Continuous latent state preintegration for inertial-aided systems

Le Gentil, C; Vidal-Calleja, T

Continuous latent state preintegration for inertial-aided systems

Le Gentil, C

Vidal-Calleja, T

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Publisher:: SAGE PUBLICATIONS LTD
Publication Type:: Journal Article
Citation:: International Journal of Robotics Research, 2023, 42, (10)
Issue Date:: 2023-01-01

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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.date.accessioned	2023-11-06T05:16:57Z
dc.date.available	2023-11-06T05:16:57Z
dc.date.issued	2023-01-01
dc.identifier.citation	International Journal of Robotics Research, 2023, 42, (10)
dc.identifier.issn	0278-3649
dc.identifier.issn	1741-3176
dc.identifier.uri	http://hdl.handle.net/10453/173051
dc.description.abstract	Traditionally, the pose and velocity prediction of a system at time t2 given inertial measurements from a 6-DoF IMU depends on the knowledge of the system’s state at time t1. It involves a series of integration and double integration that can be computationally expensive if performed regularly, in particular in the context of inertial-aided optimisation-based state estimation. The concept of preintegration consists of creating pseudo-measurements that are independent of the system’s initial conditions (pose and velocity at t1) in order to predict the system’s state at t2. These pseudo-measurements, so-called preintegrated measurements, were originally computed numerically using the integration rectangle rule. This article presents a novel method to perform continuous preintegration using Gaussian processes (GPs) to model the system’s dynamics focusing on high accuracy. It represents the preintegrated measurement’s derivatives in a continuous latent state that is learnt/optimised according to asynchronous IMU gyroscope and accelerometer measurements. The GP models allow for analytical integration and double integration of the latent state to generate accurate preintegrated measurements called unified Gaussian preintegrated measurements (UGPMs). We show through extensive quantitative experiments that the proposed UGPMs outperform the standard preintegration method by an order of magnitude. Additionally, we demonstrate that the UGPMs can be integrated into off-the-shelf multi-modal estimation frameworks with ease based on lidar-inertial, RGBD-inertial, and visual-inertial real-world experiments.
dc.language	English
dc.publisher	SAGE PUBLICATIONS LTD
dc.relation	http://purl.org/au-research/grants/arc/DP210101336
dc.relation.ispartof	International Journal of Robotics Research
dc.relation.isbasedon	10.1177/02783649231199537
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	Continuous latent state preintegration for inertial-aided systems
dc.type	Journal Article
utslib.citation.volume	42
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/Strength - RI - Robotics Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-06T05:16:56Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	42
utslib.citation.issue	10

Abstract:

Traditionally, the pose and velocity prediction of a system at time t2 given inertial measurements from a 6-DoF IMU depends on the knowledge of the system’s state at time t1. It involves a series of integration and double integration that can be computationally expensive if performed regularly, in particular in the context of inertial-aided optimisation-based state estimation. The concept of preintegration consists of creating pseudo-measurements that are independent of the system’s initial conditions (pose and velocity at t1) in order to predict the system’s state at t2. These pseudo-measurements, so-called preintegrated measurements, were originally computed numerically using the integration rectangle rule. This article presents a novel method to perform continuous preintegration using Gaussian processes (GPs) to model the system’s dynamics focusing on high accuracy. It represents the preintegrated measurement’s derivatives in a continuous latent state that is learnt/optimised according to asynchronous IMU gyroscope and accelerometer measurements. The GP models allow for analytical integration and double integration of the latent state to generate accurate preintegrated measurements called unified Gaussian preintegrated measurements (UGPMs). We show through extensive quantitative experiments that the proposed UGPMs outperform the standard preintegration method by an order of magnitude. Additionally, we demonstrate that the UGPMs can be integrated into off-the-shelf multi-modal estimation frameworks with ease based on lidar-inertial, RGBD-inertial, and visual-inertial real-world experiments.

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http://hdl.handle.net/10453/173051