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Convolutional neuronal networks based monocular object detection and depth perception for micro UAVs

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.