Multiple sensor based terrain classification

Wang, S; Kodagoda, S; Wang, Z; Dissanayake, G

Multiple sensor based terrain classification

Wang, S Kodagoda, S

Wang, Z Dissanayake, G

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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	Wang, S	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	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/10453/22383
dc.description.abstract	In the context of road vehicles, knowledge of terrain types is useful for improving passenger safety and comfort. This paper presents a method for terrain classification based on multiple sensors including an accelerometer, wheel encoders and a camera. The vertical accelerations and the speed of the vehicle together with a dynamic vehicle model are used to predict the road profile. Features extracted from the road profile are fused with image features to produce a speed invariant feature set. A supervised learning algorithm based on Neural network (NN) is used to classify different road types. Experiments carried out on an instrumented road vehicle (CRUISE), by manually driving on a variety of road types at different speeds are presented to demonstrate that the fusion of multiple sensory cues can significantly improve the road type classification accuracy.	en_US
dc.relation.ispartof	Proceedings of the 2011 Australasian Conference on Robotics and Automation	en_US
dc.title	Multiple sensor based terrain classification	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

In the context of road vehicles, knowledge of terrain types is useful for improving passenger safety and comfort. This paper presents a method for terrain classification based on multiple sensors including an accelerometer, wheel encoders and a camera. The vertical accelerations and the speed of the vehicle together with a dynamic vehicle model are used to predict the road profile. Features extracted from the road profile are fused with image features to produce a speed invariant feature set. A supervised learning algorithm based on Neural network (NN) is used to classify different road types. Experiments carried out on an instrumented road vehicle (CRUISE), by manually driving on a variety of road types at different speeds are presented to demonstrate that the fusion of multiple sensory cues can significantly improve the road type classification accuracy.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/22383