Invariant EKF based 2D Active SLAM with Exploration Task

Xu, M; Song, Y; Chen, Y; Huang, S; Hao, Q

Invariant EKF based 2D Active SLAM with Exploration Task

Xu, M Song, Y Chen, Y

Huang, S

Hao, Q

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, 2021-May, pp. 5350-5356
Issue Date:: 2021-10-18

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Field	Value	Language
dc.contributor.author	Xu, M
dc.contributor.author	Song, Y
dc.contributor.author	Chen, Y https://orcid.org/0000-0003-2756-7050
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.contributor.author	Hao, Q
dc.date	2021-05-30
dc.date.accessioned	2022-06-10T06:55:09Z
dc.date.available	2022-06-10T06:55:09Z
dc.date.issued	2021-10-18
dc.identifier.citation	2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, 2021-May, pp. 5350-5356
dc.identifier.isbn	9781728190778
dc.identifier.issn	1050-4729
dc.identifier.issn	2577-087X
dc.identifier.uri	http://hdl.handle.net/10453/158071
dc.description.abstract	Right invariant extended Kalman filter (RIEKF) based simultaneous localization and mapping (SLAM) proposed recently has shown to be able to produce more consistent SLAM estimates as compared with traditional EKF based SLAM methods, including some improved EKF SLAM methods such as observability constrained-EKF (OC-EKF) SLAM. Latest results have demonstrated that its performance is very close to optimization based SLAM algorithms such as iSAM. In this paper, we propose to use RIEKF SLAM algorithm in active SLAM where both the predicted SLAM results for choosing control actions and the actual estimated SLAM results applying the selected control actions are computed using RIEKF algorithms. The advantages over traditional EKF based active SLAM are the more accurate and consistent predicted uncertainty estimates which result in robustness of the active SLAM algorithm. The advantages over optimization based active SLAM is the reduced computational cost. Simulation results are presented to validate the advantages of the proposed algorithm3.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE International Conference on Robotics and Automation (ICRA)
dc.relation.ispartof	2021 IEEE International Conference on Robotics and Automation
dc.relation.ispartofseries	IEEE International Conference on Robotics and Automation ICRA
dc.relation.isbasedon	10.1109/icra48506.2021.9561951
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Invariant EKF based 2D Active SLAM with Exploration Task
dc.type	Conference Proceeding
utslib.citation.volume	2021-May
utslib.location.activity	Xi'an, China
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 - CAS - Centre for Autonomous Systems
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	2022-06-10T06:55:05Z
pubs.finish-date	2021-06-05
pubs.publication-status	Published
pubs.start-date	2021-05-30
pubs.volume	2021-May

Abstract:

Right invariant extended Kalman filter (RIEKF) based simultaneous localization and mapping (SLAM) proposed recently has shown to be able to produce more consistent SLAM estimates as compared with traditional EKF based SLAM methods, including some improved EKF SLAM methods such as observability constrained-EKF (OC-EKF) SLAM. Latest results have demonstrated that its performance is very close to optimization based SLAM algorithms such as iSAM. In this paper, we propose to use RIEKF SLAM algorithm in active SLAM where both the predicted SLAM results for choosing control actions and the actual estimated SLAM results applying the selected control actions are computed using RIEKF algorithms. The advantages over traditional EKF based active SLAM are the more accurate and consistent predicted uncertainty estimates which result in robustness of the active SLAM algorithm. The advantages over optimization based active SLAM is the reduced computational cost. Simulation results are presented to validate the advantages of the proposed algorithm3.

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http://hdl.handle.net/10453/158071