Ponce, HiramHiramPonceBrieva, JorgeJorgeBrievaMoya-Albor, ErnestoErnestoMoya-Albor2023-07-232023-07-232018https://scripta.up.edu.mx/handle/20.500.12552/426010.1109/IJCNN.2018.8489597Movement detection and characterization of a 3D scene are relevant tasks in vision systems and particularly in robotic applications controlled by visual features. One of the challenges to characterize a 3D scene in navigation systems is the depth estimation. In contrast to classical approaches using visual based stereo systems, we propose a monocular distance estimation system using convolutional neural networks (CNN) and a bio-inspired optical flow approach as part of a neuro-robotic system. We train the CNN using optical flow visual features guided by ultrasonic sensor-based measures in a 3D scenario. The datasets used are available in: http://sites.google.com/up.edu.mx/robotflow/. Experimental results confirm that a monocular camera can be applie for controlling the robot navigation and obstacle avoidance.enResource Types::text::conference output::conference proceedings::conference paper