An Optimization Framework for Efficient Vision-Based Autonomous Drone Navigation
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
Fully autonomous drones are a new emerging field
that has enabled many applications such as gas source leakage
localization, wild-fire detection, smart agriculture, and search
and rescue missions in unknown limited communication and
GPS denied environments. Artificial intelligence and deep Neural
Networks (NN) have enabled applications such as visual perception and navigation which can be deployed to make drones
smarter and more efficient. However, deploying such techniques
on tiny drones is extremely challenging due to the limited
computational resources and power envelope of edge devices.
To achieve this goal, this paper proposes an efficient end-to-end
optimization method for deploying deep NN models for visionbased autonomous drone navigation applications, such as obstacle
avoidance and steering task. This paper formulates two different
methods for implementing the NN inference phase onto tiny
drones and analyzing the implementation results for each case: 1)
a Cloud-IoT implementation and 2) Onboard Processing. Several
models are trained with state-of-the-art scalable NN architectures
and the most efficient cases in terms of computation complexity
and accuracy are selected for implementation on a cloud server
and several edge devices. By designing hardware-friendly NN
models and optimal configuration of the implementation platforms, we were able to reach up to 97% accuracy, speed up
the computation 2.3x, have 22x less complexity, and 53% energy
reduction. Also, we achieve up to 25 fps on the GAP8 processor,
which is enough for real-time drone navigation requirements,
even when the model is running on a small IoT device.