Stochastic simulation in surface reconstruction and application to 3D mapping

Leal, J; Scheding, S; Dissanayake, G

Stochastic simulation in surface reconstruction and application to 3D mapping

Leal, J Scheding, S Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2002, 2 pp. 1765 - 1770
Issue Date:: 2002-01-01

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Field	Value	Language
dc.contributor.author	Leal, J	en_US
dc.contributor.author	Scheding, S	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2002-01-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2002, 2 pp. 1765 - 1770	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2837
dc.description.abstract	Three dimensional terrain maps are useful representations of environments for various robotic applications. Unfortunately, sensor data (from which such maps are built) is uncertain and contains errors which are usually not accounted for in existing terrain building algorithms. In real-time applications, it is necessary to quantify these uncertainties to allow map construction decisions to be made online. This paper addresses this issue by providing a representation that explicitly accounts for sensing uncertainty. This is achieved through the use of stochastic simulation techniques. The result is in an algorithm for online 3D multi-resolution surface reconstruction of unknown, and unstructured environments. Results of the surface reconstruction algorithm in a real environment are presented.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ROBOT.2002.1014797	en_US
dc.title	Stochastic simulation in surface reconstruction and application to 3D mapping	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Washington DC,USA	en_US
dc.location.activity	WASHINGTON, DC
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
pubs.volume	2	en_US

Abstract:

Three dimensional terrain maps are useful representations of environments for various robotic applications. Unfortunately, sensor data (from which such maps are built) is uncertain and contains errors which are usually not accounted for in existing terrain building algorithms. In real-time applications, it is necessary to quantify these uncertainties to allow map construction decisions to be made online. This paper addresses this issue by providing a representation that explicitly accounts for sensing uncertainty. This is achieved through the use of stochastic simulation techniques. The result is in an algorithm for online 3D multi-resolution surface reconstruction of unknown, and unstructured environments. Results of the surface reconstruction algorithm in a real environment are presented.

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http://hdl.handle.net/10453/2837