UAV object-based semi-autonomous and autonomous navigation

Thesis type
(Thesis) M.Sc.
Date created
2021-07-19
Authors/Contributors
Abstract
In this thesis, we have developed a semi-autonomous behavior that allows us to control a drone with less effort. We have also presented a technique that enables autonomous repeating of a previously traversed route using the visual navigation system. Our first application demonstrates an experiment with driving a drone using a vision-based control (visual servoing) method, particularly by tracking selected targets in an image view. In the second application, a drone equipped with a monocular camera has been derived manually on a path. Invariant semantic features (i.e., objects) have been extracted using an object detection neural network, YOLO. Using these features, we show that the drone can repeat the traversed route autonomously independently from the lighting condition and even appearance changes.
Document
Identifier
etd21457
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Vaughan, Richard
Language
English
Member of collection
Attachment Size
input_data\22283\etd21457.pdf 4.89 MB