J. Wolf, W. Burgard, H. Burkhardt
Robust Vision-based Localization by Combining an Image Retrieval System with Monte Carlo Localization
In IEEE Transactions on Robotics, 21 (2), pp. 208-216, 2005
Abstract
In this paper we present a vision-based approach to
mobile robot localization, that integrates an image retrieval system
with Monte-Carlo localization. The image retrieval process is based
on features that are invariant with respect to image translations and
limited scale. Since it furthermore uses local features, the system
is robust against distortion and occlusions which is especially
important in populated environments. To integrate this approach with
the sample-based Monte-Carlo localization technique we extract for
each image in the database a set of possible view-points using a
two-dimensional map of the environment. Our technique has been
implemented and tested extensively. We present practical experiments
illustrating that our approach is able to globally localize a mobile
robot, to reliably keep track of the robot's position, and to recover
from localization failures. We furthermore present experiments
designed to analyze the reliability and robustness of our approach
with respect to larger errors in the odometry.
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Bibtex
@Article{WBB05,
author = {Wolf, J. and Burgard, W.
and Burkhardt, H.},
title = {Robust Vision-based Localization by Combining an Image Retrieval System with Monte Carlo Localization},
journal = {IEEE Transactions on Robotics},
year = 2005,
volume = 21,
number = 2,
pages = {208-216}
}