Efficient active SLAM based on submap joining

Chen, Y; Huang, S; Fitch, R; Yu, J

Efficient active SLAM based on submap joining

Chen, Y

Huang, S

Fitch, R

Yu, J

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2017, 2017-December pp. 141 - 147
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Chen, Y https://orcid.org/0000-0003-2756-7050	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.contributor.author	Yu, J	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2017, 2017-December pp. 141 - 147	en_US
dc.identifier.isbn	9781510860117	en_US
dc.identifier.issn	1448-2053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127266
dc.description.abstract	This paper considers the active SLAM problem where a robot is required to cover a given area while at the same time performing simultaneous localization and mapping (SLAM) for understanding the environment and localizing the robot itself. We propose a model predictive control (MPC) framework, and the minimization of uncertainty in SLAM and coverage problems are solved respectively by the Sequential Quadratic Programming (SQP) method. Then, a decision making process is used to control the switching of two control inputs. In order to reduce the estimation and planning time, we use Linear SLAM, which is a submap joining approach. Simulation results are presented to validate the effectiveness of the proposed active SLAM strategy.	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Efficient active SLAM based on submap joining	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-December	en_US
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
pubs.volume	2017-December	en_US

Abstract:

This paper considers the active SLAM problem where a robot is required to cover a given area while at the same time performing simultaneous localization and mapping (SLAM) for understanding the environment and localizing the robot itself. We propose a model predictive control (MPC) framework, and the minimization of uncertainty in SLAM and coverage problems are solved respectively by the Sequential Quadratic Programming (SQP) method. Then, a decision making process is used to control the switching of two control inputs. In order to reduce the estimation and planning time, we use Linear SLAM, which is a submap joining approach. Simulation results are presented to validate the effectiveness of the proposed active SLAM strategy.

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http://hdl.handle.net/10453/127266