Frame-to-Frame ego-motion estimation for agile drones with Convolutional Neural Network
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Issue Date
2020-04-01
Language
en
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Abstract
Openly available feature based Visual Odometery algorithms, have a high computational
cost. This hinder the use of these algorithms in agile robotics platforms such as racing
drones. A wide range of researchers have looked into, using data-driven learning methods
such as Deep Learning, instead use of traditional feature based methods for developing
a new class of visual odometery systems. These systems have mainly relied on datasets
designed for low degree-of-freedom systems such as cars. Hence, even this new class
of algorithms, su er from high computational cost and inability to handle agile robotics
sytems. Therefore, this research will look into developing a learning based visual odometry
system, speci cally designed for racing drones, which can handle high speed agile motion
with low computational cost.
Keywords| Drone, Neural Networks, Visual-odometry, Ego-motion
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Faculteit der Sociale Wetenschappen