Data association in bearing-only SLAM using a cost function-based approach

Kwok, NM; Ha, QP; Fang, G

Data association in bearing-only SLAM using a cost function-based approach

Kwok, NM Ha, QP

Fang, G

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2007, pp. 4108 - 4113
Issue Date:: 2007-11-27

Closed Access

	Filename	Description	Size
	2006015152.pdf		521.26 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Kwok, NM	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Fang, G	en_US
dc.date.issued	2007-11-27	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2007, pp. 4108 - 4113	en_US
dc.identifier.isbn	1424406021	en_US
dc.identifier.isbn	9781424406029	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2862
dc.description.abstract	When using an extended Kalman filter (EKF) in simultaneous localization and mapping (SLAM) for a mobile robot with bearing-only measurements, it is crucial to correctly assign correspondences between measurements and registered features in the map, otherwise the filter diverges or becomes inconsistent. Conventional methods based on the Mahalanobis distance metric may produce data association ambiguities. Its reliability may further be degraded in bearing-only SLAM due to the limited amount of information delivered from the sensor. The data association process is cast here as that of making a decision based on the sensor measurement as whether to update the EKF or not. For this, cost functions are applied taking into account the interferences from other features. The proposed approach enhances robustness of the data association and consequently assures the performance of bearing-only SLAM. Results from simulations and experiments are included to demonstrate the effectiveness of the method in a typical indoor scenario. © 2007 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ROBOT.2007.364110	en_US
dc.title	Data association in bearing-only SLAM using a cost function-based approach	en_US
dc.type	Conference Proceeding
utslib.for	0910 Manufacturing Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	Roma, Italy	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

When using an extended Kalman filter (EKF) in simultaneous localization and mapping (SLAM) for a mobile robot with bearing-only measurements, it is crucial to correctly assign correspondences between measurements and registered features in the map, otherwise the filter diverges or becomes inconsistent. Conventional methods based on the Mahalanobis distance metric may produce data association ambiguities. Its reliability may further be degraded in bearing-only SLAM due to the limited amount of information delivered from the sensor. The data association process is cast here as that of making a decision based on the sensor measurement as whether to update the EKF or not. For this, cost functions are applied taking into account the interferences from other features. The proposed approach enhances robustness of the data association and consequently assures the performance of bearing-only SLAM. Results from simulations and experiments are included to demonstrate the effectiveness of the method in a typical indoor scenario. © 2007 IEEE.

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

http://hdl.handle.net/10453/2862