A Right Invariant Extended Kalman Filter for Object Based SLAM

Song, Y; Zhang, Z; Wu, J; Wang, Y; Zhao, L; Huang, S

A Right Invariant Extended Kalman Filter for Object Based SLAM

Song, Y

Zhang, Z Wu, J Wang, Y Zhao, L

Huang, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2022, 7, (2), pp. 1316-1323
Issue Date:: 2022-04-01

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Field	Value	Language
dc.contributor.author	Song, Y https://orcid.org/0000-0002-5915-9230
dc.contributor.author	Zhang, Z
dc.contributor.author	Wu, J
dc.contributor.author	Wang, Y
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.date.accessioned	2023-03-09T05:16:12Z
dc.date.available	2023-03-09T05:16:12Z
dc.date.issued	2022-04-01
dc.identifier.citation	IEEE Robotics and Automation Letters, 2022, 7, (2), pp. 1316-1323
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3766
dc.identifier.uri	http://hdl.handle.net/10453/166813
dc.description.abstract	With the recent advance of deep learning based object recognition and estimation, it is possible to consider object level SLAM where the pose of each object is estimated in the SLAM process. In this letter, based on a novel Lie group structure, a right invariant extended Kalman filter (RI-EKF) for object based SLAM is proposed. The observability analysis shows that the proposed algorithm automatically maintains the correct unobservable subspace, while standard EKF (Std-EKF) based SLAM algorithm does not. This results in a better consistency for the proposed algorithm comparing to Std-EKF. Finally, simulations and real world experiments validate not only the consistency and accuracy of the proposed algorithm, but also the practicability of the proposed RI-EKF for object based SLAM problem. The MATLAB code of the algorithm is made publicly available.
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.2021.3139370
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0913 Mechanical Engineering
dc.title	A Right Invariant Extended Kalman Filter for Object Based SLAM
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/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	open_access	*
dc.date.updated	2023-03-09T05:16:10Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	2

Abstract:

With the recent advance of deep learning based object recognition and estimation, it is possible to consider object level SLAM where the pose of each object is estimated in the SLAM process. In this letter, based on a novel Lie group structure, a right invariant extended Kalman filter (RI-EKF) for object based SLAM is proposed. The observability analysis shows that the proposed algorithm automatically maintains the correct unobservable subspace, while standard EKF (Std-EKF) based SLAM algorithm does not. This results in a better consistency for the proposed algorithm comparing to Std-EKF. Finally, simulations and real world experiments validate not only the consistency and accuracy of the proposed algorithm, but also the practicability of the proposed RI-EKF for object based SLAM problem. The MATLAB code of the algorithm is made publicly available.

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http://hdl.handle.net/10453/166813