Exploration on continuous Gaussian process frontier maps

Jadidi, MG; Miro, JV; Valencia, R; Andrade-Cetto, J

Exploration on continuous Gaussian process frontier maps

Jadidi, MG Miro, JV

Valencia, R Andrade-Cetto, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2014, pp. 6077 - 6082
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Jadidi, MG	en_US
dc.contributor.author	Miro, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Valencia, R	en_US
dc.contributor.author	Andrade-Cetto, J	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2014, pp. 6077 - 6082	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34406
dc.description.abstract	© 2014 IEEE. An information-driven autonomous robotic exploration method on a continuous representation of unknown environments is proposed in This paper. The approach conveniently handles sparse sensor measurements To build a continuous model of The environment That exploits structural dependencies without The need To resort To a fixed resolution grid map. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian process providing frontier boundaries for further exploration. The resulting continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic stop criterion for a desired sensitivity. The performance of The proposed approach is evaluated Through simulation results in The well-known Freiburg and Cave maps.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2014.6907754	en_US
dc.title	Exploration on continuous Gaussian process frontier maps	en_US
dc.type	Conference Proceeding
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Hong Kong
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/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. An information-driven autonomous robotic exploration method on a continuous representation of unknown environments is proposed in This paper. The approach conveniently handles sparse sensor measurements To build a continuous model of The environment That exploits structural dependencies without The need To resort To a fixed resolution grid map. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian process providing frontier boundaries for further exploration. The resulting continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic stop criterion for a desired sensitivity. The performance of The proposed approach is evaluated Through simulation results in The well-known Freiburg and Cave maps.

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http://hdl.handle.net/10453/34406