Tom Duckett, Stephen Marsland and Jonathan Shapiro

Learning Globally Consistent Maps by Relaxation

Proc. ICRA-2000, IEEE Int. Conf. on Robotics and Automation, San Francisco, CA, 2000


Mobile robots require the ability to build their own maps to operate in unknown environments. A fundamental problem is that odometry-based dead reckoning cannot be used to assign accurate global position information to a map because of drift errors caused by wheel slippage. This paper introduces a fast, on-line method of learning globally consistent maps, using only local metric information. The approach differs from previous work in that it is computationally cheap, easy to implement and is guaranteed to find a globally optimal solution. Experiments are presented in which large, complex environments were successfully mapped by a real robot, and quantitative performance measures are used to assess the quality of the maps obtained.


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 AUTHOR = "T. {Duckett} and S. {Marsland} and J. {Shapiro}",
 TITLE = "Learning Globally Consistent Maps by Relaxation",
 BOOKTITLE = "Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'2000)", 
 ADDRESS = "San Francisco, CA", 
 YEAR = 2000,