Tom Duckett, Stephen Marsland and Jonathan Shapiro
Fast, On-Line Learning of Globally Consistent Maps
Autonomous Robots, Vol. 12, No. 3, pp. 287-300, 2002.
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 minimise 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.
Download
[ps.gz]
[pdf]
Bibtex
@ARTICLE{DuckettJAR02,
AUTHOR = "{Duckett}, Tom and {Marsland}, Stephen and {Shapiro}, Jonathan",
TITLE = "Fast, On-line Learning of Globally Consistent Maps",
JOURNAL = "Autonomous Robots",
VOLUME = 12,
NUMBER = {3},
PAGES = {287--300},
YEAR = 2002
}