First Estimate Jacobian EKF for Multi-robot SLAM

Liu, R; Song, Y; Huang, S

First Estimate Jacobian EKF for Multi-robot SLAM

Liu, R Song, Y Huang, S

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Publisher:: ARAA
Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2024, 2024-November, pp. 1-9
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Liu, R
dc.contributor.author	Song, Y
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.date	2024-11-27
dc.date.accessioned	2025-04-02T21:46:23Z
dc.date.available	2025-04-02T21:46:23Z
dc.date.issued	2024-01-01
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2024, 2024-November, pp. 1-9
dc.identifier.issn	1448-2053
dc.identifier.uri	http://hdl.handle.net/10453/186526
dc.description.abstract	Addressing inconsistency issues, i.e. underestimation of uncertainty, is crucial for the performance of Extended Kalman Filter (EKF) based Simultaneous Localization and Mapping (SLAM). By using the first estimate Jacobians, this paper designs a consistent EKF for the point feature-based multi-robot SLAM. First, the standard EKF (Std-EKF) for the considered problems is presented. Then, through the observability analysis, we prove that Std-EKF has an observable subspace of dimension higher than the underlying system, leading to the inconsistency issue. Accordingly, we propose the first estimate Jacobian EKF (FEJ-EKF), which shares the same dimension of observable subspace with the underlying system, alleviating the inconsistency issue. Finally, the effectiveness of the proposed method is validated by simulations and a practical dataset. By making the MATLAB code for this research available online1, we hope to facilitate collaboration and allow others to build upon and improve the methodology.
dc.language	en
dc.publisher	ARAA
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA
dc.relation.ispartof	Australasian Conference on Robotics and Automation
dc.rights	info:eu-repo/semantics/openAccess
dc.title	First Estimate Jacobian EKF for Multi-robot SLAM
dc.type	Conference Proceeding
utslib.citation.volume	2024-November
utslib.location.activity	University of Auckland, Auckland, New Zealand
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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Robotics Institute (RI)
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2025-04-02T21:46:21Z
pubs.finish-date	2024-11-29
pubs.place-of-publication	Australia
pubs.publication-status	Published
pubs.start-date	2024-11-27
pubs.volume	2024-November
dc.location	Australia

Abstract:

Addressing inconsistency issues, i.e. underestimation of uncertainty, is crucial for the performance of Extended Kalman Filter (EKF) based Simultaneous Localization and Mapping (SLAM). By using the first estimate Jacobians, this paper designs a consistent EKF for the point feature-based multi-robot SLAM. First, the standard EKF (Std-EKF) for the considered problems is presented. Then, through the observability analysis, we prove that Std-EKF has an observable subspace of dimension higher than the underlying system, leading to the inconsistency issue. Accordingly, we propose the first estimate Jacobian EKF (FEJ-EKF), which shares the same dimension of observable subspace with the underlying system, alleviating the inconsistency issue. Finally, the effectiveness of the proposed method is validated by simulations and a practical dataset. By making the MATLAB code for this research available online1, we hope to facilitate collaboration and allow others to build upon and improve the methodology.

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http://hdl.handle.net/10453/186526