Incremental SQP method for constrained optimization formulation in SLAM

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

Incremental SQP method for constrained optimization formulation in SLAM

Bai, F

Huang, S

Vidal-Calleja, T

Zhang, Q

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Publication Type:: Conference Proceeding
Citation:: 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2016
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Bai, F https://orcid.org/0000-0001-9606-3943	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Vidal-Calleja, T https://orcid.org/0000-0002-5763-9644	en_US
dc.contributor.author	Zhang, Q	en_US
dc.date.available	2016-08-01	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2016	en_US
dc.identifier.isbn	9781509035496	en_US
dc.identifier.uri	http://hdl.handle.net/10453/78705
dc.description.abstract	© 2016 IEEE. The simultaneous localization and mapping (SLAM) problem has been a research focus for many years and have reached a mature state. However, more robust solutions to the SLAM problem are still required, especially in large noise level scenarios. Because of the strong non-linearity of the SLAM problem, it is vital to start from a good initial value to avoid being trapped in local minima. In this paper, we propose a new SLAM formulation transforming the unconstrained Least Squares formulation into a constrained optimization problem. Algorithms based on this new formulation can naturally start from good initial value. Different from other constrained optimization problem, this new formulation can be efficiently solved with Sequential Quadratic Programming (SQP) methods. Based on SQP, we propose an incremental SQP algorithm to solve SLAM, which shows great advantage over Gauss Newton (g2o implementation) when working in large noise level scenarios. Experimental results show the validity of the proposed approach.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120102786
dc.relation.ispartof	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016	en_US
dc.relation.isbasedon	10.1109/ICARCV.2016.7838759	en_US
dc.title	Incremental SQP method for constrained optimization formulation in SLAM	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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.publication-status	Published	en_US

Abstract:

© 2016 IEEE. The simultaneous localization and mapping (SLAM) problem has been a research focus for many years and have reached a mature state. However, more robust solutions to the SLAM problem are still required, especially in large noise level scenarios. Because of the strong non-linearity of the SLAM problem, it is vital to start from a good initial value to avoid being trapped in local minima. In this paper, we propose a new SLAM formulation transforming the unconstrained Least Squares formulation into a constrained optimization problem. Algorithms based on this new formulation can naturally start from good initial value. Different from other constrained optimization problem, this new formulation can be efficiently solved with Sequential Quadratic Programming (SQP) methods. Based on SQP, we propose an incremental SQP algorithm to solve SLAM, which shows great advantage over Gauss Newton (g2o implementation) when working in large noise level scenarios. Experimental results show the validity of the proposed approach.

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http://hdl.handle.net/10453/78705