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An Image Matching System for Autonomous UAV Navigation Based on Neural Network
National Institute for Space Research, Sao Jose dos Campos, SP, Brazil.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (UASTECH – Teknologier för autonoma obemannade flygande farkoster, UASTECH - Autonomous Unmanned Aircraft Systems Technologies *(2012-05-31))
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (UASTECH – Teknologier för autonoma obemannade flygande farkoster, UASTECH - Autonomous Unmanned Aircraft Systems Technologies *(2012-05-31))
National Institute for Space Research, Sao Jose dos Campos, SP, Brazil.
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2016 (English)In: 14th International Conference on Control, Automation, Robotics and Vision (ICARCV 2016), 2016Conference paper, (Refereed)
Abstract [en]

This paper proposes an image matching system using aerial images, captured in flight time, and aerial geo-referenced images to estimate the Unmanned Aerial Vehicle (UAV) position in a situation of Global Navigation Satellite System (GNSS) failure. The image matching system is based on edge detection in the aerial and geo-referenced image and posterior automatic image registration of these edge-images (position estimation of UAV). The edge detection process is performed by an Artificial Neural Network (ANN), with an optimal architecture. A comparison with Sobel and Canny edge extraction filters is also provided. The automatic image registration is obtained by a cross-correlation process. The ANN optimal architecture is set by the Multiple Particle Collision Algorithm (MPCA). The image matching system was implemented in a low cost/consumption portable computer. The image matching system has been tested on real flight-test data and encouraging results have been obtained. Results using real flight-test data will be presented.

Place, publisher, year, edition, pages
2016.
Series
International Conference on Control Automation Robotics and Vision, ISSN 2474-2953
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-132282DOI: 10.1109/ICARCV.2016.7838775ISI: 000405520900176ISBN: 978-1-5090-3549-6 (electronic)ISBN: 978-1-5090-3550-2 (print)OAI: oai:DiVA.org:liu-132282DiVA: diva2:1040071
Conference
14th International Conference on Control, Automation, Robotics and Vision (ICARCV 2016), 12-15 November 2016, Phuket, Thailand.
Projects
CADICSELLIITCUASSymbiCloud
Note

Funding agencies:This work was carried out with support from CNPq - National Counsel of Technological and Scientific Development - Brazil. This work is partially supported by the Swedish Research Council (VR) Linnaeus Center CADICS, ELLIIT, and the Swedish Foundation for Strategic Research (CUAS Project, SymbiKCloud Project).

Available from: 2016-10-26 Created: 2016-10-26 Last updated: 2017-08-09Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
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Output format
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