Mapping repetitive structural tunnel environments for a biologically-inspired climbing robot

Paul, G; Mao, S; Liu, L; Xiong, R

Mapping repetitive structural tunnel environments for a biologically-inspired climbing robot

Paul, G

Mao, S Liu, L

Xiong, R

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Publication Type:: Conference Proceeding
Citation:: Assistive Robotics: Proceedings of the 18th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2015, 2015, pp. 325 - 333
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Paul, G https://orcid.org/0000-0002-3478-0020	en_US
dc.contributor.author	Mao, S	en_US
dc.contributor.author	Liu, L https://orcid.org/0000-0001-5321-5396	en_US
dc.contributor.author	Xiong, R	en_US
dc.date.available	2015-04-22	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Assistive Robotics: Proceedings of the 18th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2015, 2015, pp. 325 - 333	en_US
dc.identifier.isbn	9789814725231	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36650
dc.description.abstract	© 2015, World Scientific Publishing Co. Pte Ltd. All rights reserved. This paper presents an approach to using noisy and incomplete depth-camera datasets to detect reliable surface features for use in map construction for a caterpillar-inspired climbing robot. The approach uses a combination of plane extraction, clustering and template matching techniques to infer from the restricted dataset a usable map. This approach has been tested in both laboratory and real-world steel bridge tunnel datasets generated by a climbing robot, with the results showing that the generated maps are accurate enough for use in localisation and step trajectory planning.	en_US
dc.relation.ispartof	Assistive Robotics: Proceedings of the 18th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2015	en_US
dc.title	Mapping repetitive structural tunnel environments for a biologically-inspired climbing robot	en_US
dc.type	Conference Proceeding
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/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:

© 2015, World Scientific Publishing Co. Pte Ltd. All rights reserved. This paper presents an approach to using noisy and incomplete depth-camera datasets to detect reliable surface features for use in map construction for a caterpillar-inspired climbing robot. The approach uses a combination of plane extraction, clustering and template matching techniques to infer from the restricted dataset a usable map. This approach has been tested in both laboratory and real-world steel bridge tunnel datasets generated by a climbing robot, with the results showing that the generated maps are accurate enough for use in localisation and step trajectory planning.

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

http://hdl.handle.net/10453/36650