RF-MVO: Simultaneous 3D object localization and camera trajectory recovery using RFID Devices and a 2D monocular camera

Wang, Z; Xu, M; Ye, N; Wang, R; Huang, H

RF-MVO: Simultaneous 3D object localization and camera trajectory recovery using RFID Devices and a 2D monocular camera

Wang, Z

Xu, M

Ye, N Wang, R Huang, H

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Distributed Computing Systems, 2018, 2018-July pp. 534 - 544
Issue Date:: 2018-07-19

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Field	Value	Language
dc.contributor.author	Wang, Z https://orcid.org/0000-0003-3611-9540	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Ye, N	en_US
dc.contributor.author	Wang, R	en_US
dc.contributor.author	Huang, H	en_US
dc.date.available	2020-06-02T19:06:50Z
dc.date.issued	2018-07-19	en_US
dc.identifier.citation	Proceedings - International Conference on Distributed Computing Systems, 2018, 2018-July pp. 534 - 544	en_US
dc.identifier.isbn	9781538668719	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126179
dc.description.abstract	© 2018 IEEE. Most of the existing RFID-based localization systems cannot well locate RFID-tagged objects in a 3D space. Limited robot-based RFID solutions require reader antennas to be carried by a robot moving along an already-known trajectory at a constant speed. As the first attempt, this paper presents RF-MVO, which fuses battery-free RFID and monocular visual odometry to locate stationary RFID tags in a 3D space and recover an unknown trajectory of reader antennas binding with a 2D monocular camera. The proposed hybrid system exhibits three unique features. Firstly, since the trajectory of a 2D monocular camera can only be recovered up to an unknown scale factor, RF-MVO combines the relative-scale camera trajectory with depth-enabled RF phase to estimate an absolute scale factor and spatially incident angles of an RFID tag. Secondly, we propose a joint optimization algorithm consisting of coarse-to-fine angular refinement, 3D tag localization and parameter nonlinear optimization, to improve real-time performance. Thirdly, RF-MVO can determine the effect of relative tag-antenna geometry on the estimation precision, providing optimal tag positions and absolute scale factors. Our experiments show that RF-MVO can achieve 6.23cm tag localization accuracy in a 3D space and 0.0158 absolute scale factor estimation accuracy for camera trajectory recovery.	en_US
dc.relation.ispartof	Proceedings - International Conference on Distributed Computing Systems	en_US
dc.relation.isbasedon	10.1109/ICDCS.2018.00059	en_US
dc.title	RF-MVO: Simultaneous 3D object localization and camera trajectory recovery using RFID Devices and a 2D monocular camera	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0805 Distributed Computing	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. Most of the existing RFID-based localization systems cannot well locate RFID-tagged objects in a 3D space. Limited robot-based RFID solutions require reader antennas to be carried by a robot moving along an already-known trajectory at a constant speed. As the first attempt, this paper presents RF-MVO, which fuses battery-free RFID and monocular visual odometry to locate stationary RFID tags in a 3D space and recover an unknown trajectory of reader antennas binding with a 2D monocular camera. The proposed hybrid system exhibits three unique features. Firstly, since the trajectory of a 2D monocular camera can only be recovered up to an unknown scale factor, RF-MVO combines the relative-scale camera trajectory with depth-enabled RF phase to estimate an absolute scale factor and spatially incident angles of an RFID tag. Secondly, we propose a joint optimization algorithm consisting of coarse-to-fine angular refinement, 3D tag localization and parameter nonlinear optimization, to improve real-time performance. Thirdly, RF-MVO can determine the effect of relative tag-antenna geometry on the estimation precision, providing optimal tag positions and absolute scale factors. Our experiments show that RF-MVO can achieve 6.23cm tag localization accuracy in a 3D space and 0.0158 absolute scale factor estimation accuracy for camera trajectory recovery.

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http://hdl.handle.net/10453/126179