Article
Flying Free: A Research Overview of Deep Learning in
Drone Navigation Autonomy
Thomas Lee
1
, Susan Mckeever
2
and Jane Courtney
1,
*
Citation: Lee, T.; Mckeever, S.;
Courtney, J. Flying Free: A Research
Overview of Deep Learning in Drone
Navigation Autonomy. Drones 2021, 5,
52. https://doi.org/10.3390/
drones5020052
Academic Editor: George
Nikolakopoulos
Received: 4 May 2021
Accepted: 4 June 2021
Published: 17 June 2021
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4.0/).
1
School of Electronic and Electrical Engineering, TU Dublin, Central Quad, Grangegorman Lower,
D07 ADY7 Dublin, Ireland; thomas.lee@tudublin.ie
2
School of Computer Science, TU Dublin, Central Quad, Grangegorman Lower, D07 ADY7 Dublin, Ireland;
susan.mckeever@tudublin.ie
* Correspondence: jane.courtney@tudublin.ie
Abstract:
With the rise of Deep Learning approaches in computer vision applications, significant
strides have been made towards vehicular autonomy. Research activity in autonomous drone
navigation has increased rapidly in the past five years, and drones are moving fast towards the
ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific
tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and
a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is
established by mapping the definitions of vehicular autonomy levels, as defined by the Society of
Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when
applied to drones. A top–down examination of research work in the area is conducted, focusing on
drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy
levels are cross-checked against the drone navigation tasks addressed in each work to provide a
framework for understanding the trajectory of current research. This work serves as a guide to
research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating
key works and areas of opportunity for development of this area in the future.
Keywords:
artificial intelligence; deep learning; neural networks; artificial neural networks; multi-
layer neural network; neural network hardware; autonomous systems; internet of things; machine
vision; unmanned autonomous vehicles; unmanned aerial vehicles
1. Introduction
Since 2016, drone technology has seen an increase in consumer popularity, growing in
market size from 2 billion USD in 2016 [
1
] to 22.5 billion USD in 2020 [
2
]. As small form
factor UAVs similar to the drone pictured in Figure 1 flooded the market, several industries
adopted these devices for use in areas including but not limited to cable inspection, product
monitoring, civil planning, agriculture and public safety. In research, this technology has
been used mostly in areas related to data gathering and analysis to support these applica-
tions. However, direct development of navigation systems to provide great automation of
drone operation has become a realistic aim, given the increasing capability of Deep Neural
Networks (DNN) in computer vision, and its application to the related application area,
vehicular autonomy. The work outlined in this paper is twofold: (1) it provides a common
vocabulary around levels of drone autonomy, mapped against drone functionality, and
(2) it examines research works within these functionality areas, so as to provide an indexed
top–down perspective of research activity in the autonomous drone navigation sector. With
recent advances in hardware and software capability, Deep Learning has become very
versatile and there is no shortage of papers involving its application to drone autonomy.
While domain-knowledge engineered solutions exist that utilize precision GPS, lidar, image
processing and/or computer vision to form a system for autonomous navigation, these
solutions are not robust, have a high cost for implementation, and can require important
Drones 2021, 5, 52. https://doi.org/10.3390/drones5020052 https://www.mdpi.com/journal/drones
