Title:
Monte Carlo Localization for Mobile Robots
Monte Carlo Localization for Mobile Robots
Author(s)
Burgard, Wolfram
Dellaert, Frank
Fox, Dieter
Thrun, Sebastian
Dellaert, Frank
Fox, Dieter
Thrun, Sebastian
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
To navigate reliably in indoor environments, a mobile robot
must know where it is. Thus, reliable position estimation is
a key problem in mobile robotics. We believe that probabilistic
approaches are among the most promising candidates
to providing a comprehensive and real-time solution
to the robot localization problem. However, current
methods still face considerable hurdles. In particular, the
problems encountered are closely related to the type of
representation used to represent probability densities over
the robot’s state space. Recent work on Bayesian filtering
with particle-based density representations opens up a
new approach for mobile robot localization, based on these
principles. In this paper we introduce the Monte Carlo
Localization method, where we represent the probability
density involved by maintaining a set of samples that are
randomly drawn from it. By using a sampling-based representation
we obtain a localization method that can represent
arbitrary distributions. We show experimentally that
the resulting method is able to efficiently localize a mobile
robot without knowledge of its starting location. It is
faster, more accurate and less memory-intensive than earlier
grid-based methods.
Sponsor
Date Issued
1999
Extent
Resource Type
Text
Resource Subtype
Paper