Frame-to-Frame ego-motion estimation for agile drones with Convolutional Neural Network
Keywords
Loading...
Issue Date
2020-04-01
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
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
Description
Citation
Faculty
Faculteit der Sociale Wetenschappen