Using hidden markov models to improve floor level localisation

Alvarez, JK; Abeywardena, D; Shi, L; Kodagoda, S

Using hidden markov models to improve floor level localisation

Alvarez, JK Abeywardena, D Shi, L

Kodagoda, S

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2015
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Alvarez, JK	en_US
dc.contributor.author	Abeywardena, D	en_US
dc.contributor.author	Shi, L https://orcid.org/0000-0002-5215-025X	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.available	2015-10-19	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2015	en_US
dc.identifier.issn	1448-2053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/40178
dc.description.abstract	The focus of this paper is on estimating the oor level of a robot/person moving in a multi-oor environment. It demonstrates how in- formation about transitions between oors can be employed within a probabilistic framework to improve the accuracy of oor level estimation. This is achieved by combining a simple linear classifier with a Hidden Markov Model that captures the two basic motion patterns in a multi-oor environment: Within-oor and be-Tween oors, switching from one to the other as oor transition events are detected. Through real-world experiments, we demonstrate the ability of this framework to produce accurate oor level estimates using only RSSI (Received Signal Strength Indicator) measurements, even when operating in an environment with as little as five WiFi access points per oor.	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA	en_US
dc.title	Using hidden markov models to improve floor level localisation	en_US
dc.type	Conference Proceeding
utslib.for	091303 Autonomous Vehicles	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
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 Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

The focus of this paper is on estimating the oor level of a robot/person moving in a multi-oor environment. It demonstrates how in- formation about transitions between oors can be employed within a probabilistic framework to improve the accuracy of oor level estimation. This is achieved by combining a simple linear classifier with a Hidden Markov Model that captures the two basic motion patterns in a multi-oor environment: Within-oor and be-Tween oors, switching from one to the other as oor transition events are detected. Through real-world experiments, we demonstrate the ability of this framework to produce accurate oor level estimates using only RSSI (Received Signal Strength Indicator) measurements, even when operating in an environment with as little as five WiFi access points per oor.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/40178