Name
Title | ||
|---|---|---|
An Open Source and Open Hardware Deep Learning-Powered Visual Navigation Engine for Autonomous Nano-UAVs |
Abstract | ||
|---|---|---|
Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diameter and sub-10 Watts of total power budget, have so far been considered incapable of running sophisticated visual-based autonomous navigation software without external aid from base-stations, ad-hoc local positioning infrastructure, and powerful external computation servers. In this work, we present what is, to the best of our knowledge, the first 27g nano-UAV system able to run aboard an end-to-end, closed-loop visual pipeline for autonomous navigation based on a state-of-the-art deep-learning algorithm, built upon the open-source CrazyFlie 2.0 nano-quadrotor. Our visual navigation engine is enabled by the combination of an ultra-low power computing device (the GAP8 system-on-chip) with a novel methodology for the deployment of deep convolutional neural networks (CNNs). We enable onboard real-time execution of a state-of-the-art deep CNN at up to 18Hz. Field experiments demonstrate that the system's high responsiveness prevents collisions with unexpected dynamic obstacles up to a flight speed of 1.5m/s. In addition, we also demonstrate the capability of our visual navigation engine of fully autonomous indoor navigation on a 113m previously unseen path. To share our key findings with the embedded and robotics communities and foster further developments in autonomous nano-UAVs, we publicly release all our code, datasets, and trained networks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DCOSS.2019.00111 | 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) |
Keywords | Field | DocType |
autonomous navigation, nano-size UAVs, deep learning, CNN, heterogeneous computing, parallel ultra-low power, bio-inspired | Power budget,Software deployment,Convolutional neural network,Server,Software,Artificial intelligence,Deep learning,Engineering,Computer hardware,Robotics,Computation | Journal |
Volume | ISSN | ISBN |
abs/1905.04166 | 2325-2936 | 978-1-7281-0571-0 |
Citations | PageRank | References |
3 | 0.40 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Palossi | 1 | 41 | 6.12 |
| Francesco Conti 0001 | 2 | 125 | 18.24 |
| Luca Benini | 3 | 13116 | 1188.49 |