Mobile robot localization and mapping using a Gaussian sum filter

Kwok, NM; Ha, QP; Huang, S; Dissanayake, G; Fang, G

Mobile robot localization and mapping using a Gaussian sum filter

Kwok, NM Ha, QP

Huang, S

Dissanayake, G

Fang, G

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Publication Type:: Journal Article
Citation:: International Journal of Control, Automation and Systems, 2007, 5 (3), pp. 251 - 268
Issue Date:: 2007-06-01

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Field	Value	Language
dc.contributor.author	Kwok, NM	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Fang, G	en_US
dc.date.issued	2007-06-01	en_US
dc.identifier.citation	International Journal of Control, Automation and Systems, 2007, 5 (3), pp. 251 - 268	en_US
dc.identifier.issn	1598-6446	en_US
dc.identifier.uri	http://hdl.handle.net/10453/6180
dc.description.abstract	A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.	en_US
dc.relation.ispartof	International Journal of Control, Automation and Systems	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Mobile robot localization and mapping using a Gaussian sum filter	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	5	en_US
utslib.for	0910 Manufacturing Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.

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http://hdl.handle.net/10453/6180