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：纳米无人机之间的相对定位是集群操作的基础。本文针对小型化纳米无人机（直径约10厘米）展开研究，这类无人机因其紧凑的外形带来的新型应用场景而日益受到关注。然而，其灵活性的代价是机载资源（如传感器、处理单元和内存）有限，这限制了机载算法的复杂度。克服这些局限的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。本文利用仅搭载灰度低分辨率摄像头和超低功耗片上系统（SoC）的纳米无人机，解决其之间的相对位姿估计难题。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，该系统在搭载GWT GAP8 SoC的Crazyflie纳米无人机上以39Hz频率运行，功耗仅101mW。我们将其与三种最先进系统进行对比。在包含3万张图像的真实数据集上，相较于性能最优的最先进方法，我们的模型在水平图像坐标上的R平方值从32%提升至47%，垂直图像坐标上从18%提升至55%。实地测试表明，相较于先前的最先进工作，平均跟踪误差降低37%，且续航性能可达整个电池生命周期（4分钟）。