用事件相机与毫米波雷达协同，实现高精度低延迟无人机着陆定位。
Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing
- 用事件相机替代传统摄像头，与毫米波雷达同步采样频率。
- 在真实场景中定位误差小于2厘米，延迟低于5毫秒。
- 适合无人机自动降落、智能物流等对实时性要求高的场景。
为实现精准、高效、安全的无人机着陆，地面平台需实时准确地定位下降中的无人机并引导其至指定位置。尽管毫米波感知结合摄像头可提升定位精度，但传统帧相机的采样频率远低于毫米波雷达，成为系统吞吐量的瓶颈。本文将传统帧相机替换为事件相机，该传感器在采样频率上与毫米波雷达高度一致，提出mmE-Loc这一高精度、低延迟的地面定位系统，专用于无人机着陆。为充分挖掘两种模态间的时序一致性与空间互补性，设计了两项创新模块：一致性指导的协同跟踪与图结构引导的自适应联合优化，以实现精准的无人机测量提取与高效传感器融合。来自领先无人机配送公司的大量真实着陆场景实验表明，mmE-Loc在定位精度和延迟方面均优于现有最优方法。
原文摘要 · Abstract (English)
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, the lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace the traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within the ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the \textit{temporal consistency} and \textit{spatial complementarity} between these modalities, we propose two innovative modules, \textit{consistency-instructed collaborative tracking} and \textit{graph-informed adaptive joint optimization}, for accurate drone measurement extraction and efficient sensor fusion. Extensive real-world experiments in landing scenarios from a leading drone delivery company demonstrate that mmE-Loc outperforms state-of-the-art methods in both localization accuracy and latency.
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