Evaluation of different SLAM algorithms using Google tangle data

Liu, L; Wang, Y; Zhao, L; Huang, S

Evaluation of different SLAM algorithms using Google tangle data

Liu, L

Wang, Y Zhao, L

Huang, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, 2018, 2018-February pp. 1954 - 1959
Issue Date:: 2018-02-05

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Field	Value	Language
dc.contributor.author	Liu, L https://orcid.org/0000-0001-5321-5396	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.date.issued	2018-02-05	en_US
dc.identifier.citation	Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, 2018, 2018-February pp. 1954 - 1959	en_US
dc.identifier.isbn	9781538621035	en_US
dc.identifier.uri	http://hdl.handle.net/10453/107814
dc.description.abstract	© 2017 IEEE. In this paper, we evaluate three state-of-the-art Simultaneous Localization and Mapping (SLAM) methods using data extracted from a state-of-the-art device for indoor navigation - the Google Tango tablet. The SLAM algorithms we investigated include Preintegration Visual Inertial Navigation System (VINS), ParallaxBA and ORB-SLAM. We first describe the detailed process of obtaining synchronized IMU and image data from the Google Tango device, then we present some of the SLAM results obtained using the three different SLAM algorithms, all with the datasets collected from Tango. These SLAM results are compared with that obtained from Tango's inbuilt motion tracking system. The advantages and failure modes of the different SLAM algorithms are analysed and illustrated thereafter. The evaluation results presented in this paper are expected to provide some guidance on further development of more robust SLAM algorithms for robotic applications.	en_US
dc.relation.ispartof	Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017	en_US
dc.relation.isbasedon	10.1109/ICIEA.2017.8283158	en_US
dc.title	Evaluation of different SLAM algorithms using Google tangle data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-February	en_US
utslib.for	0906 Electrical and Electronic 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	2018-February	en_US

Abstract:

© 2017 IEEE. In this paper, we evaluate three state-of-the-art Simultaneous Localization and Mapping (SLAM) methods using data extracted from a state-of-the-art device for indoor navigation - the Google Tango tablet. The SLAM algorithms we investigated include Preintegration Visual Inertial Navigation System (VINS), ParallaxBA and ORB-SLAM. We first describe the detailed process of obtaining synchronized IMU and image data from the Google Tango device, then we present some of the SLAM results obtained using the three different SLAM algorithms, all with the datasets collected from Tango. These SLAM results are compared with that obtained from Tango's inbuilt motion tracking system. The advantages and failure modes of the different SLAM algorithms are analysed and illustrated thereafter. The evaluation results presented in this paper are expected to provide some guidance on further development of more robust SLAM algorithms for robotic applications.

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http://hdl.handle.net/10453/107814