For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we upgrade traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for precise drone landings. To fully exploit the \textit{temporal consistency} and \textit{spatial complementarity} between these two modalities, we propose two innovative modules: \textit{(i)} the Consistency-instructed Collaborative Tracking module, which further leverages the drone's physical knowledge of periodic micro-motions and structure for accurate measurements extraction, and \textit{(ii)} the Graph-informed Adaptive Joint Optimization module, which integrates drone motion information for efficient sensor fusion and drone localization. Real-world experiments conducted in landing scenarios with a drone delivery company demonstrate that mmE-Loc significantly outperforms state-of-the-art methods in both accuracy and latency.

翻译：为实现精确、高效且安全的无人机着陆，地面平台需实时准确定位下降中的无人机并引导其至指定位置。虽然毫米波传感与相机结合提升了定位精度，但传统帧相机的采样频率低于毫米波雷达，导致系统吞吐量存在瓶颈。本研究将地面平台中的传统帧相机升级为事件相机——一种能与毫米波雷达在采样频率上协调匹配的新型传感器，并提出了mmE-Loc系统，这是一个专为精准无人机着陆设计的高精度、低延迟地面定位系统。为充分挖掘两种模态间的时序一致性与空间互补性，我们提出了两个创新模块：一致性引导协同追踪模块，其进一步利用无人机周期性微运动及结构的物理知识以提取精确测量数据；以及图引导自适应联合优化模块，该模块融合无人机运动信息以实现高效的传感器融合与无人机定位。在与某无人机配送公司合作的实际着陆场景实验中，mmE-Loc系统在精度与延迟方面均显著优于现有先进方法。