An audio-visual solution to sound source localization and tracking with applications to HRI

Emery, BM; Jadidi, MG; Nakamura, K; Miro, JV

An audio-visual solution to sound source localization and tracking with applications to HRI

Emery, BM Jadidi, MG Nakamura, K Miro, JV

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2016, 2016-December pp. 268 - 277
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Emery, BM	en_US
dc.contributor.author	Jadidi, MG	en_US
dc.contributor.author	Nakamura, K	en_US
dc.contributor.author	Miro, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2016, 2016-December pp. 268 - 277	en_US
dc.identifier.isbn	9781634396080	en_US
dc.identifier.issn	1448-2053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130936
dc.description.abstract	© 2018 Australasian Robotics and Automation Association. All rights reserved. Robot audition is an emerging and growing branch in the robotic community and is necessary for a natural Human-Robot Interaction (HRI). In this paper, we propose a framework that integrates advances from Simultaneous Localization And Mapping (SLAM), bearing-only target tracking, and robot audition techniques into a unified system for sound source identification, localization, and tracking. In indoors, acoustic observations are often highly noisy and corrupted due to reverberations, the robot egomotion and background noise, and the possible discontinuous nature of them. Therefore, in everyday interaction scenarios, the system requires accommodating for outliers, robust data association, and appropriate management of the landmarks, i.e. sound sources. We solve the robot self-localization and environment representation problems using an RGB-D SLAM algorithm, and sound source localization and tracking using recursive Bayesian estimation in the form of the extended Kalman filter with unknown data associations and an unknown number of landmarks. The experimental results show that the proposed system performs well in the medium-sized cluttered indoor environment.	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA	en_US
dc.title	An audio-visual solution to sound source localization and tracking with applications to HRI	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-December	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/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2016-December	en_US

Abstract:

© 2018 Australasian Robotics and Automation Association. All rights reserved. Robot audition is an emerging and growing branch in the robotic community and is necessary for a natural Human-Robot Interaction (HRI). In this paper, we propose a framework that integrates advances from Simultaneous Localization And Mapping (SLAM), bearing-only target tracking, and robot audition techniques into a unified system for sound source identification, localization, and tracking. In indoors, acoustic observations are often highly noisy and corrupted due to reverberations, the robot egomotion and background noise, and the possible discontinuous nature of them. Therefore, in everyday interaction scenarios, the system requires accommodating for outliers, robust data association, and appropriate management of the landmarks, i.e. sound sources. We solve the robot self-localization and environment representation problems using an RGB-D SLAM algorithm, and sound source localization and tracking using recursive Bayesian estimation in the form of the extended Kalman filter with unknown data associations and an unknown number of landmarks. The experimental results show that the proposed system performs well in the medium-sized cluttered indoor environment.

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http://hdl.handle.net/10453/130936