Cross-modal localization through mutual information

Alempijevic, A; Kodagoda, S; Dissanayake, G

Cross-modal localization through mutual information

Alempijevic, A

Kodagoda, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, 2009, pp. 5597 - 5602
Issue Date:: 2009-12-11

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Field	Value	Language
dc.contributor.author	Alempijevic, A https://orcid.org/0000-0002-1769-8041	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2009-12-11	en_US
dc.identifier.citation	2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, 2009, pp. 5597 - 5602	en_US
dc.identifier.isbn	9781424438044	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11125
dc.description.abstract	Relating information originating from disparate sensors observing a given scene is a challenging task, particularly when an appropriate model of the environment or the behaviour of any particular object within it is not available. One possible strategy to address this task is to examine whether the sensor outputs contain information which can be attributed to a common cause. In this paper, we present an approach to localise this embedded common information through an indirect method of estimating mutual information between all signal sources. Ability of L1 regularization to enforce sparseness of the solution is exploited to identify a subset of signals that are related to each other, from among a large number of sensor outputs. As opposed to the conventional L2 regularization, the proposed method leads to faster convergence with much reduced spurious associations. Simulation and experimental results are presented to validate the findings. © 2009 IEEE.	en_US
dc.relation.ispartof	2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009	en_US
dc.relation.isbasedon	10.1109/IROS.2009.5354200	en_US
dc.title	Cross-modal localization through mutual information	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	St Louis, Missouri	en_US
dc.location.activity	St. Louis, MO
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 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:

Relating information originating from disparate sensors observing a given scene is a challenging task, particularly when an appropriate model of the environment or the behaviour of any particular object within it is not available. One possible strategy to address this task is to examine whether the sensor outputs contain information which can be attributed to a common cause. In this paper, we present an approach to localise this embedded common information through an indirect method of estimating mutual information between all signal sources. Ability of L1 regularization to enforce sparseness of the solution is exploited to identify a subset of signals that are related to each other, from among a large number of sensor outputs. As opposed to the conventional L2 regularization, the proposed method leads to faster convergence with much reduced spurious associations. Simulation and experimental results are presented to validate the findings. © 2009 IEEE.

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http://hdl.handle.net/10453/11125