Extending the limits of feature-based SLAM with B-splines

Pedraza, L; Rodriguez-Losada, D; Matía, F; Dissanayake, G; Miró, JV

Extending the limits of feature-based SLAM with B-splines

Pedraza, L Rodriguez-Losada, D Matía, F Dissanayake, G

Miró, JV

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Robotics, 2009, 25 (2), pp. 353 - 366
Issue Date:: 2009-02-24

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Field	Value	Language
dc.contributor.author	Pedraza, L	en_US
dc.contributor.author	Rodriguez-Losada, D	en_US
dc.contributor.author	Matía, F	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Miró, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.date.issued	2009-02-24	en_US
dc.identifier.citation	IEEE Transactions on Robotics, 2009, 25 (2), pp. 353 - 366	en_US
dc.identifier.issn	1552-3098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9160
dc.description.abstract	This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments. © 2009 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Robotics	en_US
dc.relation.isbasedon	10.1109/TRO.2009.2013496	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Extending the limits of feature-based SLAM with B-splines	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	25	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic 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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments. © 2009 IEEE.

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http://hdl.handle.net/10453/9160