Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization

Wickramanayake, S; Thiyagarajan, K; Kodagoda, S

Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization

Wickramanayake, S Thiyagarajan, K

Kodagoda, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of IEEE Sensors, 2022, 2022-October, pp. 1-4
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Wickramanayake, S
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138
dc.date	2022-10-30
dc.date.accessioned	2023-01-16T03:23:28Z
dc.date.available	2023-01-16T03:23:28Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings of IEEE Sensors, 2022, 2022-October, pp. 1-4
dc.identifier.isbn	9781665484640
dc.identifier.issn	1930-0395
dc.identifier.issn	2168-9229
dc.identifier.uri	http://hdl.handle.net/10453/164994
dc.description.abstract	Sensors help robots perceive their environment and localize themselves. Determining a robot's location requires a range of sensing systems. Depending on accuracy criteria and navigation conditions, robot localization sensors can differ. Common sensors for robot localization include encoders, GPS, cameras, LIDARs, and IMUs. Traditional sensors are not capable enough in changing environments and uneven terrain. In this paper, we propose a method based on deep learning to use the subsurface features obtained through a Ground Penetrating Radar (GPR) to estimate the odometry of a robot. This proposed method does not rely on visual features or the distance gathered from wheel encoders. The proposed approach was evaluated on a publicly available dataset, and the evaluation results show that the proposed method can be used for robot localization without the need for odometry from wheel encoders.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of IEEE Sensors
dc.relation.ispartof	2022 IEEE Sensors
dc.relation.isbasedon	10.1109/SENSORS52175.2022.9967350
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Deep Learned Ground Penetrating Radar Subsurface Features for Robot Localization
dc.type	Conference Proceeding
utslib.citation.volume	2022-October
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
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-01-01T00:00:00+1000Z
dc.date.updated	2023-01-16T03:23:27Z
pubs.finish-date	2022-11-02
pubs.publication-status	Published
pubs.start-date	2022-10-30
pubs.volume	2022-October

Abstract:

Sensors help robots perceive their environment and localize themselves. Determining a robot's location requires a range of sensing systems. Depending on accuracy criteria and navigation conditions, robot localization sensors can differ. Common sensors for robot localization include encoders, GPS, cameras, LIDARs, and IMUs. Traditional sensors are not capable enough in changing environments and uneven terrain. In this paper, we propose a method based on deep learning to use the subsurface features obtained through a Ground Penetrating Radar (GPR) to estimate the odometry of a robot. This proposed method does not rely on visual features or the distance gathered from wheel encoders. The proposed approach was evaluated on a publicly available dataset, and the evaluation results show that the proposed method can be used for robot localization without the need for odometry from wheel encoders.

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http://hdl.handle.net/10453/164994