Novel insights into the impact of graph structure on SLAM

Khosoussi, K; Huang, S; Dissanayake, G

Novel insights into the impact of graph structure on SLAM

Khosoussi, K

Huang, S

Dissanayake, G

Permalink

Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Intelligent Robots and Systems, 2014, pp. 2707 - 2714
Issue Date:: 2014-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted ManuscriptAdobe PDF (1.56 MB)

View on publisher's site

View statistics

Full metadata record

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	2014-01-01	en_US
dc.identifier.citation	IEEE International Conference on Intelligent Robots and Systems, 2014, pp. 2707 - 2714	en_US
dc.identifier.isbn	9781479969340	en_US
dc.identifier.issn	2153-0858	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33010
dc.description.abstract	© 2014 IEEE. SLAM can be viewed as an estimation problem over graphs. It is well known that the topology of each dataset affects the quality of the corresponding optimal estimate. In this paper we present a formal analysis of the impact of graph structure on the reliability of the maximum likelihood estimator. In particular, we show that the number of spanning trees in the graph is closely related to the D-optimality criterion in experimental design. We also reveal that in a special class of linear-Gaussian estimation problems over graphs, the algebraic connectivity is related to the E-optimality design criterion. Furthermore, we explain how the average node degree of the graph is related to the ratio between the minimum of negative log-likelihood achievable and its value at the ground truth. These novel insights give us a deeper understanding of the SLAM problem. Finally we discuss two important applications of our analysis in active measurement selection and graph pruning. The results obtained from simulations and experiments on real data confirm our theoretical findings.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120102786
dc.relation.ispartof	IEEE International Conference on Intelligent Robots and Systems	en_US
dc.relation.isbasedon	10.1109/IROS.2014.6942932	en_US
dc.title	Novel insights into the impact of graph structure on SLAM	en_US
dc.type	Conference Proceeding
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

Abstract:

© 2014 IEEE. SLAM can be viewed as an estimation problem over graphs. It is well known that the topology of each dataset affects the quality of the corresponding optimal estimate. In this paper we present a formal analysis of the impact of graph structure on the reliability of the maximum likelihood estimator. In particular, we show that the number of spanning trees in the graph is closely related to the D-optimality criterion in experimental design. We also reveal that in a special class of linear-Gaussian estimation problems over graphs, the algebraic connectivity is related to the E-optimality design criterion. Furthermore, we explain how the average node degree of the graph is related to the ratio between the minimum of negative log-likelihood achievable and its value at the ground truth. These novel insights give us a deeper understanding of the SLAM problem. Finally we discuss two important applications of our analysis in active measurement selection and graph pruning. The results obtained from simulations and experiments on real data confirm our theoretical findings.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/33010