Adaptive-Resolution Field Mapping Using Gaussian Process Fusion with Integral Kernels

Jin, L; Ruckin, J; Kiss, SH; Vidal-Calleja, T; Popovic, M

Adaptive-Resolution Field Mapping Using Gaussian Process Fusion with Integral Kernels

Jin, L Ruckin, J Kiss, SH Vidal-Calleja, T Popovic, M

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2022, 7, (3), pp. 7471-7478
Issue Date:: 2022-07-01

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Field	Value	Language
dc.contributor.author	Jin, L
dc.contributor.author	Ruckin, J
dc.contributor.author	Kiss, SH
dc.contributor.author	Vidal-Calleja, T
dc.contributor.author	Popovic, M
dc.date.accessioned	2023-02-02T05:36:00Z
dc.date.available	2023-02-02T05:36:00Z
dc.date.issued	2022-07-01
dc.identifier.citation	IEEE Robotics and Automation Letters, 2022, 7, (3), pp. 7471-7478
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3766
dc.identifier.uri	http://hdl.handle.net/10453/165852
dc.description.abstract	Unmanned aerial vehicles are rapidly gaining popularity in many environmental monitoring tasks. A prerequisite for their autonomous operation is the ability to perform efficient and accurate mapping online, given limited on-board resources constraining operation time and computational capacity. To address this, we present an online adaptive-resolution approach for field mapping based on Gaussian Process fusion, a strategy in which Bayesian fusion is applied to update a Gaussian Process prior map. A key aspect of our approach is an integral kernel encoding spatial correlation over the areas of grid cells. This enables efficient information compression in uninteresting areas to achieve a compact map representation while maintaining spatial correlations in a theoretically sound fashion. We evaluate the performance of our approach on both synthetic and real-world data. Results show that our method is more efficient in terms of mapping time and memory consumption without compromising on map quality. Further, we integrate our mapping strategy into an adaptive path planning framework to show that it facilitates information gathering efficiency in online settings.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Robotics and Automation Letters
dc.relation.isbasedon	10.1109/LRA.2022.3183797
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0913 Mechanical Engineering
dc.title	Adaptive-Resolution Field Mapping Using Gaussian Process Fusion with Integral Kernels
dc.type	Journal Article
utslib.citation.volume	7
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	2024-07-31T00:00:00+1000Z
dc.date.updated	2023-02-02T05:35:59Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	3

Abstract:

Unmanned aerial vehicles are rapidly gaining popularity in many environmental monitoring tasks. A prerequisite for their autonomous operation is the ability to perform efficient and accurate mapping online, given limited on-board resources constraining operation time and computational capacity. To address this, we present an online adaptive-resolution approach for field mapping based on Gaussian Process fusion, a strategy in which Bayesian fusion is applied to update a Gaussian Process prior map. A key aspect of our approach is an integral kernel encoding spatial correlation over the areas of grid cells. This enables efficient information compression in uninteresting areas to achieve a compact map representation while maintaining spatial correlations in a theoretically sound fashion. We evaluate the performance of our approach on both synthetic and real-world data. Results show that our method is more efficient in terms of mapping time and memory consumption without compromising on map quality. Further, we integrate our mapping strategy into an adaptive path planning framework to show that it facilitates information gathering efficiency in online settings.

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http://hdl.handle.net/10453/165852