Surface type classification with a laser range finder

Kirchner, N; Liu, D; Dissanayake, G

Surface type classification with a laser range finder

Kirchner, N

Liu, D

Dissanayake, G

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Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2009, 9 (9), pp. 1160 - 1168
Issue Date:: 2009-09-01

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Field	Value	Language
dc.contributor.author	Kirchner, N https://orcid.org/0000-0002-3018-992X	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2009-09-01	en_US
dc.identifier.citation	IEEE Sensors Journal, 2009, 9 (9), pp. 1160 - 1168	en_US
dc.identifier.issn	1530-437X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8401
dc.description.abstract	This paper presents a system for surface classification using a laser range finder. It is shown that the return intensities and range errors provide sufficient information to distinguish a wide range of surfaces commonly found in a number of environments. A supervised learning scheme (using curves representing the return intensity and range error as a function of angle of incidence) is used to classify the surface type of planar patches. Extensive experimental evidence is presented to demonstrate the potential of the proposed technique. The surface type classification, which uses a typical laser range finder, is targeted for use with autonomous robotic systems in which significantly different interaction is required for each of the various materials present. Results from an on-site experiment demonstrate that the information from the laser range finder is sufficient to identify the different materials (via their surface properties) present in a scene where a bridge structure is being prepared for grit blasting. © 2009 IEEE.	en_US
dc.relation.ispartof	IEEE Sensors Journal	en_US
dc.relation.isbasedon	10.1109/JSEN.2009.2027413	en_US
dc.subject.classification	Analytical Chemistry	en_US
dc.title	Surface type classification with a laser range finder	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0205 Optical Physics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	ISI:000269213000010	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
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	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

This paper presents a system for surface classification using a laser range finder. It is shown that the return intensities and range errors provide sufficient information to distinguish a wide range of surfaces commonly found in a number of environments. A supervised learning scheme (using curves representing the return intensity and range error as a function of angle of incidence) is used to classify the surface type of planar patches. Extensive experimental evidence is presented to demonstrate the potential of the proposed technique. The surface type classification, which uses a typical laser range finder, is targeted for use with autonomous robotic systems in which significantly different interaction is required for each of the various materials present. Results from an on-site experiment demonstrate that the information from the laser range finder is sufficient to identify the different materials (via their surface properties) present in a scene where a bridge structure is being prepared for grit blasting. © 2009 IEEE.

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http://hdl.handle.net/10453/8401