Gaussian Process Preintegration for Inertial-Aided State Estimation

Le Gentil, C; Vidal-Calleja, T; Huang, S

Gaussian Process Preintegration for Inertial-Aided State Estimation

Le Gentil, C

Vidal-Calleja, T Huang, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2020, 5, (2), pp. 2108-2114
Issue Date:: 2020-04-01

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Field	Value	Language
dc.contributor.author	Le Gentil, C https://orcid.org/0000-0002-9790-5935
dc.contributor.author	Vidal-Calleja, T
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.date.accessioned	2021-01-10T22:51:29Z
dc.date.available	2021-01-10T22:51:29Z
dc.date.issued	2020-04-01
dc.identifier.citation	IEEE Robotics and Automation Letters, 2020, 5, (2), pp. 2108-2114
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3766
dc.identifier.uri	http://hdl.handle.net/10453/145251
dc.description.abstract	© 2020 IEEE. In this letter, we present Gaussian Process Preintegration, a preintegration theory based on continuous representations of inertial measurements. A novel use of linear operators on Gaussian Process kernels is employed to generate the proposed Gaussian Preintegrated Measurements (GPMs). This formulation allows the analytical integration of inertial signals on any time interval. Consequently, GPMs are especially suited for asynchronous inertial-aided estimation frameworks. Unlike discrete preintegration approaches, the proposed method does not rely on any explicit motion-model and does not suffer from numerical integration noise. Additionally, we provide the analytical derivation of the Jacobians involved in the first-order expansion for postintegration bias and inter-sensor time-shift correction. We benchmarked the proposed method against existing preintegration methods on simulated data. Our experiments show that GPMs produce the most accurate results and their computation time allows close-to-real-time operations. We validated the suitability of GPMs for inertial-aided estimation by integrating them into a lidar-inertial localisation and mapping framework.
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.2020.2970940
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0913 Mechanical Engineering
dc.title	Gaussian Process Preintegration for Inertial-Aided State Estimation
dc.type	Journal Article
utslib.citation.volume	5
utslib.for	0913 Mechanical 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
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Design
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
utslib.copyright.embargo	2022-02-03T00:00:00+1000Z
dc.date.updated	2021-01-10T22:51:06Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	5
utslib.citation.issue	2

Abstract:

© 2020 IEEE. In this letter, we present Gaussian Process Preintegration, a preintegration theory based on continuous representations of inertial measurements. A novel use of linear operators on Gaussian Process kernels is employed to generate the proposed Gaussian Preintegrated Measurements (GPMs). This formulation allows the analytical integration of inertial signals on any time interval. Consequently, GPMs are especially suited for asynchronous inertial-aided estimation frameworks. Unlike discrete preintegration approaches, the proposed method does not rely on any explicit motion-model and does not suffer from numerical integration noise. Additionally, we provide the analytical derivation of the Jacobians involved in the first-order expansion for postintegration bias and inter-sensor time-shift correction. We benchmarked the proposed method against existing preintegration methods on simulated data. Our experiments show that GPMs produce the most accurate results and their computation time allows close-to-real-time operations. We validated the suitability of GPMs for inertial-aided estimation by integrating them into a lidar-inertial localisation and mapping framework.

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http://hdl.handle.net/10453/145251