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Detection strategy for kidnapped robot problem in Monte Carlo localization based on similarity measure of environment

This paper investigates new alternative approaches to detect the kidnapped robot problem event in Monte Carlo Localization. The underlying idea is based on the local similarity measures of the environment seen by the robot at two consecutive time instances. Six different similarity measures are inve...

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Main Authors: Bukhori, Iksan, Ismail, Zool Hilmi
Format: Conference or Workshop Item
Published: IEEE 2016
Subjects:
T Technology
Online Access:http://eprints.utm.my/id/eprint/67076/
http://dx.doi.org/10.1109/USYS.2016.7893936
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spelling my.utm.670762017-08-25T06:30:21Z http://eprints.utm.my/id/eprint/67076/ Detection strategy for kidnapped robot problem in Monte Carlo localization based on similarity measure of environment Bukhori, Iksan Ismail, Zool Hilmi T Technology This paper investigates new alternative approaches to detect the kidnapped robot problem event in Monte Carlo Localization. The underlying idea is based on the local similarity measures of the environment seen by the robot at two consecutive time instances. Six different similarity measures are investigated and tested against particles weight-based detectors to see how good each detector’s ability to distinguish normal condition from kidnapping event. These simulations show that similarity based-detectors promises better general accuracy across all kidnapping points compared to particles weight-based detectors and the accuracy is not affected by the result of re-localization. IEEE 2016-01-12 Conference or Workshop Item PeerReviewed Bukhori, Iksan and Ismail, Zool Hilmi (2016) Detection strategy for kidnapped robot problem in Monte Carlo localization based on similarity measure of environment. In: 2016 IEEE 6TH International Conference on Underwater System Technology: Theory and Applications, 13-14 Dec, 2016, Pulau Pinang, Malaysia. http://dx.doi.org/10.1109/USYS.2016.7893936
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology
spellingShingle T Technology
Bukhori, Iksan
Ismail, Zool Hilmi
Detection strategy for kidnapped robot problem in Monte Carlo localization based on similarity measure of environment
description This paper investigates new alternative approaches to detect the kidnapped robot problem event in Monte Carlo Localization. The underlying idea is based on the local similarity measures of the environment seen by the robot at two consecutive time instances. Six different similarity measures are investigated and tested against particles weight-based detectors to see how good each detector’s ability to distinguish normal condition from kidnapping event. These simulations show that similarity based-detectors promises better general accuracy across all kidnapping points compared to particles weight-based detectors and the accuracy is not affected by the result of re-localization.
format Conference or Workshop Item
author Bukhori, Iksan
Ismail, Zool Hilmi
author_facet Bukhori, Iksan
Ismail, Zool Hilmi
author_sort Bukhori, Iksan
title Detection strategy for kidnapped robot problem in Monte Carlo localization based on similarity measure of environment
title_short Detection strategy for kidnapped robot problem in Monte Carlo localization based on similarity measure of environment
title_full Detection strategy for kidnapped robot problem in Monte Carlo localization based on similarity measure of environment
title_fullStr Detection strategy for kidnapped robot problem in Monte Carlo localization based on similarity measure of environment
title_full_unstemmed Detection strategy for kidnapped robot problem in Monte Carlo localization based on similarity measure of environment
title_sort detection strategy for kidnapped robot problem in monte carlo localization based on similarity measure of environment
publisher IEEE
publishDate 2016
url http://eprints.utm.my/id/eprint/67076/
http://dx.doi.org/10.1109/USYS.2016.7893936
_version_ 1643655894564601856
score 13.160551

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