Robust Incremental SLAM under Constrained Optimization Formulation

Bai, F; Vidal-Calleja, T; Huang, S

Robust Incremental SLAM under Constrained Optimization Formulation

Bai, F

Vidal-Calleja, T

Huang, S

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Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2018, 3 (2), pp. 1207 - 1214
Issue Date:: 2018-04-01

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Field	Value	Language
dc.contributor.author	Bai, F https://orcid.org/0000-0001-9606-3943	en_US
dc.contributor.author	Vidal-Calleja, T https://orcid.org/0000-0002-5763-9644	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.date.available	2020-05-25T19:16:00Z
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	IEEE Robotics and Automation Letters, 2018, 3 (2), pp. 1207 - 1214	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125853
dc.description.abstract	© 2016 IEEE. In this letter, we propose a constrained optimization formulation and a robust incremental framework for the simultaneous localization and mapping problem (SLAM). The new SLAM formulation is derived from the nonlinear least squares (NLS) formulation by mathematically formulating loop-closure cycles as constraints. Under the constrained SLAM formulation, we study the robustness of an incremental SLAM algorithm against local minima and outliers as a constraint/loop-closure cycle selection problem. We find a constraint metric that can predict the objective function growth after including the constraint. By the virtue of the constraint metric, we select constraints into the incremental SLAM according to a least objective function growth principle to increase robustness against local minima and perform χ 2 difference test on the constraint metric to increase robustness against outliers. Finally, using sequential quadratic programming (SQP) as the solver, an incremental SLAM algorithm (iSQP) is proposed. Experimental validations are provided to illustrate the accuracy of the constraint metric and the robustness of the proposed incremental SLAM algorithm. Nonetheless, the proposed approach is currently confined to datasets with sparse loop-closures due to its computational cost.	en_US
dc.relation.ispartof	IEEE Robotics and Automation Letters	en_US
dc.relation.isbasedon	10.1109/LRA.2018.2794610	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Robust Incremental SLAM under Constrained Optimization Formulation	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	3	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	open_access	*
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

© 2016 IEEE. In this letter, we propose a constrained optimization formulation and a robust incremental framework for the simultaneous localization and mapping problem (SLAM). The new SLAM formulation is derived from the nonlinear least squares (NLS) formulation by mathematically formulating loop-closure cycles as constraints. Under the constrained SLAM formulation, we study the robustness of an incremental SLAM algorithm against local minima and outliers as a constraint/loop-closure cycle selection problem. We find a constraint metric that can predict the objective function growth after including the constraint. By the virtue of the constraint metric, we select constraints into the incremental SLAM according to a least objective function growth principle to increase robustness against local minima and perform χ 2 difference test on the constraint metric to increase robustness against outliers. Finally, using sequential quadratic programming (SQP) as the solver, an incremental SLAM algorithm (iSQP) is proposed. Experimental validations are provided to illustrate the accuracy of the constraint metric and the robustness of the proposed incremental SLAM algorithm. Nonetheless, the proposed approach is currently confined to datasets with sparse loop-closures due to its computational cost.

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http://hdl.handle.net/10453/125853