Autonomous Robots

, Volume 12, Issue 3, pp 287–300 | Cite as

Fast, On-Line Learning of Globally Consistent Maps

  • Tom Duckett
  • Stephen Marsland
  • Jonathan Shapiro

Abstract

To navigate in unknown environments, mobile robots require the ability to build their own maps. A major problem for robot map building is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of cumulative drift errors. This paper introduces a fast, on-line algorithm for learning geometrically consistent maps using only local metric information. The algorithm works by using a relaxation technique to minimize an energy function over many small steps. The approach differs from previous work in that it is computationally cheap, easy to implement and is proven to converge to a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot.

simultaneous localization and mapping concurrent map-building and self-localization relaxation algorithm Gibbs sampling learning and adaptation 

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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Tom Duckett
    • 1
  • Stephen Marsland
    • 2
  • Jonathan Shapiro
    • 2
  1. 1.Department of TechnologyUniversity of ÖrebroÖrebroSweden
  2. 2.Department of Computer ScienceUniversity of ManchesterManchesterUK

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