arXiv 2510.006462025-10-01cs.RO

面向精准无人机降落的高频跨模态视觉定位服务实现

Enabling High-Frequency Cross-Modality Visual Positioning Service for Accurate Drone Landing

Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院）；Carnegie Mellon University, USA（卡内基梅隆大学）；Meituan Academy of Robotics Shenzhen and Meituan Inc., China（美团机器人深圳研究院和美团公司）；Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China（电子科技大学深圳研究院）；School of Software and BNRist, Tsinghua University, Beijing 100084, China（软件学院和BNRist，清华大学，北京100084，中国）

Haoyang Wang, Xinyu Luo, Wenhua Ding, Jingao Xu, Xuecheng Chen, Ruiyang Duan, Jialong Chen, Haitao Zhang, Yunhao Liu, Xinlei Chen

备注：: 15 pages, 23 figures

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AI总结：

针对无人机降落阶段传统定位系统不可靠、现有VPS精度与效率不足的问题，提出基于事件相机的EV-Pose系统，通过时空特征引导位姿估计与运动感知分层优化，实现高精度高频6-DoF位姿追踪，性能超基线50%以上。

摘要：

经过多年发展，无人机配送正在重塑物流行业。其核心在于实时6自由度（6-DoF）无人机位姿追踪，可实现精准飞行控制与精确无人机降落。随着城市3D地图的广泛普及，视觉定位服务（Visual Positioning Service，VPS）——一种移动位姿估计系统——已被适配用于增强降落阶段的无人机位姿追踪，因为GPS等传统系统在城市环境中会因信号衰减和多径传播而不可靠。然而，当前的VPS部署在无人机上时，在估计精度和效率方面均存在局限。在本研究中，我们采用事件相机重新设计了面向无人机的VPS，并提出EV-Pose系统，以实现精准、高频的6-DoF位姿追踪，支撑精准无人机降落。EV-Pose引入了时空特征引导的位姿估计模块，该模块提取时间距离场以实现3D点云地图匹配，从而完成位姿估计；同时还提出了运动感知的分层融合与优化方案，通过在事件滤波的“早期阶段”和位姿优化的“后期阶段”利用无人机运动信息，提升上述估计的精度与效率。评估结果显示，EV-Pose的旋转精度达1.34°，平移精度达6.9mm，追踪延迟为10.08ms，性能优于基线方法50%以上，从而可实现精准无人机降落。演示页面：https://ev-pose.github.io/

英文摘要：

After years of growth, drone-based delivery is transforming logistics. At its core, real-time 6-DoF drone pose tracking enables precise flight control and accurate drone landing. With the widespread availability of urban 3D maps, the Visual Positioning Service (VPS), a mobile pose estimation system, has been adapted to enhance drone pose tracking during the landing phase, as conventional systems like GPS are unreliable in urban environments due to signal attenuation and multi-path propagation. However, deploying the current VPS on drones faces limitations in both estimation accuracy and efficiency. In this work, we redesign drone-oriented VPS with the event camera and introduce EV-Pose to enable accurate, high-frequency 6-DoF pose tracking for accurate drone landing. EV-Pose introduces a spatio-temporal feature-instructed pose estimation module that extracts a temporal distance field to enable 3D point map matching for pose estimation; and a motion-aware hierarchical fusion and optimization scheme to enhance the above estimation in accuracy and efficiency, by utilizing drone motion in the \textit{early stage} of event filtering and the \textit{later stage} of pose optimization. Evaluation shows that EV-Pose achieves a rotation accuracy of 1.34$\degree$ and a translation accuracy of 6.9$mm$ with a tracking latency of 10.08$ms$, outperforming baselines by $>$50\%, \tmcrevise{thus enabling accurate drone landings.} Demo: https://ev-pose.github.io/

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。