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.
Highlights
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]
Features of Autonomy We identified that autonomous navigation features fall into three distinct groups: “Awareness”, which details the vehicle’s understanding of its surroundings, which can be collected via non-specific sensors; “Basic Navigation”, which includes the functionality expected from autonomous navigation, such as avoiding relevant obstacles and collision avoidance strategies; and “Expanded Navigation”, which covers features with a higher development depth such as pathway planning and multiple use case autonomous navigation
This encompasses any feature that is included in the referred solution as analysis of the drone’s spatial environment; though basic navigation features can be developed without this understanding, it limits the capability of the said navigation
Summary
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 applications. 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. While the general focus is on DNN-based papers, some non-DNN-based solutions are present in the collected papers for contrast
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call