Kullback-leibler divergence based graph pruning in robotic feature mapping

Wang, Y; Xiong, R; Li, Q; Huang, S

Kullback-leibler divergence based graph pruning in robotic feature mapping

Wang, Y Xiong, R Li, Q

Huang, S

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Publication Type:: Conference Proceeding
Citation:: 2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings, 2013, pp. 32 - 37
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Xiong, R	en_US
dc.contributor.author	Li, Q https://orcid.org/0000-0003-0370-7100	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings, 2013, pp. 32 - 37	en_US
dc.identifier.isbn	9781479902637	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27596
dc.description.abstract	In pose feature graph simultaneous localization and mapping, the robot poses and feature positions are treated as graph nodes and the odometry and observations are treated as edges. The size of the graph exerts an important influence on the efficiency of the graph optimization. Conventionally, the size of the graph is kept small by discarding the current frame if it is not spatially far enough from the previous one or not informative enough. However, these approaches cannot discard the already preserved frames when the robot re-visits the previously explored area. We propose a measure derived from Kullbach-Leibler divergence to decide whether a frame should be discarded, achieving an online implementation of the graph pruning algorithm for feature mapping, of which the pruned frame can be any of the preserved frames. The experimental results using real world datasets show that the proposed pruning algorithm can effectively reduce the size of the graph while maintaining the map accuracy. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 European Conference on Mobile Robots, ECMR 2013 - Conference Proceedings	en_US
dc.relation.isbasedon	10.1109/ECMR.2013.6698816	en_US
dc.title	Kullback-leibler divergence based graph pruning in robotic feature mapping	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	Barcelona, Spain	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.publication-status	Published	en_US

Abstract:

In pose feature graph simultaneous localization and mapping, the robot poses and feature positions are treated as graph nodes and the odometry and observations are treated as edges. The size of the graph exerts an important influence on the efficiency of the graph optimization. Conventionally, the size of the graph is kept small by discarding the current frame if it is not spatially far enough from the previous one or not informative enough. However, these approaches cannot discard the already preserved frames when the robot re-visits the previously explored area. We propose a measure derived from Kullbach-Leibler divergence to decide whether a frame should be discarded, achieving an online implementation of the graph pruning algorithm for feature mapping, of which the pruned frame can be any of the preserved frames. The experimental results using real world datasets show that the proposed pruning algorithm can effectively reduce the size of the graph while maintaining the map accuracy. © 2013 IEEE.

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http://hdl.handle.net/10453/27596