Denna tjänst avvecklas 2026-01-19. Läs mer här (länk) Using Multiple Gaussian Hypotheses to Represent Probability Distributions for Mobile Robot
Localization
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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