Tree-connectivity: Evaluating the graphical structure of SLAM

Khosoussi, K; Huang, S; Dissanayake, G

Tree-connectivity: Evaluating the graphical structure of SLAM

Khosoussi, K

Huang, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2016, 2016-June pp. 1316 - 1322
Issue Date:: 2016-06-08

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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	2016-06-08	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2016, 2016-June pp. 1316 - 1322	en_US
dc.identifier.isbn	9781467380263	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/44398
dc.description.abstract	© 2016 IEEE. Simultaneous localization and mapping (SLAM) in robotics, and a number of related problems that arise in sensor networks are instances of estimation problems over weighted graphs. This paper studies the relation between the graphical representation of such problems and estimationtheoretic concepts such as the Cramér-Rao lower bound (CRLB) and D-optimality. We prove that the weighted number of spanning trees, as a graph connectivity metric, is closely related to the determinant of CRLB. This metric can be efficiently computed for large graphs by exploiting the sparse structure of underlying estimation problems. Our analysis is validated using experiments with publicly available pose-graph SLAM datasets.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120102786
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2016.7487264	en_US
dc.title	Tree-connectivity: Evaluating the graphical structure of SLAM	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-June	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	open_access
pubs.publication-status	Published	en_US
pubs.volume	2016-June	en_US

Abstract:

© 2016 IEEE. Simultaneous localization and mapping (SLAM) in robotics, and a number of related problems that arise in sensor networks are instances of estimation problems over weighted graphs. This paper studies the relation between the graphical representation of such problems and estimationtheoretic concepts such as the Cramér-Rao lower bound (CRLB) and D-optimality. We prove that the weighted number of spanning trees, as a graph connectivity metric, is closely related to the determinant of CRLB. This metric can be efficiently computed for large graphs by exploiting the sparse structure of underlying estimation problems. Our analysis is validated using experiments with publicly available pose-graph SLAM datasets.

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