Indoor Navigation System Using the Fetch Robot

Zhu, H; Leighton, B; Chen, Y; Ke, X; Liu, S; Zhao, L

Indoor Navigation System Using the Fetch Robot

Zhu, H Leighton, B Chen, Y

Ke, X Liu, S Zhao, L

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11743 LNAI, pp. 686-696
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Zhu, H
dc.contributor.author	Leighton, B
dc.contributor.author	Chen, Y https://orcid.org/0000-0003-2756-7050
dc.contributor.author	Ke, X
dc.contributor.author	Liu, S
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.date.accessioned	2020-05-14T01:29:20Z
dc.date.available	2020-08-03T19:04:54Z
dc.date.issued	2019-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11743 LNAI, pp. 686-696
dc.identifier.isbn	9783030275372
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/140701
dc.description.abstract	© 2019, Springer Nature Switzerland AG. In this paper, we present a navigation system, including off-line mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.hasversion	Accepted Manuscript version	en_US
dc.relation.isbasedon	10.1007/978-3-030-27538-9_59
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-27538-9_59	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). The final authenticated version is available online at: https://dx.doi.org/10.1007/978-3-030-27538-9_59.	en_US
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Indoor Navigation System Using the Fetch Robot
dc.type	Conference Proceeding
utslib.citation.volume	11743 LNAI
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-05-14T01:29:15Z
pubs.publication-status	Published
pubs.volume	11743 LNAI
utslib.start-page	686

Abstract:

© 2019, Springer Nature Switzerland AG. In this paper, we present a navigation system, including off-line mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.

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http://hdl.handle.net/10453/140701