J. Wolf, W. Burgard, H. Burkhardt
Robust Vision-based Localization for Mobile Robots
Using an Image Retrieval System Based on Invariant Features
In Proc. of the IEEE International Conference on Robotics &
Automation (ICRA), 2002.
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,
rotations, and limited scale. Since it furthermore uses local
features, the system is robust against distortion and occlusions
which is especially important in populated environments. By using
the sample-based Monte-Carlo localization technique our robot is
able to globally localize itself, to reliably keep track of its
position, and to recover from localization failures. Both
techniques are combined by extracting for each image a set of
possible view-points using a two-dimensional map of the environment.
Our technique has been implemented and tested extensively. We
present several experiments demonstrating the reliability and
robustness of our approach even in the context of dynamics in the
environment and larger errors in the odometry.
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Bibtex
@InProceedings{Wolf02Robust,
author = {Wolf, J. and Burgard, W.
and Burkhardt, H.},
title = {Robust Vision-based Localization for Mobile Robots using an Image Retrieval System Based on Invariant Features},
booktitle = {Proc. of the IEEE International Conference on Robotics \& Automation (ICRA)},
year = {2002}
}