mmE-Loc: Facilitating Accurate Drone Landing With Ultra-High-Frequency Localization

IF 8.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
IEEE Transactions on Mobile Computing Pub Date : 2026-07-01 Epub Date: 2026-02-19 DOI:10.1109/TMC.2026.3666235
Haoyang Wang;Jingao Xu;Xinyu Luo;Ting Zhang;Xuecheng Chen;Ruiyang Duan;Yunhao Liu;Jianfeng Zheng;Weijie Hong;Xiaoqiang Ji;Yuqing Tang;Xinlei Chen
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引用次数: 0

Abstract

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 temporal consistency and spatial complementarity between these two modalities, we propose two innovative modules: (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 (ii) the Graph-informed Adaptive Joint Optimization module, which integrates drone motion information for efficient sensor fusion and drone localization. Extensive experiments (30+ hours) demonstrate that mmE-Loc attains 0.083 $\text{m}$ localization accuracy and 5.12 $\text{ms}$ end-to-end latency, outperforming four state-of-the-art methods by over 48% and 62%, respectively.
mmE-Loc:利用超高频定位技术促进无人机精确着陆
为了实现无人机的精确、高效、安全着陆,地面平台应该实时、准确地定位下降的无人机,并将其引导到指定地点。虽然毫米波传感与相机相结合可以提高定位精度,但与毫米波雷达相比,传统帧相机的采样频率较低,这给系统吞吐量带来了瓶颈。在这项工作中,我们用事件相机升级了传统的框架相机,事件相机是一种新型传感器,可以在地面平台设置中与毫米波雷达协调采样频率,并引入了mmE-Loc,一种高精度,低延迟的地面定位系统,专为无人机精确着陆而设计。为了充分利用这两种模式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:(i)一致性指导的协同跟踪模块,它进一步利用无人机对周期性微运动和结构的物理知识进行精确的测量提取;(ii)图形通知的自适应关节优化模块,它集成了无人机的运动信息,以实现高效的传感器融合和无人机定位。大量实验(30多个小时)表明,mmE-Loc实现了0.083 $\text{m}$的定位精度和5.12 $\text{ms}$的端到端延迟,分别比四种最先进的方法高出48%和62%。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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