Tightly-coupled model aided visual-inertial fusion for quadrotor micro air vehicles

Abeywardena, D; Dissanayake, G

Tightly-coupled model aided visual-inertial fusion for quadrotor micro air vehicles

Abeywardena, D Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Springer Tracts in Advanced Robotics, 2015, 105 pp. 153 - 166
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Abeywardena, D	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Springer Tracts in Advanced Robotics, 2015, 105 pp. 153 - 166	en_US
dc.identifier.isbn	9783319074870	en_US
dc.identifier.issn	1610-7438	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30531
dc.description.abstract	© Springer International Publishing Switzerland 2015. The main contribution of this paper is a tightly-coupled visual-inertial fusion algorithm for simultaneous localisation and mapping (SLAM) for a quadrotor micro aerial vehicle (MAV). Proposed algorithm is based on an extended Kalman filter that uses a platform specific dynamic model to integrate information from an inertial measurement unit (IMU) and a monocular camera on board the MAV. MAV dynamic model exploits the unique characteristics of the quadrotor, making it possible to generate relatively accurate motion predictions. This, together with an undelayed feature initialisation strategy based on inverse depth parametrisation enables more effective feature tracking and reliable visual SLAM with a small number of features even during rapid manoeuvres. Experimental results are presented to demonstrate the effectiveness of the proposed algorithm.	en_US
dc.relation.ispartof	Springer Tracts in Advanced Robotics	en_US
dc.relation.isbasedon	10.1007/978-3-319-07488-7_11	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Tightly-coupled model aided visual-inertial fusion for quadrotor micro air vehicles	en_US
dc.type	Conference Proceeding
utslib.citation.volume	105	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	closed_access
pubs.publication-status	Published	en_US
pubs.volume	105	en_US

Abstract:

© Springer International Publishing Switzerland 2015. The main contribution of this paper is a tightly-coupled visual-inertial fusion algorithm for simultaneous localisation and mapping (SLAM) for a quadrotor micro aerial vehicle (MAV). Proposed algorithm is based on an extended Kalman filter that uses a platform specific dynamic model to integrate information from an inertial measurement unit (IMU) and a monocular camera on board the MAV. MAV dynamic model exploits the unique characteristics of the quadrotor, making it possible to generate relatively accurate motion predictions. This, together with an undelayed feature initialisation strategy based on inverse depth parametrisation enables more effective feature tracking and reliable visual SLAM with a small number of features even during rapid manoeuvres. Experimental results are presented to demonstrate the effectiveness of the proposed algorithm.

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http://hdl.handle.net/10453/30531