Comparison of EKF based SLAM and optimization based SLAM algorithms

Zhang, Y; Zhang, T; Huang, S

Comparison of EKF based SLAM and optimization based SLAM algorithms

Zhang, Y Zhang, T Huang, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018, 2018, pp. 1308 - 1313
Issue Date:: 2018-06-26

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Field	Value	Language
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Zhang, T	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.date.available	2020-06-02T19:13:20Z
dc.date.issued	2018-06-26	en_US
dc.identifier.citation	Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018, 2018, pp. 1308 - 1313	en_US
dc.identifier.isbn	9781538637579	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125778
dc.description.abstract	© 2018 IEEE. This paper compares the recent developed state-of-the-art extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) algorithm, namely, invariant EKF SLAM, with the nonlinear least squares optimization based SLAM algorithms. Simulations in 1D, 2D, and 3D are used to evaluate the invariant EKF SLAM algorithm. It is demonstrated that in most 2D/3D scenarios with practical noise levels, the accuracy of invariant EKF is very close to that of nonlinear least squares optimization based SLAM. In the simple 1D case, the Kalman filter results and the linear least squares results are exactly the same (for any noise levels) due to the linear motion model and linear observation model involved.	en_US
dc.relation.ispartof	Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018	en_US
dc.relation.isbasedon	10.1109/ICIEA.2018.8397911	en_US
dc.title	Comparison of EKF based SLAM and optimization based SLAM algorithms	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
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/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. This paper compares the recent developed state-of-the-art extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) algorithm, namely, invariant EKF SLAM, with the nonlinear least squares optimization based SLAM algorithms. Simulations in 1D, 2D, and 3D are used to evaluate the invariant EKF SLAM algorithm. It is demonstrated that in most 2D/3D scenarios with practical noise levels, the accuracy of invariant EKF is very close to that of nonlinear least squares optimization based SLAM. In the simple 1D case, the Kalman filter results and the linear least squares results are exactly the same (for any noise levels) due to the linear motion model and linear observation model involved.

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http://hdl.handle.net/10453/125778