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

Haoyang Wang, Jingao Xu, Xinyu Luo, Ting Zhang, Xuecheng Chen, Ruiyang Duan, Yunhao Liu, Jianfeng Zheng, Weijie Hong, Xiaoqiang Ji, Yuqing Tang, Xinlei Chen

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$m$ localization accuracy and 5.12$ms$ end-to-end latency, outperforming four state-of-the-art methods by over 48% and 62%, respectively.

External IDs:doi:10.1109/tmc.2026.3666235