An Image Matching System for Autonomous UAV Navigation Based on Neural NetworkShow others and affiliations
2016 (English)In: 14th International Conference on Control, Automation, Robotics and Vision (ICARCV 2016), 2016Conference paper, Published 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 Sciences
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, id: 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).
2016-10-262016-10-262018-07-17Bibliographically approved