Abstract | ||
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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. |
Year | Venue | Field |
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2016 | 2016 14TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV) | Template matching,Canny edge detector,Computer vision,Feature detection (computer vision),Computer science,Edge detection,Sobel operator,Global Positioning System,GNSS applications,Artificial intelligence,Artificial neural network |
DocType | ISSN | Citations |
Conference | 2474-2953 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jose R. G. Braga | 1 | 0 | 0.34 |
Haroldo F. De Campos Velho | 2 | 12 | 5.42 |
Gianpaolo Conte | 3 | 103 | 7.28 |
Patrick Doherty | 4 | 15 | 6.97 |
E. H. Shiguemori | 5 | 2 | 1.87 |