Precise relative localization is a crucial functional block for swarm robotics. This work presents a novel autonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones, i.e., sub-40g of weight and sub-100mW processing power. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, from the dataset collection to the final in-field deployment, including dataset augmentation, quantization, and system optimizations. Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to ~2m distance. On a disjoint testing dataset our model yields a mean R2 score of 0.42 and a root mean square error of 18cm, which results in a mean in-field prediction error of 15cm and in a closed-loop control error of 17cm, over a ~60s-flight test. Ultimately, the proposed system improves the State-of-the-Art by showing long-endurance tracking performance (up to 2min continuous tracking), generalization capabilities being deployed in a never-seen-before environment, and requiring a minimal power consumption of 95mW for an onboard real-time inference-rate of 48Hz.

本研究提出了一种通过深度神经网络解决双对等纳米无人机的单目相对定位的新型自治端到端系统，从数据集收集到最终的实地部署，包括数据集增强、量化和系统优化。实验结果表明，我们的DNN可以通过使用低分辨率单色图像，在最多2米的距离内精确定位10厘米大小的目标纳米无人机，系统精度达到15厘米，控制误差为17厘米，实现了长时间跟踪，具有泛化能力，每秒运行速度为48Hz，仅需95mW的最小功耗。

基于超低功耗深度学习的纳米四轴飞行器单目相对定位