An Audio-visual Solution to Sound Source Localization and Tracking with Applications to HRI

Emery, BM; Ghaffari Jadidi, M; Nakamura, K; Valls Miro, J

An Audio-visual Solution to Sound Source Localization and Tracking with Applications to HRI

Emery, BM Ghaffari Jadidi, M

Nakamura, K Valls Miro, J

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Publisher:: ARAA
Publication Type:: Conference Proceeding
Citation:: Proceedings of the Australasian Conference on Robotics & Automation (ACRA), 2016, pp. 1 - 10 (10)
Issue Date:: 2016-11-05

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Field	Value	Language
dc.contributor.author	Emery, BM	en_US
dc.contributor.author	Ghaffari Jadidi, M https://orcid.org/0000-0002-4734-4295	en_US
dc.contributor.author	Nakamura, K	en_US
dc.contributor.author	Valls Miro, J https://orcid.org/0000-0002-0083-7797	en_US
dc.date	2016-11-05	en_US
dc.date.issued	2016-11-05	en_US
dc.identifier.citation	Proceedings of the Australasian Conference on Robotics & Automation (ACRA), 2016, pp. 1 - 10 (10)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/86974
dc.description.abstract	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 unifed system for sound source identification, localization, and tracking. In indoors, acoustic observations are often highly noisy and corrupted due to reverberations, the robot ego-motion and background noise, and 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.publisher	ARAA	en_US
dc.relation.ispartof	Proceedings of the Australasian Conference on Robotics & Automation (ACRA)	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation	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.location	Australia	en_US
utslib.location.activity	Brisbane	en_US
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/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2017-11-07	en_US
pubs.place-of-publication	Australia	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2016-11-05	en_US

Abstract:

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 unifed system for sound source identification, localization, and tracking. In indoors, acoustic observations are often highly noisy and corrupted due to reverberations, the robot ego-motion and background noise, and 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/10453/86973