  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.
