Autonomous navigation of drones using computer vision has achieved promising performance. Nano-sized drones based on edge computing platforms are lightweight, flexible, and cheap, thus suitable for exploring narrow spaces. However, due to their extremely limited computing power and storage, vision algorithms designed for high-performance GPU platforms cannot be used for nano drones. To address this issue this paper presents a lightweight CNN depth estimation network deployed on nano drones for obstacle avoidance. Inspired by Knowledge Distillation (KD), a Channel-Aware Distillation Transformer (CADiT) is proposed to facilitate the small network to learn knowledge from a larger network. The proposed method is validated on the KITTI dataset and tested on a nano drone Crazyflie, with an ultra-low power microprocessor GAP8.

翻译：基于计算机视觉的无人机自主导航已取得显著进展。基于边缘计算平台的纳米无人机具有轻量化、灵活且低成本的特点，因而适用于狭小空间探索。然而，由于其计算能力和存储空间极为有限，为高性能GPU平台设计的视觉算法无法直接应用于纳米无人机。针对这一问题，本文提出了一种部署于纳米无人机的轻量级卷积神经网络深度估计网络，用于实现避障功能。受知识蒸馏（KD）启发，本文设计了一种通道感知蒸馏Transformer（CADiT），以促进小网络从大网络中学习知识。所提方法在KITTI数据集上进行了验证，并在配备超低功耗微处理器GAP8的纳米无人机Crazyflie上完成了测试。