A convex optimization based approach for pose SLAM problems

Liu, M; Huang, S; Dissanayake, G; Wang, H

A convex optimization based approach for pose SLAM problems

Liu, M Huang, S

Dissanayake, G

Wang, H

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Intelligent Robots and Systems, 2012, pp. 1898 - 1903
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Liu, M	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.contributor.author	Wang, H	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	IEEE International Conference on Intelligent Robots and Systems, 2012, pp. 1898 - 1903	en_US
dc.identifier.isbn	9781467317375	en_US
dc.identifier.issn	2153-0858	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32348
dc.description.abstract	This paper demonstrates that 2D pose SLAM has an underlining near convex structure when formulated as a least squares (LS) optimization problem. By introducing new variables and some approximations, the LS pose SLAM problem can be formulated as a quadratically constrained quadratic programming (QCQP) problem. The QCQP formulation can then be relaxed into a semi-definite programming (SDP) problem which is convex. Unique solution to the convex SDP problem can be obtained without initial state estimate and can be used to construct a candidate solution to the original LS pose SLAM problem. Simulation datasets and the Intel Research Lab dataset have been used to demonstrate that when the relative pose information contain noises with reasonable level, the candidate solution obtained through the relaxation is very close to the optimal solution to the LS SLAM problem. Thus in practice, the candidate solution can serve as either an approximate solution or a good initial guess for a local optimization algorithm to obtain the optimal solution to the LS pose SLAM problem. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Intelligent Robots and Systems	en_US
dc.relation.isbasedon	10.1109/IROS.2012.6385742	en_US
dc.title	A convex optimization based approach for pose SLAM problems	en_US
dc.type	Conference Proceeding
utslib.for	1099 Other Technology	en_US
dc.location.activity	Algarve, PORTUGAL
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	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper demonstrates that 2D pose SLAM has an underlining near convex structure when formulated as a least squares (LS) optimization problem. By introducing new variables and some approximations, the LS pose SLAM problem can be formulated as a quadratically constrained quadratic programming (QCQP) problem. The QCQP formulation can then be relaxed into a semi-definite programming (SDP) problem which is convex. Unique solution to the convex SDP problem can be obtained without initial state estimate and can be used to construct a candidate solution to the original LS pose SLAM problem. Simulation datasets and the Intel Research Lab dataset have been used to demonstrate that when the relative pose information contain noises with reasonable level, the candidate solution obtained through the relaxation is very close to the optimal solution to the LS SLAM problem. Thus in practice, the candidate solution can serve as either an approximate solution or a good initial guess for a local optimization algorithm to obtain the optimal solution to the LS pose SLAM problem. © 2012 IEEE.

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http://hdl.handle.net/10453/32348