2021 IEEE RAS Winter School on Simultaneous Localization and Mapping in Deformable Environments [Education]

Huang, S; Zhao, L

2021 IEEE RAS Winter School on Simultaneous Localization and Mapping in Deformable Environments [Education]

Huang, S

Zhao, L

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Magazine, 2022, 29, (1), pp. 120-122
Issue Date:: 2022-03-01

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Field	Value	Language
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.date.accessioned	2023-01-19T02:01:20Z
dc.date.available	2023-01-19T02:01:20Z
dc.date.issued	2022-03-01
dc.identifier.citation	IEEE Robotics and Automation Magazine, 2022, 29, (1), pp. 120-122
dc.identifier.issn	1070-9932
dc.identifier.issn	1558-223X
dc.identifier.uri	http://hdl.handle.net/10453/165175
dc.description.abstract	Simultaneous localization and mapping (SLAM) is an important research problem for robot navigation in unknown environments, particularly when GPS is not available. SLAM requires a robot to be able to build a map of the environment in real time and simultaneously estimate its own location within the map. In the past two decades, significant progress has been made in the research for SLAM in static environments. However, when an environment has deformations, such as when a surgical robot is navigating in internal body environments, SLAM needs to build a time-varying 3D map of the soft tissues and estimate the location of the robot/sensor within the map. This poses a challenging problem since the robot/sensor is moving while the environment is deforming.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP200100982
dc.relation.ispartof	IEEE Robotics and Automation Magazine
dc.relation.isbasedon	10.1109/MRA.2022.3145919
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	2021 IEEE RAS Winter School on Simultaneous Localization and Mapping in Deformable Environments [Education]
dc.type	Journal Article
utslib.citation.volume	29
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
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	2024-03-01T00:00:00+1000Z
dc.date.updated	2023-01-19T02:01:18Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	29
utslib.citation.issue	1

Abstract:

Simultaneous localization and mapping (SLAM) is an important research problem for robot navigation in unknown environments, particularly when GPS is not available. SLAM requires a robot to be able to build a map of the environment in real time and simultaneously estimate its own location within the map. In the past two decades, significant progress has been made in the research for SLAM in static environments. However, when an environment has deformations, such as when a surgical robot is navigating in internal body environments, SLAM needs to build a time-varying 3D map of the soft tissues and estimate the location of the robot/sensor within the map. This poses a challenging problem since the robot/sensor is moving while the environment is deforming.

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http://hdl.handle.net/10453/165175