A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones

By Daniele Palossi, Antonio Loquercio, Francesco Conti, Eric Flamand, Davide Scaramuzza, Luca Benini. 2018

173 citations recorded by the source corpus.

Fully miniaturized robots (e.g., drones), with artificial intelligence (AI)-based visual navigation capabilities, are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nano-drones with a size of a few cm2. In this paper, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on board resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultralow-power computing platform, and a 27-g commercial, open-source Crazyflie 2.0 nano-quadrotor. As part of our general methodology, we discuss the software mapping techniques that enable the DroNet state-of-the-art deep convolutional neural network to be fully executed aboard within a strict 6 frame-per-second real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner, it achieves 18 frames/s while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft. To share our key findings with the embedded and robotics communities and foster further developments in autonomous nano-unmanned aerial vehicles (UAVs), we publicly release all our code, datasets, and trained networks.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Robotics & Control

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Citations in the corpus

A General Framework for Uncertainty Estimation in Deep Learning (2019, methodology): “In our experiments, we selected T = 20 as it allows processing at ≈ 10Hz, which is acceptable for closed-loop control [32], [33].”