Conic Feature Based Simultaneous Localization and Mapping in Open Environment via 2D Lidar

Zhao, J; Huang, S; Zhao, L; Chen, Y; Luo, X

Conic Feature Based Simultaneous Localization and Mapping in Open Environment via 2D Lidar

Zhao, J

Huang, S

Zhao, L

Chen, Y

Luo, X

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Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7 pp. 173703 - 173718
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Zhao, J https://orcid.org/0000-0002-3195-8639	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183	en_US
dc.contributor.author	Chen, Y https://orcid.org/0000-0003-2756-7050	en_US
dc.contributor.author	Luo, X	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Access, 2019, 7 pp. 173703 - 173718	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138303
dc.description.abstract	© 2013 IEEE. The conventional planar scan matching approach cannot cope well with the open environment as lacking of sufficient edges and corners. This paper presents a conic feature based simultaneous localization and mapping (SLAM) algorithm via 2D lidar which can adapt to an open environment nicely. The novelty of this work includes threefold: (1) defining a conic feature based parametrization approach; (2) developing a method to utilize feature's conic geometric information and odometry information since open environments are short of regular linear geometric features; (3) developing a factor graph based framework which can be adapted with the proposed parametrization. Simulation experiments and real environment experiments demonstrated that the proposed SLAM algorithm can get accurate and convincing results for the open environment and the map in our representation can express accurately the environment situation.	en_US
dc.relation.ispartof	IEEE Access	en_US
dc.relation.isbasedon	10.1109/ACCESS.2019.2956563	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Conic Feature Based Simultaneous Localization and Mapping in Open Environment via 2D Lidar	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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	7	en_US

Abstract:

© 2013 IEEE. The conventional planar scan matching approach cannot cope well with the open environment as lacking of sufficient edges and corners. This paper presents a conic feature based simultaneous localization and mapping (SLAM) algorithm via 2D lidar which can adapt to an open environment nicely. The novelty of this work includes threefold: (1) defining a conic feature based parametrization approach; (2) developing a method to utilize feature's conic geometric information and odometry information since open environments are short of regular linear geometric features; (3) developing a factor graph based framework which can be adapted with the proposed parametrization. Simulation experiments and real environment experiments demonstrated that the proposed SLAM algorithm can get accurate and convincing results for the open environment and the map in our representation can express accurately the environment situation.

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http://hdl.handle.net/10453/138303