F. Dellaert, W. Burgard D. Fox, and S. Thrun
Using the Condensation Algorithm
for Robust, Vision-based Mobile Robot Localization
Proc. of the IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (CVPR'99)
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 (Isard 96,Isard
98), 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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Bibtex
@INPROCEEDINGS{Del99Usi,
AUTHOR
= {Dellaert, F. and Burgard, W. and Fox, D. and Thrun, S.},
TITLE
= {Using the Condensation Algorithm for Robust, Vision-based Mobile Robot
Localization},
YEAR
= {1999},
BOOKTITLE = {Proc.~of the IEEE Computer
Society Conference on Computer Vision and Pattern Recognition}
}
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