GEM: online globally consistent dense elevation mapping for unstructured terrain

Pan, Y; Xu, X; Ding, X; Huang, S; Wang, Y; Xiong, R

GEM: online globally consistent dense elevation mapping for unstructured terrain

Pan, Y Xu, X Ding, X Huang, S

Wang, Y Xiong, R

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Instrumentation and Measurement, 2020, 70, (99), pp. 1-13
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Pan, Y
dc.contributor.author	Xu, X
dc.contributor.author	Ding, X
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.contributor.author	Wang, Y
dc.contributor.author	Xiong, R
dc.date.accessioned	2021-01-31T21:46:02Z
dc.date.available	2021-01-31T21:46:02Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Transactions on Instrumentation and Measurement, 2020, 70, (99), pp. 1-13
dc.identifier.issn	0018-9456
dc.identifier.issn	1557-9662
dc.identifier.uri	http://hdl.handle.net/10453/145682
dc.description.abstract	IEEE Online dense mapping gives a representation of the unstructured terrain, which is indispensable for safe robotic motion planning. In this paper, we propose such an elevation mapping system, namely GEM, to generate a dense local elevation map in constant real-time for fast responsive local planning, and maintain a globally consistent dense map for path routing at the same time. We model the global elevation map as a collection of submaps. When the trajectory estimation of the robot is corrected by SLAM, only relative poses between submaps are updated without re-building the submap. As a result, this deformable global dense map representation is able to keep the global consistency online. Besides, we accelerate the local mapping by integrating traversability analysis into the mapping system to save the computation cost by obstacle awareness. The system is implemented by CPU-GPU coordinated processing to guarantee constant real-time performance for in-time handling of dynamic obstacles. Substantial experimental results on both simulated and real-world dataset validate the efficiency and effectiveness of GEM.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Instrumentation and Measurement
dc.relation.isbasedon	10.1109/TIM.2020.3044338
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0299 Other Physical Sciences, 0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	GEM: online globally consistent dense elevation mapping for unstructured terrain
dc.type	Journal Article
utslib.citation.volume	70
utslib.for	0299 Other Physical Sciences
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
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
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2022-12-14T00:00:00+1000Z
dc.date.updated	2021-01-31T21:45:49Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	70
utslib.citation.issue	99

Abstract:

IEEE Online dense mapping gives a representation of the unstructured terrain, which is indispensable for safe robotic motion planning. In this paper, we propose such an elevation mapping system, namely GEM, to generate a dense local elevation map in constant real-time for fast responsive local planning, and maintain a globally consistent dense map for path routing at the same time. We model the global elevation map as a collection of submaps. When the trajectory estimation of the robot is corrected by SLAM, only relative poses between submaps are updated without re-building the submap. As a result, this deformable global dense map representation is able to keep the global consistency online. Besides, we accelerate the local mapping by integrating traversability analysis into the mapping system to save the computation cost by obstacle awareness. The system is implemented by CPU-GPU coordinated processing to guarantee constant real-time performance for in-time handling of dynamic obstacles. Substantial experimental results on both simulated and real-world dataset validate the efficiency and effectiveness of GEM.

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http://hdl.handle.net/10453/145682