registration methods. Simulated and real-world experiments demonstrate the efficacy of our algorithm.


Mobile Robot Image Registration Iterative Close Point Transformation Space Common Subgraph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    K. R. Beevers. Topological mapping and map merging with sensing-limited robots. Master’s thesis, Rensselaer Polytechnic Institute, Troy, NY, April 2004.Google Scholar
  2. 2.
    P. Besl and N. McKay. A method for registration of 3-D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence, 14(2):239–256, February 1992.Google Scholar
  3. 3.
    H. Bunke. On a relation between graph edit distance and maximum common subgraph. Pattern Recognition Letters, 18(8):689–694, 1997.CrossRefMathSciNetGoogle Scholar
  4. 4.
    G. Dedeoglu and G.S. Sukhatme. Landmark-based matching algorithm for cooperative mapping by autonomous robots. In L. E. Parker, G. W. Bekey, and J. Barhen, editors, Distributed Autonomous Robotic Systems 4, pages 251–260. Springer-Verlag, 2000.Google Scholar
  5. 5.
    T. Duckett, S. Marsland, and J. Shapiro. Learning globally consistent maps by relaxation. In IEEE Intl. Conf. on Robotics & Automation, pages 3841–3846, 2000.Google Scholar
  6. 6.
    G. Dudek, M. Jenkin, E. Milos, and D. Wilkes. Topological exploration with multiple robots. In 7th Intl. Symp. on Robotics with Applications, 1998.Google Scholar
  7. 7.
    J. Fenwick, P. Newman, and J. Leonard. Cooperative concurrent mapping and localization. In IEEE Intl. Conf. on Robotics & Automation, 2002.Google Scholar
  8. 8.
    J.M. Fitzpatrick, D.L. Hill, and C.R. Maurer, Jr. Image registration. In M. Sonka and J. M. Fitzpatrick, editors, Handbook of Medical Imaging, volume 2: Medical Image Processing and Analysis, chapter 8. SPIE, 2000.Google Scholar
  9. 9.
    M. Golfarelli, D. Maio, and S. Rizzi. Elastic correction of dead-reckoning errors in map building. In Intl. Conf. on Intelligent Robots and Systems, pages 905–911, 1998.Google Scholar
  10. 10.
    J. Ko, B. Stewart, D. Fox, K. Konolige, and B. Limketkai. A practical, decisiontheoretic approach to multi-robot mapping and exploration. In Intl. Conf. on Intelligent Robots and Systems, 2003.Google Scholar
  11. 11.
    K. Konolige, D. Fox, B. Limketkai, J. Ko, and B. Stewart. Map merging for distributed robot navigation. In Intl. Conf. on Intelligent Robots and Systems, pages 212–217, 2003.Google Scholar
  12. 12.
    F. Lu and E. Milios. Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4(4):333–349, 1997.CrossRefGoogle Scholar
  13. 13.
    S. Thrun. A probabilistic online mapping algorithm for teams of mobile robots. Intl. Journal of Robotics Research, 20(5):335–363, 2001.CrossRefGoogle Scholar
  14. 14.
    R. Wilson and E. Hancock. Structural matching by discrete relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(6), June 1997.Google Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Wesley H. Huang
    • 1
  • Kristopher R. Beevers
    • 1
  1. 1.Department of Computer ScienceRensselaer Polytechnic InstituteTroyUSA

Personalised recommendations