Nano-sized unmanned aerial vehicles (UAVs) are well-fit for indoor applications and for close proximity to humans. To enable autonomy, the nano-UAV must be able to self-localize in its operating environment. This is a particularly-challenging task due to the limited sensing and compute resources on board. This work presents an online and onboard approach for localization in floor plans annotated with semantic information. Unlike sensor-based maps, floor plans are readily-available, and do not increase the cost and time of deployment. To overcome the difficulty of localizing in sparse maps, the proposed approach fuses geometric information from miniaturized time-of-flight sensors and semantic cues. The semantic information is extracted from images by deploying a state-of-the-art object detection model on a high-performance multi-core microcontroller onboard the drone, consuming only 2.5mJ per frame and executing in 38ms. In our evaluation, we globally localize in a real-world office environment, achieving 90% success rate. We also release an open-source implementation of our work.

翻译：纳米级无人机（UAV）非常适合室内应用及与人类近距离接触。为实现自主性，纳米无人机必须能够在运行环境中进行自身定位。由于机载感知和计算资源极其有限，这是一项极具挑战性的任务。本文提出一种在线机载定位方法，利用标注语义信息的楼层平面图实现定位。与基于传感器的地图不同，楼层平面图易于获取，且不会增加部署成本和时间。为克服在稀疏地图中定位的困难，所提方法融合了来自微型化飞行时间传感器的几何信息与语义线索。通过在无人机机载高性能多核微控制器上部署最新目标检测模型，从图像中提取语义信息，每帧处理仅消耗2.5mJ能量且执行时间为38ms。在实际办公环境中的全局定位评估中，我们实现了90%的成功率。同时，我们公开了本工作的开源实现代码。