Using Multiple Gaussian Hypotheses to Represent Probability Distributions for Mobile Robot
Localization
David Austin and Patric Jensfelt
Abstract
A new mobile robot localization technique is presented which
uses multiple Gaussian hypotheses to represent the probability
distribution of the robots location in the environment. A tree of
hypotheses is built by the application of Bayes' rule with each new
sensor mesurement. However, such a tree can grow without bound and so
rules are introduced for the elimination of the least likely
hypotheses from the tree and for the proper re-distribution of their
probability. This technique is applied to a feature-based mobile robot
localization scheme and experimental results are given demonstrating
the effectiveness of the scheme.
BibTeX Entry:
@InProceedings{AustinJensfelt00a,
author = {David Austin and Patric Jensfelt},
title = {Using Multiple Gaussian Hypotheses to Represent Probability
Distributions for Mobile Robot Localization},
booktitle = {IEEE Intl. Conf. on Robotics and Automation},
year = 2000
}
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