Integration of GPS/INS/VISION sensors to navigate unmanned aerial vehicles

Wang, J; Garratt, M; Lambert, A; Wang, JJ; Han, S; Sinclair, D

Integration of GPS/INS/VISION sensors to navigate unmanned aerial vehicles

Wang, J

Garratt, M Lambert, A Wang, JJ Han, S Sinclair, D

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Publication Type:: Conference Proceeding
Citation:: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2008, 37, pp. 963-969
Issue Date:: 2008-01-01

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Field	Value	Language
dc.contributor.author	Wang, J https://orcid.org/0000-0002-6282-0456
dc.contributor.author	Garratt, M
dc.contributor.author	Lambert, A
dc.contributor.author	Wang, JJ
dc.contributor.author	Han, S
dc.contributor.author	Sinclair, D
dc.date.accessioned	2024-01-22T05:16:09Z
dc.date.available	2024-01-22T05:16:09Z
dc.date.issued	2008-01-01
dc.identifier.citation	International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2008, 37, pp. 963-969
dc.identifier.issn	1682-1750
dc.identifier.uri	http://hdl.handle.net/10453/174843
dc.description.abstract	This paper presents an integrated GPS/INS/Vision navigation system for Unmanned Aerial Vehicles (UAVs). A CCD (Charge- Coupled Device) video camera and laser rangefinder (LRF) based vision system, combined with inertial sensors, provides the information on the vertical and horizontal movements of the UAV (helicopter) relative to the ground, which is critical for the safety of UAV operations. Two Kalman filers have been designed to operate separately to provide a reliable check on the navigation solutions. When GPS signals are available, the GPS measurements are used to update the error states in the two Kalman filters, in order to estimate the INS sensors, LRF and optic flow modelling errors, and provide redundant navigation solutions. With the corrected measurements from the vision system, the UAV's relative movements relative to the ground are then estimated continuously, even during GPS signal blockages. The modelling strategies and the data fusion procedure for this sensor integration scenario are discussed with some numerical analysis results, demonstrating the potential performance of the proposed triple integration.
dc.language	en
dc.relation.ispartof	International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0909 Geomatic Engineering
dc.subject.classification	3709 Physical geography and environmental geoscience
dc.subject.classification	4013 Geomatic engineering
dc.title	Integration of GPS/INS/VISION sensors to navigate unmanned aerial vehicles
dc.type	Conference Proceeding
utslib.citation.volume	37
utslib.for	0909 Geomatic Engineering
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 Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-22T05:16:08Z
pubs.publication-status	Published
pubs.volume	37

Abstract:

This paper presents an integrated GPS/INS/Vision navigation system for Unmanned Aerial Vehicles (UAVs). A CCD (Charge- Coupled Device) video camera and laser rangefinder (LRF) based vision system, combined with inertial sensors, provides the information on the vertical and horizontal movements of the UAV (helicopter) relative to the ground, which is critical for the safety of UAV operations. Two Kalman filers have been designed to operate separately to provide a reliable check on the navigation solutions. When GPS signals are available, the GPS measurements are used to update the error states in the two Kalman filters, in order to estimate the INS sensors, LRF and optic flow modelling errors, and provide redundant navigation solutions. With the corrected measurements from the vision system, the UAV's relative movements relative to the ground are then estimated continuously, even during GPS signal blockages. The modelling strategies and the data fusion procedure for this sensor integration scenario are discussed with some numerical analysis results, demonstrating the potential performance of the proposed triple integration.

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http://hdl.handle.net/10453/174843