Abstract
This research has the purpose of allowing anyone, with or without experience handling micro aerial vehicles, to operate unmanned aerial vehicles (UAV) in a natural and intuitive way, unlike typical interfaces that need experience and knowledge in piloting to be used. To achieve this, our approach uses gesture recognition, based on machine learning with Support Vector Machine (SVM) for classification and a RGB-D sensor for the feature extraction. Tests for recognition with different Kernel-SVM and for the RGB-D sensor with different levels of light were carried out.
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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., Cobeña, B., Rodriguez, G., Salcedo, V.S., Collaguazo, B. (2018). SVM and RGB-D Sensor Based Gesture Recognition for UAV Control. In: De Paolis, L., Bourdot, P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2018. Lecture Notes in Computer Science(), vol 10851. Springer, Cham. https://doi.org/10.1007/978-3-319-95282-6_50
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DOI: https://doi.org/10.1007/978-3-319-95282-6_50
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