(Georgia Institute of Technology, 1999)
Burgard, Wolfram; Dellaert, Frank; Fox, Dieter; Thrun, Sebastian
To navigate reliably in indoor environments, a mobile
robot must know where it is. This includes both the ability
of globally localizing the robot from scratch, as well
as tracking the robot’s position once its location is known.
Vision has long been advertised as providing a solution to
these problems, but we still lack efficient solutions in unmodified
environments. Many existing approaches require
modification of the environment to function properly, and
those that work within unmodified environments seldomly
address the problem of global localization.
In this paper we present a novel, vision-based localization
method based on the CONDENSATION algorithm
[17, 18], a Bayesian filtering method that uses a sampling-based
density representation. We show how the CONDENSATION
algorithm can be used in a novel way to track the
position of the camera platform rather than tracking an object
in the scene. In addition, it can also be used to globally
localize the camera platform, given a visual map of the environment.
Based on these two observations, we present a vision-based
robot localization method that provides a solution to
a difficult and open problem in the mobile robotics community.
As evidence for the viability of our approach, we show
both global localization and tracking results in the context
of a state of the art robotics application.
(Georgia Institute of Technology, 1999)
Burgard, Wolfram; Dellaert, Frank; Fox, Dieter; Thrun, Sebastian
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.