Fast indoor scene classification using 3D point clouds

Shi, L; Kodagoda, S; Ranasinghe, R

Fast indoor scene classification using 3D point clouds

Shi, L

Kodagoda, S

Ranasinghe, R

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2011 Australasian Conference on Robotics and Automation, 2011
Issue Date:: 2011-12-01

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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.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings of the 2011 Australasian Conference on Robotics and Automation, 2011	en_US
dc.identifier.isbn	9780980740424	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19086
dc.description.abstract	A representation of space that includes both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Identifying and categorizing environments based on onboard sensors are essential in these scenarios. The Kinect™, a 3D low cost sensor is appealing in these scenarios as it can provide rich information. The downside is the presence of large amount of information, which could lead to higher computational complexity. In this paper, we propose a methodology to efficiently classify indoor environments into semantic categories using Kinect™ data. With a fast feature extraction method along with an efficient feature selection algorithm (DEFS) and, support vector machines (SVM) classifier, we could realize a fast scene classification algorithm. Experimental results in an indoor scenario are presented including comparisons with its counterpart of commonly available 2D laser range finder data.	en_US
dc.relation.ispartof	Proceedings of the 2011 Australasian Conference on Robotics and Automation	en_US
dc.title	Fast indoor scene classification using 3D point clouds	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
dc.location.activity	Monash University, Melbourne Australia	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
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

A representation of space that includes both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Identifying and categorizing environments based on onboard sensors are essential in these scenarios. The Kinect™, a 3D low cost sensor is appealing in these scenarios as it can provide rich information. The downside is the presence of large amount of information, which could lead to higher computational complexity. In this paper, we propose a methodology to efficiently classify indoor environments into semantic categories using Kinect™ data. With a fast feature extraction method along with an efficient feature selection algorithm (DEFS) and, support vector machines (SVM) classifier, we could realize a fast scene classification algorithm. Experimental results in an indoor scenario are presented including comparisons with its counterpart of commonly available 2D laser range finder data.

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http://hdl.handle.net/10453/19086