Count Every Rotation and Every Rotation Counts: Exploring Drone Dynamics via Propeller Sensing
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908672870842368 |
|---|---|
| author | Chen, Xuecheng Xu, Jingao Ding, Wenhua Wang, Haoyang Luo, Xinyu Duan, Ruiyang Chen, Jialong Wang, Xueqian Liu, Yunhao Chen, Xinlei |
| author_facet | Chen, Xuecheng Xu, Jingao Ding, Wenhua Wang, Haoyang Luo, Xinyu Duan, Ruiyang Chen, Jialong Wang, Xueqian Liu, Yunhao Chen, Xinlei |
| contents | As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, \sysname. \sysname features two components: \textit{Count Every Rotation} achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. \textit{Every Rotation Counts} leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that \sysname achieves a sensing latency of 3$ms$ and a rotational speed estimation error of merely 0.23\%. Additionally, \sysname infers drone flight commands with 96.5\% precision and improves drone tracking accuracy by over 22\% when combined with other sensing modalities. \textit{ Demo: {\color{blue}https://eventpro25.github.io/EventPro/.} } |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13100 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Count Every Rotation and Every Rotation Counts: Exploring Drone Dynamics via Propeller Sensing Chen, Xuecheng Xu, Jingao Ding, Wenhua Wang, Haoyang Luo, Xinyu Duan, Ruiyang Chen, Jialong Wang, Xueqian Liu, Yunhao Chen, Xinlei Robotics As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, \sysname. \sysname features two components: \textit{Count Every Rotation} achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. \textit{Every Rotation Counts} leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that \sysname achieves a sensing latency of 3$ms$ and a rotational speed estimation error of merely 0.23\%. Additionally, \sysname infers drone flight commands with 96.5\% precision and improves drone tracking accuracy by over 22\% when combined with other sensing modalities. \textit{ Demo: {\color{blue}https://eventpro25.github.io/EventPro/.} } |
| title | Count Every Rotation and Every Rotation Counts: Exploring Drone Dynamics via Propeller Sensing |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.13100 |