A Deep Neural Network Sensor for Visual Servoing in 3D Spaces

Sensors
Petar Durdevic, Daniel Ortiz-Arroyo

Abstract

This paper describes a novel stereo vision sensor based on deep neural networks, that can be used to produce a feedback signal for visual servoing in unmanned aerial vehicles such as drones. Two deep convolutional neural networks attached to the stereo camera in the drone are trained to detect wind turbines in images and stereo triangulation is used to calculate the distance from a wind turbine to the drone. Our experimental results show that the sensor produces data accurate enough to be used for servoing, even in the presence of noise generated when the drone is not being completely stable. Our results also show that appropriate filtering of the signals is needed and that to produce correct results, it is very important to keep the wind turbine within the field of vision of both cameras, so that both deep neural networks could detect it.

References

Jul 12, 2014·IEEE Transactions on Neural Networks and Learning Systems·Ling ShaoXuelong Li

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Citations

Mar 30, 2021·Trends in Cognitive Sciences·Jacqueline C Snow, Jody C Culham

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MobileNet
TinyYolo
WindMillNet
Yolov2
CNN
R
YOLO
Quanser
Alexnet
Yolov3

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