Evaluation of feature detectors for KLT based feature tracking using the odroid U3

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

Evaluation of feature detectors for KLT based feature tracking using the odroid U3

Barnes, B Abeywardena, D Kodagoda, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2014, 02-04-December-2014
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Barnes, B	en_US
dc.contributor.author	Abeywardena, D	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2014, 02-04-December-2014	en_US
dc.identifier.isbn	9780980740448	en_US
dc.identifier.issn	1448-2053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30530
dc.description.abstract	Feature tracking is an integral part of most vision-based state estimation frameworks. However, tracking features at a sufficient frame rate is a challenging task for mobile robots such as Micro Aerial Vehicles (MAVs) due to their fast dynamics and limited on-board computing resources. Recent developments in smartphone processors have led to embedded computing platforms that are ideal on-board computers for MAV state estimation. This paper analyses the performance of a Kanade-Lucas-Thomasi (KLT) based feature tracker on a state-of-theart embedded computing platform suitable for on-board MAV state estimation. It compares the performance of different implementations of the feature tracker using four different lowcomplexity feature detectors. The experimental results presented herein may serve as guidelines for the selection of a feature detector, image resolution, framerate and feature quantity when developing on-board feature tracking systems based on ARM Cortex-A9 embedded computers.	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA	en_US
dc.title	Evaluation of feature detectors for KLT based feature tracking using the odroid U3	en_US
dc.type	Conference Proceeding
utslib.citation.volume	02-04-December-2014	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Melbourne
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	02-04-December-2014	en_US

Abstract:

Feature tracking is an integral part of most vision-based state estimation frameworks. However, tracking features at a sufficient frame rate is a challenging task for mobile robots such as Micro Aerial Vehicles (MAVs) due to their fast dynamics and limited on-board computing resources. Recent developments in smartphone processors have led to embedded computing platforms that are ideal on-board computers for MAV state estimation. This paper analyses the performance of a Kanade-Lucas-Thomasi (KLT) based feature tracker on a state-of-theart embedded computing platform suitable for on-board MAV state estimation. It compares the performance of different implementations of the feature tracker using four different lowcomplexity feature detectors. The experimental results presented herein may serve as guidelines for the selection of a feature detector, image resolution, framerate and feature quantity when developing on-board feature tracking systems based on ARM Cortex-A9 embedded computers.

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http://hdl.handle.net/10453/30530