Abstract
In this article, we present the use of depth estimation in real time using the on-board camera in a micro-UAV through convolutional neuronal networks. The experiments and results of the implementation of the system in a micro-UAV are presented to verify the unsupervised model improvement with monocular cameras and the error regarding real model.
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Acknowledgement
This work is part of the project Perception and localization system for autonomous navigation of rotor micro aerial vehicle in gps-denied environments, VisualNavDrone, 2016-PIC-024, from the Universidad de las Fuerzas Armadas ESPE, directed by Dr. Wilbert G. Aguilar.
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Aguilar, W.G., Quisaguano, F.J., Alvarez, L.G., Pardo, J.A., Proaño, Z. (2018). Monocular Depth Perception on a Micro-UAV Using Convolutional Neuronal Networks. In: Boudriga, N., Alouini, MS., Rekhis, S., Sabir, E., Pollin, S. (eds) Ubiquitous Networking. UNet 2018. Lecture Notes in Computer Science(), vol 11277. Springer, Cham. https://doi.org/10.1007/978-3-030-02849-7_35
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