A sparse separable SLAM back-end

Khosoussi, K; Huang, S; Dissanayake, G

A sparse separable SLAM back-end

Khosoussi, K

Huang, S

Dissanayake, G

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Robotics, 2016, 32 (6), pp. 1536 - 1549
Issue Date:: 2016-12-01

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Field	Value	Language
dc.contributor.author	Khosoussi, K https://orcid.org/0000-0002-9969-1176	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	2016-12-01	en_US
dc.identifier.citation	IEEE Transactions on Robotics, 2016, 32 (6), pp. 1536 - 1549	en_US
dc.identifier.issn	1552-3098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121817
dc.description.abstract	© 2004-2012 IEEE. We propose a scalable algorithm to take advantage of the separable structure of simultaneous localization and mapping (SLAM). Separability is an overlooked structure of SLAM that distinguishes it from a generic nonlinear least-squares problem. The standard relative-pose and relative-position measurement models in SLAM are affine with respect to robot and features' positions. Therefore, given an estimate for robot orientation, the conditionally optimal estimate for the rest of the state variables can be easily computed by solving a sparse linear least-squares problem. We propose an algorithm to exploit this intrinsic property of SLAM by stripping the problem down to its nonlinear core, while maintaining its natural sparsity. Our algorithm can be used in conjunction with any Newton-based solver and is applicable to 2-D/3-D pose-graph and feature-based SLAM. Our results suggest that iteratively solving the nonlinear core of SLAM leads to a fast and reliable convergence as compared to the state-of-the-art sparse back-ends.	en_US
dc.relation.ispartof	IEEE Transactions on Robotics	en_US
dc.relation.isbasedon	10.1109/TRO.2016.2609394	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	A sparse separable SLAM back-end	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	32	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
utslib.copyright.status	closed_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

© 2004-2012 IEEE. We propose a scalable algorithm to take advantage of the separable structure of simultaneous localization and mapping (SLAM). Separability is an overlooked structure of SLAM that distinguishes it from a generic nonlinear least-squares problem. The standard relative-pose and relative-position measurement models in SLAM are affine with respect to robot and features' positions. Therefore, given an estimate for robot orientation, the conditionally optimal estimate for the rest of the state variables can be easily computed by solving a sparse linear least-squares problem. We propose an algorithm to exploit this intrinsic property of SLAM by stripping the problem down to its nonlinear core, while maintaining its natural sparsity. Our algorithm can be used in conjunction with any Newton-based solver and is applicable to 2-D/3-D pose-graph and feature-based SLAM. Our results suggest that iteratively solving the nonlinear core of SLAM leads to a fast and reliable convergence as compared to the state-of-the-art sparse back-ends.

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http://hdl.handle.net/10453/121817