Distance function based 6DOF localization for unmanned aerial vehicles in GPS denied environments

Unicomb, J; Dantanarayana, L; Arukgoda, J; Ranasinghe, R; Dissanayake, G; Furukawa, T

Distance function based 6DOF localization for unmanned aerial vehicles in GPS denied environments

Unicomb, J Dantanarayana, L

Arukgoda, J Ranasinghe, R

Dissanayake, G

Furukawa, T

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Intelligent Robots and Systems, 2017, 2017-September pp. 5292 - 5297
Issue Date:: 2017-12-13

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Field	Value	Language
dc.contributor.author	Unicomb, J	en_US
dc.contributor.author	Dantanarayana, L https://orcid.org/0000-0002-8887-329X	en_US
dc.contributor.author	Arukgoda, J	en_US
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Furukawa, T	en_US
dc.date.issued	2017-12-13	en_US
dc.identifier.citation	IEEE International Conference on Intelligent Robots and Systems, 2017, 2017-September pp. 5292 - 5297	en_US
dc.identifier.isbn	9781538626825	en_US
dc.identifier.issn	2153-0858	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121566
dc.description.abstract	© 2017 IEEE. This paper presents an algorithm for localizing an unmanned aerial vehicle (UAV) in GPS denied environments. Localization is performed with respect to a pre-built map of the environment represented using the distance function of a binary mosaic, avoiding the need for extraction and explicit matching of visual features. Edges extracted from images acquired by an on-board camera are projected to the map to compute an error metric that indicates the misalignment between the predicted and true pose of the UAV. A constrained extended Kalman filter (EKF) framework is used to generate an estimate of the full 6-DOF location of the UAV by enforcing the condition that the distance function values are zero when there is no misalignment. Use of an EKF also makes it possible to seamlessly incorporate information from any other system on the UAV, for example, from its auto-pilot, a height sensor or an optical flow sensor. Experiments using a hexarotor UAV both in a simulation environment and in the field are presented to demonstrate the effectiveness of the proposed algorithm.	en_US
dc.relation.ispartof	IEEE International Conference on Intelligent Robots and Systems	en_US
dc.relation.isbasedon	10.1109/IROS.2017.8206421	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Distance function based 6DOF localization for unmanned aerial vehicles in GPS denied environments	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-September	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2017-September	en_US

Abstract:

© 2017 IEEE. This paper presents an algorithm for localizing an unmanned aerial vehicle (UAV) in GPS denied environments. Localization is performed with respect to a pre-built map of the environment represented using the distance function of a binary mosaic, avoiding the need for extraction and explicit matching of visual features. Edges extracted from images acquired by an on-board camera are projected to the map to compute an error metric that indicates the misalignment between the predicted and true pose of the UAV. A constrained extended Kalman filter (EKF) framework is used to generate an estimate of the full 6-DOF location of the UAV by enforcing the condition that the distance function values are zero when there is no misalignment. Use of an EKF also makes it possible to seamlessly incorporate information from any other system on the UAV, for example, from its auto-pilot, a height sensor or an optical flow sensor. Experiments using a hexarotor UAV both in a simulation environment and in the field are presented to demonstrate the effectiveness of the proposed algorithm.

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http://hdl.handle.net/10453/121566