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/ http://umpir.ump.edu.my/id/eprint/7109/ http://umpir.ump.edu.my/id/eprint/7109/ 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

id	ump-7109
recordtype	eprints
spelling	ump-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 HÃ¢Â�Â� filter in SLAM instead of EKF. In implementing HÃ¢Â�Â� filter in SLAM, the parameters of the filter especially Ã¯Â�Â§ 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
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
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 HÃ¢Â�Â� filter in SLAM instead of EKF. In implementing HÃ¢Â�Â� filter in SLAM, the parameters of the filter especially Ã¯Â�Â§ 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/ http://umpir.ump.edu.my/id/eprint/7109/ http://umpir.ump.edu.my/id/eprint/7109/ 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
first_indexed	2023-09-18T22:03:28Z
last_indexed	2023-09-18T22:03:28Z
_version_	1777414553063129088

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

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