Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing
Haoyang Wang, Jingao Xu, Xinyu Luo, Xuecheng Chen, Ting Zhang, Ruiyang, Duan, Yunhao Liu, Xinlei Chen

TL;DR
This paper introduces mmE-Loc, a novel high-precision drone localization system that combines mmWave radar and event cameras with innovative fusion modules, achieving superior accuracy and low latency for drone landing tasks.
Contribution
The work presents a new sensor fusion framework using event cameras and mmWave radar, with novel modules for improved drone localization in real-time landing scenarios.
Findings
Outperforms state-of-the-art localization methods in accuracy.
Reduces latency in drone landing localization.
Demonstrated effectiveness in real-world drone delivery scenarios.
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, 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
