Abstract
In this paper, a visual system for helping unmanned aerial vehicles navigation, designed with a convolutional neural network, is presented. This network is trained to match on-board captured images with several previously obtained global maps, generating actions given a known global control policy. This system can be used directly for navigation or filtered, combining it with other aircraft systems. Our model will be compared with a classical map registration application, using a Scale-Invariant Feature Transform (SIFT) key point extractor. The system will be trained and evaluated with real aerial images. The results obtained show the viability of the proposed system and demonstrate its performance.
This work has been supported by the Spanish Ministerio de Economia y Competitividad, project TIN2013-40982-R. Project co-financed with FEDER funds.
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Aznar, F., Pujol, M., Rizo, R. (2016). Visual Navigation for UAV with Map References Using ConvNets. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_2
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DOI: https://doi.org/10.1007/978-3-319-44636-3_2
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