Convergence and Consistency Analysis for a 3-D Invariant-EKF SLAM

Zhang, T; Wu, K; Song, J; Huang, S; Dissanayake, G

Convergence and Consistency Analysis for a 3-D Invariant-EKF SLAM

Zhang, T

Wu, K Song, J Huang, S

Dissanayake, G

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Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2017, 2 (2), pp. 733 - 740
Issue Date:: 2017-04-01

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Field	Value	Language
dc.contributor.author	Zhang, T https://orcid.org/0000-0001-9321-9332	en_US
dc.contributor.author	Wu, K	en_US
dc.contributor.author	Song, J	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2017-04-01	en_US
dc.identifier.citation	IEEE Robotics and Automation Letters, 2017, 2 (2), pp. 733 - 740	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118117
dc.description.abstract	© 2016 IEEE. In this letter, we investigate the convergence and consistency properties of an invariant-extended Kalman filter (RI-EKF) based simultaneous localization and mapping (SLAM) algorithm. Basic convergence properties of this algorithm are proven. These proofs do not require the restrictive assumption that the Jacobians of the motion and observation models need to be evaluated at the ground truth. It is also shown that the output of RI-EKF is invariant under any stochastic rigid body transformation in contrast to SO(3) based EKF SLAM algorithm (SO(3)-EKF) that is only invariant under deterministic rigid body transformation. Implications of these invariance properties on the consistency of the estimator are also discussed. Monte Carlo simulation results demonstrate that RI-EKF outperforms SO(3)-EKF, Robocentric-EKF and the 'First Estimates Jacobian' EKF, for three-dimensional point feature-based SLAM.	en_US
dc.relation.ispartof	IEEE Robotics and Automation Letters	en_US
dc.relation.isbasedon	10.1109/LRA.2017.2651376	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Convergence and Consistency Analysis for a 3-D Invariant-EKF SLAM	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.for	0913 Mechanical Engineering	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

© 2016 IEEE. In this letter, we investigate the convergence and consistency properties of an invariant-extended Kalman filter (RI-EKF) based simultaneous localization and mapping (SLAM) algorithm. Basic convergence properties of this algorithm are proven. These proofs do not require the restrictive assumption that the Jacobians of the motion and observation models need to be evaluated at the ground truth. It is also shown that the output of RI-EKF is invariant under any stochastic rigid body transformation in contrast to SO(3) based EKF SLAM algorithm (SO(3)-EKF) that is only invariant under deterministic rigid body transformation. Implications of these invariance properties on the consistency of the estimator are also discussed. Monte Carlo simulation results demonstrate that RI-EKF outperforms SO(3)-EKF, Robocentric-EKF and the 'First Estimates Jacobian' EKF, for three-dimensional point feature-based SLAM.

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http://hdl.handle.net/10453/118117