Feature Based Condensation for Mobile Robot Localization
Patric
Jensfelt, David Austin,
Olle Wijk and
Magnus Andersson
Abstract
Much attention has been given to Monte Carlo methods
for mobile robot localization. This has resulted in somewhat of a
breakthrough in representing uncertainty for mobile robots. In this
paper we use CONDENSATION as a tool for doing feature based global
localization in a large and semi-structured environment. This paper
presents a comparison of four different feature types: sonar based
triangulation points and pairs of triangulation points, as well as
lines and doors extracted using a laser scanner. We show experimental
results that highlight the information content of the different
features, and point to fruitful combinations. Four performance
measures are introduced to evaluate the results, convergence speed,
accuracy, computation time and how well suited the features are for
using in combination with what is called planned sampling, an
enhancement over random sampling. From the comparison of the
features, some general guidelines are drawn for determining good
feature types.
BibTeX Entry:
@InProceedings{Jensfelt00a,
author = {Patric Jensfelt and David Austin and Olle Wijk and Magnus Andersson},
title = {Feature Based Condensation for Mobile Robot Localization},
booktitle = {IEEE Intl. Conf. on Robotics and Automation},
year = 2000
}
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