Relative drone-to-drone localization is a fundamental building block for any
swarm operations. We address this task in the context of miniaturized
nano-drones, i.e., 10cm in diameter, which show an ever-growing interest due to
novel use cases enabled by their reduced form factor. The price for their
versatility comes with limited onboard resources, i.e., sensors, processing
units, and memory, which limits the complexity of the onboard algorithms. A
traditional solution to overcome these limitations is represented by
lightweight deep learning models directly deployed aboard nano-drones. This
work tackles the challenging relative pose estimation between nano-drones using
only a gray-scale low-resolution camera and an ultra-low-power System-on-Chip
(SoC) hosted onboard. We present a vertically integrated system based on a
novel vision-based fully convolutional neural network (FCNN), which runs at
39Hz within 101mW onboard a Crazyflie nano-drone extended with the GWT GAP8
SoC. We compare our FCNN against three State-of-the-Art (SoA) systems.
Considering the best-performing SoA approach, our model results in an R-squared
improvement from 32 to 47% on the horizontal image coordinate and from 18 to
55% on the vertical image coordinate, on a real-world dataset of 30k images.
Finally, our in-field tests show a reduction of the average tracking error of
37% compared to a previous SoA work and an endurance performance up to the
entire battery lifetime of 4 minutes.

基于低分辨率相机和超低功耗车载 SoC 的垂直集成系统，通过全卷积神经网络（FCNN）在纳米无人机之间背对背的相对姿态估计方面取得了显著改进。