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.

翻译：无人机间的相对定位是任何集群操作的基础组成部分。本研究在微型纳米无人机（直径约10厘米）的背景下解决该任务，此类无人机因其紧凑的形态带来的新型应用场景而备受关注。其通用性的代价在于机载资源（传感器、处理单元和内存）有限，这限制了机载算法的复杂度。为克服这些限制，传统解决方案是直接在纳米无人机上部署轻量级深度学习模型。本研究仅通过机载的低分辨率灰度摄像头和超低功耗片上系统（SoC）来应对纳米无人机间的相对姿态估计挑战。我们提出了一种基于新型视觉全卷积神经网络（FCNN）的垂直集成系统，在搭载GWT GAP8 SoC的Crazyflie纳米无人机上，该系统以39Hz频率运行，功耗仅101mW。我们将所提出的FCNN与三种最先进（SoA）系统进行了比较。在包含3万张图像的真实世界数据集上，与性能最佳的SoA方法相比，我们的模型在水平图像坐标上的R平方改进从32%提升至47%，在垂直图像坐标上从18%提升至55%。最后，现场测试表明，与先前SoA工作相比，平均跟踪误差降低了37%，且续航性能可达整个4分钟电池寿命周期。