Towards generalization of semi-supervised place classification over generalized Voronoi graph

Shi, L; Kodagoda, S

Towards generalization of semi-supervised place classification over generalized Voronoi graph

Shi, L

Kodagoda, S

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Publication Type:: Journal Article
Citation:: Robotics and Autonomous Systems, 2013, 61 (8), pp. 785 - 796
Issue Date:: 2013-08-01

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Field	Value	Language
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.issued	2013-08-01	en_US
dc.identifier.citation	Robotics and Autonomous Systems, 2013, 61 (8), pp. 785 - 796	en_US
dc.identifier.issn	0921-8890	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27770
dc.description.abstract	With the progress of human-robot interaction (HRI), the ability of a robot to perform high-level tasks in complex environments is fast becoming an essential requirement. To this end, it is desirable for a robot to understand the environment at both geometric and semantic levels. Therefore in recent years, research towards place classification has been gaining in popularity. After the era of heuristic and rule-based approaches, supervised learning algorithms have been extensively used for this purpose, showing satisfactory performance levels. However, most of those approaches have only been trained and tested in the same environments and thus impede a generalized solution. In this paper, we have proposed a semi-supervised place classification over a generalized Voronoi graph (SPCoGVG) which is a semi-supervised learning framework comprised of three techniques: support vector machine (SVM), conditional random field (CRF) and generalized Voronoi graph (GVG), in order to improve the generalizability. The inherent problem of training CRF with partially labeled data has been solved using a novel parameter estimation algorithm. The effectiveness of the proposed algorithm is validated through extensive analysis of data collected in international university environments. © 2013 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Robotics and Autonomous Systems	en_US
dc.relation.isbasedon	10.1016/j.robot.2013.04.013	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Towards generalization of semi-supervised place classification over generalized Voronoi graph	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	61	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical 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/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	61	en_US

Abstract:

With the progress of human-robot interaction (HRI), the ability of a robot to perform high-level tasks in complex environments is fast becoming an essential requirement. To this end, it is desirable for a robot to understand the environment at both geometric and semantic levels. Therefore in recent years, research towards place classification has been gaining in popularity. After the era of heuristic and rule-based approaches, supervised learning algorithms have been extensively used for this purpose, showing satisfactory performance levels. However, most of those approaches have only been trained and tested in the same environments and thus impede a generalized solution. In this paper, we have proposed a semi-supervised place classification over a generalized Voronoi graph (SPCoGVG) which is a semi-supervised learning framework comprised of three techniques: support vector machine (SVM), conditional random field (CRF) and generalized Voronoi graph (GVG), in order to improve the generalizability. The inherent problem of training CRF with partially labeled data has been solved using a novel parameter estimation algorithm. The effectiveness of the proposed algorithm is validated through extensive analysis of data collected in international university environments. © 2013 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/27770