Bearing-only SLAM using a SPRT Based Gaussian Sum Filter

Kwok, N; Dissanayake, G; Ha, QP

Bearing-only SLAM using a SPRT Based Gaussian Sum Filter

Kwok, N Dissanayake, G

Ha, QP

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Publisher:: The IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 1121 - 1126
Issue Date:: 2005-01

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Field	Value	Language
dc.contributor.author	Kwok, N	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.editor	Board, E	en_US
dc.contributor.editor	Robotics, IEEEICO	en_US
dc.contributor.editor	Automation	en_US
dc.date	2005-04-18	en_US
dc.date.issued	2005-01	en_US
dc.identifier.citation	Proceedings of 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 1121 - 1126	en_US
dc.identifier.isbn	0-7803-8914-X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10994
dc.description.abstract	Use of a Gaussian Sum filter (GSF) to efficiently solve the initialisation problem in bearing-only simultaneous localisation and mapping (SLAM) is the main contribution of this paper. When information about the range is not available, the initial probability density function (pdf) of a landmark in the environment can not be represented using a Gaussian. The GSF is an attractive candidate for estimation in this scenario as it can deal with arbitrary pdfs represented as sets of Gaussians. However, the implementation of the GSF requires maintaining a bank of extended Kalman filters. The resulting computational complexity needs to be reduced by employing a minimum number of filters. In this work, the performance of each extended Kalman filter (EKF) in the GSF is evaluated using the sequential probability ratio test (SPRT). As such the number of members in the Gaussian sum can be reduced rapidly and the efficiency of the GSF can be significantly increased, providing a solution to the important problem of bearing-only SLAM. The effectiveness of the proposed approach is demonstrated by simulation and experiment conducted using a Pioneer mobile robot.	en_US
dc.publisher	The IEEE	en_US
dc.relation.ispartof	Proceedings of 2005 IEEE International Conference on Robotics and Automation	en_US
dc.relation.ispartof	IEEE International Conference on Robotics and Automation	en_US
dc.rights	© 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Bearing-only SLAM using a SPRT Based Gaussian Sum Filter	en_US
dc.type	Conference Proceeding
utslib.location	New York, USA	en_US
utslib.location.activity	Barcelona Spain	en_US
utslib.for	091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Barcelona Spain	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	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	New York, USA	en_US
pubs.start-date	2005-04-18	en_US

Abstract:

Use of a Gaussian Sum filter (GSF) to efficiently solve the initialisation problem in bearing-only simultaneous localisation and mapping (SLAM) is the main contribution of this paper. When information about the range is not available, the initial probability density function (pdf) of a landmark in the environment can not be represented using a Gaussian. The GSF is an attractive candidate for estimation in this scenario as it can deal with arbitrary pdfs represented as sets of Gaussians. However, the implementation of the GSF requires maintaining a bank of extended Kalman filters. The resulting computational complexity needs to be reduced by employing a minimum number of filters. In this work, the performance of each extended Kalman filter (EKF) in the GSF is evaluated using the sequential probability ratio test (SPRT). As such the number of members in the Gaussian sum can be reduced rapidly and the efficiency of the GSF can be significantly increased, providing a solution to the important problem of bearing-only SLAM. The effectiveness of the proposed approach is demonstrated by simulation and experiment conducted using a Pioneer mobile robot.

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http://hdl.handle.net/10453/10994