Wenhua Ding

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, EventPro. EventPro features two components: Count Every Rotation achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. Every Rotation Counts leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that EventPro achieves a sensing latency of 3 ms and a rotational speed estimation error of merely 0.23%. Additionally, EventPro infers drone flight commands with 96.5% precision and improves drone tracking accuracy by over 22% when combined with other sensing modalities. Demo: https://eventpro25.github.io/EventPro/.

With the evolution of mobile embodied intelligence, agents such as drones and autonomous robots are transitioning toward high agility. This shift imposes stringent demands on embodied perception, requiring high-accuracy and low-latency feedback loops for reliable interaction. Event-based vision has emerged as a transformative paradigm. Its microsecond-level temporal resolution and high dynamic range render it ideal for embodied perception tasks on high-agility mobile platforms. However, asynchronous nature, substantial noise, lack of persistent semantic information, and large data volume pose challenges for processing on resource-constrained mobile agents. This article surveys the literature from 2014–2025 and presents a comprehensive overview of event-based mobile embodied perception. We organize review around four key pillars: event abstraction methods, perception algorithm advancements, hardware and software acceleration strategies, and mobile applications . We discuss critical tasks including visual odometry, object tracking, optical flow, and 3D reconstruction, while highlighting challenges associated with sensor fusion and real-time deployment. Furthermore, we outline future research directions, such as improving event cameras with advanced optics and leveraging neuromorphic computing for efficient processing. To support ongoing research, we provide an open-source Online Sheet with recent developments. We hope this survey serves as a reference, facilitating adoption of event-based vision across diverse mobile embodied applications.