On-line 3D active pose-graph SLAM based on key poses using graph topology and sub-maps

Chen, Y; Huang, S; Fitch, R; Zhao, L; Yu, H; Yang, D

On-line 3D active pose-graph SLAM based on key poses using graph topology and sub-maps

Chen, Y

Huang, S

Fitch, R

Zhao, L

Yu, H Yang, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2019, 2019-May pp. 169 - 175
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Chen, Y https://orcid.org/0000-0003-2756-7050	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183	en_US
dc.contributor.author	Yu, H	en_US
dc.contributor.author	Yang, D	en_US
dc.date.available	2021-05-18T19:02:37Z
dc.date.issued	2019-05-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2019, 2019-May pp. 169 - 175	en_US
dc.identifier.isbn	9781538660263	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136452
dc.description.abstract	© 2019 IEEE. In this paper, we present an on-line active pose-graph simultaneous localization and mapping (SLAM) frame-work for robots in three-dimensional (3D) environments using graph topology and sub-maps. This framework aims to find the best trajectory for loop-closure by re-visiting old poses based on the T-optimality and D-optimality metrics of the Fisher information matrix (FIM) in pose-graph SLAM. In order to reduce computational complexity, graph topologies are introduced, including weighted node degree (T-optimality metric) and weighted tree-connectivity (D-optimality metric), to choose a candidate trajectory and several key poses. With the help of the key poses, a sampling-based path planning method and a continuous-time trajectory optimization method are combined hierarchically and applied in the whole framework. So as to further improve the real-time capability of the method, the sub-map joining method is used in the estimation and planning process for large-scale active SLAM problems. In simulations and experiments, we validate our approach by comparing against existing methods, and we demonstrate the on-line planning part using a quad-rotor unmanned aerial vehicle (UAV).	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2019.8793632	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	On-line 3D active pose-graph SLAM based on key poses using graph topology and sub-maps	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-May	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	2019-May	en_US

Abstract:

© 2019 IEEE. In this paper, we present an on-line active pose-graph simultaneous localization and mapping (SLAM) frame-work for robots in three-dimensional (3D) environments using graph topology and sub-maps. This framework aims to find the best trajectory for loop-closure by re-visiting old poses based on the T-optimality and D-optimality metrics of the Fisher information matrix (FIM) in pose-graph SLAM. In order to reduce computational complexity, graph topologies are introduced, including weighted node degree (T-optimality metric) and weighted tree-connectivity (D-optimality metric), to choose a candidate trajectory and several key poses. With the help of the key poses, a sampling-based path planning method and a continuous-time trajectory optimization method are combined hierarchically and applied in the whole framework. So as to further improve the real-time capability of the method, the sub-map joining method is used in the estimation and planning process for large-scale active SLAM problems. In simulations and experiments, we validate our approach by comparing against existing methods, and we demonstrate the on-line planning part using a quad-rotor unmanned aerial vehicle (UAV).

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http://hdl.handle.net/10453/136452