面向超低功耗纳米无人机的自适应深度学习高效视觉姿态估计
Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones
AI总结：
本文针对纳米无人机资源极度受限的问题，提出一种自适应深度学习机制，通过结合两个性能-成本权衡不同的CNN和三种基于时间一致性及辅助任务的主动切换策略，在真实硬件上实现了延迟降低28%或MAE降低3%、峰值性能提升6%的效果。
摘要：
直径小于10厘米的纳米无人机因其在狭窄环境和靠近人类的场景中的适用性而日益受到关注，而这些场景是大型飞行无人机无法进入的。然而，其微小的外形尺寸也带来了主要缺点：用于机载执行感知流程的内存和处理器资源极度受限。因此，基于轻量级深度学习的方法正变得越来越流行，这凸显了计算效率和节能的重要性，因为它们可能决定一个完全可用的闭环系统与一个失败系统之间的差异。在本工作中，为了最大限度地利用纳米无人机上极其有限的资源，我们提出了一种新颖的自适应深度学习机制，用于高效执行基于视觉的人体姿态估计任务。我们利用两个最先进的卷积神经网络（CNN），它们在回归性能与计算成本之间具有不同的权衡。通过将这些CNN与三种基于输出时间一致性和辅助任务的新颖自适应策略相结合，以主动切换正在执行的CNN，我们提出了六种不同的系统。在真实世界数据集和实际纳米无人机硬件上，与仅执行更大且最准确的最先进模型相比，我们性能最佳的系统在保持相同平均绝对误差（MAE）的情况下实现了28%的延迟降低，在等延迟条件下实现了3%的MAE降低，并达到了绝对峰值性能，即比最先进模型好6%。
英文摘要：
Sub-10cm diameter nano-drones are gaining momentum thanks to their applicability in scenarios prevented to bigger flying drones, such as in narrow environments and close to humans. However, their tiny form factor also brings their major drawback: ultra-constrained memory and processors for the onboard execution of their perception pipelines. Therefore, lightweight deep learning-based approaches are becoming increasingly popular, stressing how computational efficiency and energy-saving are paramount as they can make the difference between a fully working closed-loop system and a failing one. In this work, to maximize the exploitation of the ultra-limited resources aboard nano-drones, we present a novel adaptive deep learning-based mechanism for the efficient execution of a vision-based human pose estimation task. We leverage two State-of-the-Art (SoA) convolutional neural networks (CNNs) with different regression performance vs. computational costs trade-offs. By combining these CNNs with three novel adaptation strategies based on the output's temporal consistency and on auxiliary tasks to swap the CNN being executed proactively, we present six different systems. On a real-world dataset and the actual nano-drone hardware, our best-performing system, compared to executing only the bigger and most accurate SoA model, shows 28% latency reduction while keeping the same mean absolute error (MAE), 3% MAE reduction while being iso-latency, and the absolute peak performance, i.e., 6% better than SoA model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
