3D landmarks extraction from a range imager data for SLAM

Wang, JJ; Hu, G; Huang, S; Dissanayake, G

3D landmarks extraction from a range imager data for SLAM

Wang, JJ

Hu, G Huang, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2009 Australasian Conference on Robotics and Automation, ACRA 2009, 2009
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Wang, JJ https://orcid.org/0000-0002-6282-0456	en_US
dc.contributor.author	Hu, G	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Proceedings of the 2009 Australasian Conference on Robotics and Automation, ACRA 2009, 2009	en_US
dc.identifier.isbn	9780980740400	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11099
dc.description.abstract	This paper introduces a new 3D landmark extraction method using the range and intensity images captured by a single range camera. Speeded up robust features (SURF) detection and matching is used to extract and match features from the intensity images. The range image information is used to transfer the selected 2D features into 3D points. The range measurement bias and uncertainty of the range camera are analysed, and their models are developed for improving the range estimation. After outliers' detection and removal using random sampling consensus (RANSAC), reliable 3D points are obtained. 3D landmarks for simultaneous localisation and mapping (SLAM) are selected from the 3D points considering several factors, such as the uncertainty and geometry of their locations. Because of the availability of the SURF descriptor, the data association in SLAM has been performed using both the geometry and the descriptor information. The proposed method is tested in unstructured indoor environments, where the range camera moves in six degrees of freedom. Experimental results demonstrate the success of the proposed 3D landmark extraction method for SLAM.	en_US
dc.relation.ispartof	Proceedings of the 2009 Australasian Conference on Robotics and Automation, ACRA 2009	en_US
dc.title	3D landmarks extraction from a range imager data for SLAM	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Sydney, 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 Civil and Environmental Engineering
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/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

This paper introduces a new 3D landmark extraction method using the range and intensity images captured by a single range camera. Speeded up robust features (SURF) detection and matching is used to extract and match features from the intensity images. The range image information is used to transfer the selected 2D features into 3D points. The range measurement bias and uncertainty of the range camera are analysed, and their models are developed for improving the range estimation. After outliers' detection and removal using random sampling consensus (RANSAC), reliable 3D points are obtained. 3D landmarks for simultaneous localisation and mapping (SLAM) are selected from the 3D points considering several factors, such as the uncertainty and geometry of their locations. Because of the availability of the SURF descriptor, the data association in SLAM has been performed using both the geometry and the descriptor information. The proposed method is tested in unstructured indoor environments, where the range camera moves in six degrees of freedom. Experimental results demonstrate the success of the proposed 3D landmark extraction method for SLAM.

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http://hdl.handle.net/10453/11099