Exploiting the separable structure of SLAM

Khosoussi, K; Huang, S; Dissanyake, G

Exploiting the separable structure of SLAM

Khosoussi, K

Huang, S

Dissanyake, G

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Publication Type:: Conference Proceeding
Citation:: Robotics: Science and Systems, 2015, 11
Issue Date:: 2015-01-01

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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	Dissanyake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.available	2015-04-29	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Robotics: Science and Systems, 2015, 11	en_US
dc.identifier.isbn	9780992374716	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36699
dc.description.abstract	© 2015, MIT Press Journals. All rights reserved. In this paper we point out an overlooked structure of SLAM that distinguishes it from a generic nonlinear least squares problem. The measurement function in most common forms of SLAM is linear with respect to robot and features' positions. Therefore, given an estimate for robot orientation, the conditionally optimal estimate for the rest of state variables can be easily obtained by solving a sparse linear-Gaussian estimation 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 together with any Newton-based iterative solver and is applicable to 2D/3D pose-graph and feature-based problems. 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 back-ends.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120102786
dc.relation.ispartof	Robotics: Science and Systems	en_US
dc.relation.isbasedon	10.15607/RSS.2015.XI.023	en_US
dc.title	Exploiting the separable structure of SLAM	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Rome, Italy
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	open_access
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

© 2015, MIT Press Journals. All rights reserved. In this paper we point out an overlooked structure of SLAM that distinguishes it from a generic nonlinear least squares problem. The measurement function in most common forms of SLAM is linear with respect to robot and features' positions. Therefore, given an estimate for robot orientation, the conditionally optimal estimate for the rest of state variables can be easily obtained by solving a sparse linear-Gaussian estimation 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 together with any Newton-based iterative solver and is applicable to 2D/3D pose-graph and feature-based problems. 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 back-ends.

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http://hdl.handle.net/10453/36699