J. Wolf, W. Burgard, H. Burkhardt
Robust Vision-Based Localization by Combining an Image Retrieval
System with Monte Carlo Localization
IEEE Transactions on Robotics, 21(2), 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{Wolf05ieeetro,
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},
VOLUME = 21,
NUMBER = 2,
pages = {208-216},
YEAR = 2005,
}