Title:
Using the CONDENSATION Algorithm for Robust, Vision-Based Mobile Robot Localization
Using the CONDENSATION Algorithm for Robust, Vision-Based Mobile Robot Localization
Author(s)
Burgard, Wolfram
Dellaert, Frank
Fox, Dieter
Thrun, Sebastian
Dellaert, Frank
Fox, Dieter
Thrun, Sebastian
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Abstract
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.
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Date Issued
1999
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Paper