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Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM

Extended Kalman filter (EKF) is often employed in determining the position of mobile robot and landmarks in Simultaneous Localization and Mapping (SLAM). Nonetheless, there are some disadvantages of using EKF, namely the requirement of Gaussian distribution for the state and noises, as well as the f...

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Main Authors: Hamzah, Ahmad, Nur Aqilah, Othman, Namerikawa, Toru
Format: Article
Language:English
English
Published: Hindawi Publishing Corporation 2015
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/7109/1/fkee-2014-hamzah-inpress.pdf
http://umpir.ump.edu.my/id/eprint/7109/4/fkee-2015-aqilah-Sufficient%20Condition%20for%20Estimation.pdf
http://umpir.ump.edu.my/id/eprint/7109/
http://dx.doi.org/10.1155/2015/238131
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spelling my.ump.umpir.71092018-02-05T06:50:49Z http://umpir.ump.edu.my/id/eprint/7109/ Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM Hamzah, Ahmad Nur Aqilah, Othman Namerikawa, Toru TK Electrical engineering. Electronics Nuclear engineering Extended Kalman filter (EKF) is often employed in determining the position of mobile robot and landmarks in Simultaneous Localization and Mapping (SLAM). Nonetheless, there are some disadvantages of using EKF, namely the requirement of Gaussian distribution for the state and noises, as well as the fact that it requires the smallest possible initial state covariance. This has led researchers to find alternative ways to mitigate the aforementioned shortcomings. Therefore, this study is conducted to propose an alternative technique by implementing HâÂ�Â� filter in SLAM instead of EKF. In implementing HâÂ�Â� filter in SLAM, the parameters of the filter especially ïÂ�§ needs to be properly defined to prevent finite escape time problem. Hence, this study proposes a sufficient condition for the estimation purposes. Two distinct cases of initial state covariance are analysed considering an indoor environment to ensure the best solution for SLAM problem exists along with considerations of process and measurement noises statistical behaviour. If the prescribed conditions are not satisfied, then the estimation would exhibit unbounded uncertainties and consequently results in erroneous inference about the robot and landmarks estimation. The simulation results have shown the reliability and consistency as suggested by the theoretical analysis and our previous findings. Hindawi Publishing Corporation 2015 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/7109/1/fkee-2014-hamzah-inpress.pdf application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/7109/4/fkee-2015-aqilah-Sufficient%20Condition%20for%20Estimation.pdf Hamzah, Ahmad and Nur Aqilah, Othman and Namerikawa, Toru (2015) Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM. Mathematical Problems in Engineering, 2015. pp. 1-14. ISSN 1024-123X (print); 1563-5147 (online) http://dx.doi.org/10.1155/2015/238131 DOI: 10.1155/2015/238131
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Hamzah, Ahmad
Nur Aqilah, Othman
Namerikawa, Toru
Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM
description Extended Kalman filter (EKF) is often employed in determining the position of mobile robot and landmarks in Simultaneous Localization and Mapping (SLAM). Nonetheless, there are some disadvantages of using EKF, namely the requirement of Gaussian distribution for the state and noises, as well as the fact that it requires the smallest possible initial state covariance. This has led researchers to find alternative ways to mitigate the aforementioned shortcomings. Therefore, this study is conducted to propose an alternative technique by implementing H� filter in SLAM instead of EKF. In implementing H� filter in SLAM, the parameters of the filter especially � needs to be properly defined to prevent finite escape time problem. Hence, this study proposes a sufficient condition for the estimation purposes. Two distinct cases of initial state covariance are analysed considering an indoor environment to ensure the best solution for SLAM problem exists along with considerations of process and measurement noises statistical behaviour. If the prescribed conditions are not satisfied, then the estimation would exhibit unbounded uncertainties and consequently results in erroneous inference about the robot and landmarks estimation. The simulation results have shown the reliability and consistency as suggested by the theoretical analysis and our previous findings.
format Article
author Hamzah, Ahmad
Nur Aqilah, Othman
Namerikawa, Toru
author_facet Hamzah, Ahmad
Nur Aqilah, Othman
Namerikawa, Toru
author_sort Hamzah, Ahmad
title Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM
title_short Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM
title_full Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM
title_fullStr Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM
title_full_unstemmed Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM
title_sort sufficient condition for estimation in designing h∞ filter-based slam
publisher Hindawi Publishing Corporation
publishDate 2015
url http://umpir.ump.edu.my/id/eprint/7109/1/fkee-2014-hamzah-inpress.pdf
http://umpir.ump.edu.my/id/eprint/7109/4/fkee-2015-aqilah-Sufficient%20Condition%20for%20Estimation.pdf
http://umpir.ump.edu.my/id/eprint/7109/
http://dx.doi.org/10.1155/2015/238131
_version_ 1643665543314538496
score 13.211869

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