超高频和谐：mmWave雷达与事件相机协同实现精准无人机降落
Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing
AI总结：
针对传统帧相机与mmWave雷达采样频率不匹配导致的吞吐量瓶颈，本研究提出基于事件相机与mmWave雷达协同的高精度低延迟地面定位系统mmE-Loc，通过一致性指导的协同跟踪和图信息自适应联合优化模块实现高效传感器融合，在真实无人机降落场景中显著提升了定位精度并降低了延迟。
摘要：
为实现精准、高效且安全的无人机降落，地面平台应实时、准确地定位下降的无人机并引导其至指定位置。尽管将mmWave感知与摄像头结合可提高定位精度，但传统帧相机的采样频率低于mmWave雷达，造成了系统吞吐量的瓶颈。在本工作中，我们用事件相机替代传统帧相机，这是一种在地面平台设置中与mmWave雷达在采样频率上相协调的新型传感器，并引入了mmE-Loc，一个专为无人机降落设计的高精度、低延迟地面定位系统。为充分利用这些模态之间的时间一致性和空间互补性，我们提出了两个创新模块：一致性指导的协同跟踪和图信息自适应联合优化，以实现准确的无人机测量提取和高效的传感器融合。一家领先的无人机配送公司在降落场景中的大量真实世界实验表明，mmE-Loc在定位精度和延迟方面均优于最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
