Probabilistic Pose Estimation of Deformable Linear Objects

Lai, Y; Poon, J; Paul, G; Han, H; Matsubara, T

Probabilistic Pose Estimation of Deformable Linear Objects

Lai, Y

Poon, J Paul, G

Han, H Matsubara, T

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Automation Science and Engineering, 2018, 2018-August pp. 471 - 476
Issue Date:: 2018-12-04

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Field	Value	Language
dc.contributor.author	Lai, Y https://orcid.org/0000-0002-9381-5836	en_US
dc.contributor.author	Poon, J	en_US
dc.contributor.author	Paul, G https://orcid.org/0000-0002-3478-0020	en_US
dc.contributor.author	Han, H	en_US
dc.contributor.author	Matsubara, T	en_US
dc.date.available	2020-08-21T19:06:10Z
dc.date.issued	2018-12-04	en_US
dc.identifier.citation	IEEE International Conference on Automation Science and Engineering, 2018, 2018-August pp. 471 - 476	en_US
dc.identifier.isbn	9781538635933	en_US
dc.identifier.issn	2161-8070	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128550
dc.description.abstract	© 2018 IEEE. This paper presents a probabilistic framework for online tracking of nodes along deformable linear objects. The proposed framework does not require an a-priori model; instead, a Bayesian Committee Machine, starting as a tabula rasa, accumulates knowledge over time. The key benefits of this approach are a lack of reliance upon extensive pre-training data, which can be difficult to obtain in sufficiently large quantities, and the ability for robust estimation of nodes subject to occlusion. Another benefit is that the uncertainties obtained during inference from the underlying Gaussian Processes can be beneficial towards subsequent handling tasks. Comparisons of the non-time series framework were conducted against conventional regression models to measure the efficacy of the proposed framework.	en_US
dc.relation.ispartof	IEEE International Conference on Automation Science and Engineering	en_US
dc.relation.isbasedon	10.1109/COASE.2018.8560497	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Probabilistic Pose Estimation of Deformable Linear Objects	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-August	en_US
utslib.for	0906 Electrical and Electronic Engineering	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 Mechanical and Mechatronic Engineering
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.publication-status	Published	en_US
pubs.volume	2018-August	en_US

Abstract:

© 2018 IEEE. This paper presents a probabilistic framework for online tracking of nodes along deformable linear objects. The proposed framework does not require an a-priori model; instead, a Bayesian Committee Machine, starting as a tabula rasa, accumulates knowledge over time. The key benefits of this approach are a lack of reliance upon extensive pre-training data, which can be difficult to obtain in sufficiently large quantities, and the ability for robust estimation of nodes subject to occlusion. Another benefit is that the uncertainties obtained during inference from the underlying Gaussian Processes can be beneficial towards subsequent handling tasks. Comparisons of the non-time series framework were conducted against conventional regression models to measure the efficacy of the proposed framework.

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http://hdl.handle.net/10453/128550