MM-E-LOC：通过超高频定位促进无人机精准着陆
mmE-Loc: Facilitating Accurate Drone Landing with Ultra-High-Frequency Localization
AI总结：
MM-E-LOC系统通过事件摄像头与毫米波雷达结合，解决传统帧式摄像头采样频率瓶颈，提出一致性指导协同跟踪和图信息自适应联合优化模块，实现高精度低延迟的无人机着陆定位。
摘要：
为实现精确、高效且安全的无人机着陆，地面平台应实时、准确地定位下降中的无人机并引导其至指定位置。尽管将毫米波感知与摄像头结合可提升定位精度，但传统帧式摄像头的采样频率低于毫米波雷达，造成系统吞吐量瓶颈。本研究中，我们将传统帧式摄像头升级为事件摄像头，这是一种在地面平台设置中与毫米波雷达采样频率相协调的新型传感器，并提出了MM-E-LOC，一种专为无人机精准着陆设计的高精度、低延迟地面定位系统。为充分利用这两种模态间的“时间一致性”和“空间互补性”，我们提出两个创新模块：一致性指导协同跟踪模块，进一步利用无人机周期性微动和结构的物理知识以提取精确测量值；图信息自适应联合优化模块，集成无人机运动信息以实现高效的传感器融合与无人机定位。在无人机配送公司的着陆场景中进行的真实世界实验表明，MM-E-LOC在精度和延迟方面均显著优于现有最先进方法。
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, 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.
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。
