Experiments on Augmenting Condensation for Mobile Robot Localization
Patric
Jensfelt, Olle Wijk, David Austin,
and Magnus Andersson
Abstract:
In this paper we study some modifications of the,
by now well known CONDENSATION algorithm. The case studied is feature
based mobile robot localization in a large scale environment. The
required sample set size for making the CONDENSATION algorithm
converge properly can in many cases be too high with respect to
computational complexity. This is often the case in area with lots of
(feature) symmetries, for instance long corridors. To deal with this
problem we experimentally study two small modifications of the
CONDENSATION algorithm. The first strategy called ``CONDENSATION with
random sampling'' is to take part of the sample set and spread it
randomly over the environment the robot operates in. The second
strategy called ``CONDENSATION with planned sampling'' is to place
part of the sample set at planned posititions based on the features
detected. From the experiments we conclude that it is possible to
manage with a sample set size which would be insufficient for the
original CONDENSATION algorithm.
BibTeX Entry:
@InProceedings{Jensfelt00b,
author = {Patric Jensfelt and Olle Wijk and David Austin and Magnus Andersson},
title = {Experiments on Augmenting Condensation for Mobile Robot Localization},
booktitle = {IEEE Intl. Conf. on Robotics and Automation},
year = 2000,
pages = "2518--2524"
}
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