Abstract
In this work, we present the development of a system for the detection and depth estimation of objects in real time using the on-board camera in a micro-UAV through convolutional neuronal networks. Traditionally for the detection of obstacles shows the use of SLAM visual systems. However, to solve this problem, this level of complexity is not necessary, saving resources and execution time. The training with convolutional neural networks using stereo images for the depth estimation and in the same way training the detection of common observable objects can obtain an accurate detection of obstacles in a real time.
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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., Rodríguez, G.A., Alvarez, L.G., Limaico, A., Sandoval, D.S. (2018). Convolutional Neuronal Networks Based Monocular Object Detection and Depth Perception for Micro UAVs. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_35
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