Toward Consistent and Efficient Map-Based Visual-Inertial Localization: Theory Framework and Filter Design

Zhang, Z; Song, Y; Huang, S; Xiong, R; Wang, Y

Toward Consistent and Efficient Map-Based Visual-Inertial Localization: Theory Framework and Filter Design

Zhang, Z Song, Y

Huang, S

Xiong, R Wang, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Robotics, 2023, 39, (4), pp. 2892-2911
Issue Date:: 2023-08-01

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Field	Value	Language
dc.contributor.author	Zhang, Z
dc.contributor.author	Song, Y https://orcid.org/0000-0002-5915-9230
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.contributor.author	Xiong, R
dc.contributor.author	Wang, Y
dc.date.accessioned	2023-11-06T06:11:42Z
dc.date.available	2023-11-06T06:11:42Z
dc.date.issued	2023-08-01
dc.identifier.citation	IEEE Transactions on Robotics, 2023, 39, (4), pp. 2892-2911
dc.identifier.issn	1552-3098
dc.identifier.issn	1941-0468
dc.identifier.uri	http://hdl.handle.net/10453/173065
dc.description.abstract	This article focuses on designing a consistent and efficient filter for visual-inertial localization given a prebuilt map. First, we propose a new Lie group with its algebra based on which a novel invariant extended Kalman filter (invariant EKF) is designed. We theoretically prove that, when we do not consider the uncertainty of map information, the proposed invariant EKF is able to naturally preserve the correct observability properties of the system. To consider the uncertainty of map information, we introduce a Schmidt filter. With the Schmidt filter, the uncertainty of map information can be taken into consideration to avoid overconfident estimation while the computation cost only increases linearly with the size of the map keyframes. In addition, we introduce an easily implemented observability-constrained technique because directly combining the invariant EKF with the Schmidt filter cannot maintain the correct observability properties of the system that considers the uncertainty of map information. Finally, we validate our proposed system's high consistency, accuracy, and efficiency via extensive simulations and real-world experiments.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Robotics
dc.relation.isbasedon	10.1109/TRO.2023.3272847
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.title	Toward Consistent and Efficient Map-Based Visual-Inertial Localization: Theory Framework and Filter Design
dc.type	Journal Article
utslib.citation.volume	39
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	2023-11-06T06:11:41Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	39
utslib.citation.issue	4

Abstract:

This article focuses on designing a consistent and efficient filter for visual-inertial localization given a prebuilt map. First, we propose a new Lie group with its algebra based on which a novel invariant extended Kalman filter (invariant EKF) is designed. We theoretically prove that, when we do not consider the uncertainty of map information, the proposed invariant EKF is able to naturally preserve the correct observability properties of the system. To consider the uncertainty of map information, we introduce a Schmidt filter. With the Schmidt filter, the uncertainty of map information can be taken into consideration to avoid overconfident estimation while the computation cost only increases linearly with the size of the map keyframes. In addition, we introduce an easily implemented observability-constrained technique because directly combining the invariant EKF with the Schmidt filter cannot maintain the correct observability properties of the system that considers the uncertainty of map information. Finally, we validate our proposed system's high consistency, accuracy, and efficiency via extensive simulations and real-world experiments.

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http://hdl.handle.net/10453/173065