Fast, on-board, model-aided visual-inertial odometry system for quadrotor micro aerial vehicles

Abeywardena, D; Huang, S; Barnes, B; Dissanayake, G; Kodagoda, S

Fast, on-board, model-aided visual-inertial odometry system for quadrotor micro aerial vehicles

Abeywardena, D Huang, S

Barnes, B Dissanayake, G

Kodagoda, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2016, 2016-June pp. 1530 - 1537
Issue Date:: 2016-06-08

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Field	Value	Language
dc.contributor.author	Abeywardena, D	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Barnes, B	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.issued	2016-06-08	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2016, 2016-June pp. 1530 - 1537	en_US
dc.identifier.isbn	9781467380263	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/47145
dc.description.abstract	© 2016 IEEE. The main contribution of this paper is a high frequency, low-complexity, on-board visual-inertial odometry system for quadrotor micro air vehicles. The system consists of an extended Kalman filter (EKF) based state estimation algorithm that fuses information from a low cost MEMS inertial measurement unit acquired at 200Hz and VGA resolution images from a monocular camera at 50Hz. The dynamic model describing the quadrotor motion is employed in the estimation algorithm as a third source of information. Visual information is incorporated into the EKF by enforcing the epipolar constraint on features tracked between image pairs, avoiding the need to explicitly estimate the location of the tracked environmental features. Combined use of the dynamic model and epipolar constraints makes it possible to obtain drift free velocity and attitude estimates in the presence of both accelerometer and gyroscope biases. A strategy to deal with the unobservability that arises when the quadrotor is in hover is also provided. Experimental data from a real-time implementation of the system on a 50 gram embedded computer are presented in addition to the simulations to demonstrate the efficacy of the proposed system.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2016.7487290	en_US
dc.title	Fast, on-board, model-aided visual-inertial odometry system for quadrotor micro aerial vehicles	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-June	en_US
utslib.for	091303 Autonomous Vehicles	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 Electrical and Data Engineering
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
pubs.volume	2016-June	en_US

Abstract:

© 2016 IEEE. The main contribution of this paper is a high frequency, low-complexity, on-board visual-inertial odometry system for quadrotor micro air vehicles. The system consists of an extended Kalman filter (EKF) based state estimation algorithm that fuses information from a low cost MEMS inertial measurement unit acquired at 200Hz and VGA resolution images from a monocular camera at 50Hz. The dynamic model describing the quadrotor motion is employed in the estimation algorithm as a third source of information. Visual information is incorporated into the EKF by enforcing the epipolar constraint on features tracked between image pairs, avoiding the need to explicitly estimate the location of the tracked environmental features. Combined use of the dynamic model and epipolar constraints makes it possible to obtain drift free velocity and attitude estimates in the presence of both accelerometer and gyroscope biases. A strategy to deal with the unobservability that arises when the quadrotor is in hover is also provided. Experimental data from a real-time implementation of the system on a 50 gram embedded computer are presented in addition to the simulations to demonstrate the efficacy of the proposed system.

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http://hdl.handle.net/10453/47145