Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining
popularity due to their small size, enabling new tasks such as indoor
navigation or people monitoring. Nonetheless, their size and simple electronics
pose severe challenges in implementing advanced onboard intelligence. This work
proposes a new automatic optimization pipeline for visual pose estimation tasks
using Deep Neural Networks (DNNs). The pipeline leverages two different Neural
Architecture Search (NAS) algorithms to pursue a vast complexity-driven
exploration in the DNNs' architectural space. The obtained networks are then
deployed on an off-the-shelf nano-drone equipped with a parallel ultra-low
power System-on-Chip leveraging a set of novel software kernels for the
efficient fused execution of critical DNN layer sequences. Our results improve
the state-of-the-art reducing inference latency by up to 3.22x at iso-error.

通过使用深度神经网络进行视觉姿态估计任务的新型自动优化流程，本研究针对小型自主无人机的智能化实现提出了两种不同的神经架构搜索算法，采用一套新颖的软件核心，通过并行超低功耗的片上系统在现成的纳米无人机上执行关键深度神经网络层序列，成功提高了当前技术水平并将推理延迟降低高达 3.22 倍。

优化的深度神经网络在纳米无人机上的视觉姿态估计部署

Optimized Deployment of Deep Neural Networks for Visual Pose Estimation  on Nano-drones

Miniaturized autonomous unmanned aerial vehicles (UAVs) are an emerging and
trending topic. With their form factor as big as the palm of one hand, they can
reach spots otherwise inaccessible to bigger robots and safely operate in human
surroundings. The simple electronics aboard such robots (sub-100mW) make them
particularly cheap and attractive but pose significant challenges in enabling
onboard sophisticated intelligence. In this work, we leverage a novel neural
architecture search (NAS) technique to automatically identify several
Pareto-optimal convolutional neural networks (CNNs) for a visual pose
estimation task. Our work demonstrates how real-life and field-tested robotics
applications can concretely leverage NAS technologies to automatically and
efficiently optimize CNNs for the specific hardware constraints of small UAVs.
We deploy several NAS-optimized CNNs and run them in closed-loop aboard a 27-g
Crazyflie nano-UAV equipped with a parallel ultra-low power System-on-Chip. Our
results improve the State-of-the-Art by reducing the in-field control error of
32% while achieving a real-time onboard inference-rate of ~10Hz@10mW and
~50Hz@90mW.

运用神经结构搜索技术 (NAS), 我们为视觉姿态估计任务自动确定了几个帕累托最优的卷积神经网络 (CNNs), 以适应小型 UAV 硬件限制的特点，展示了现实生活中多个 NAS 优化的 CNNs 可以在 27 克的 Crazyflie 纳米 UAV 上运行，并通过降低 32％的实地控制误差，具有实时的推断速率～10Hz@10mW 和～50Hz@90mW，从而进一步优化了 CNNs。