An Event-based Fast Intensity Reconstruction Scheme for UAV Real-time Perception
This work addresses the problem of real-time perception for UAVs under challenging visual conditions, offering a significant improvement over existing methods but is incremental as it builds on event-based intensity reconstruction techniques.
The paper tackles the challenge of extracting effective information from asynchronous event streams for onboard event camera implementation by proposing a streamlined event-based intensity reconstruction scheme called ESI, which achieves real-time intensity reconstruction at 100 FPS and demonstrates superior runtime efficiency and reconstruction quality compared to state-of-the-art algorithms, enabling effective UAV visual tracking under extremely low illumination conditions (2-10 lux).
Event cameras offer significant advantages, including a wide dynamic range, high temporal resolution, and immunity to motion blur, making them highly promising for addressing challenging visual conditions. Extracting and utilizing effective information from asynchronous event streams is essential for the onboard implementation of event cameras. In this paper, we propose a streamlined event-based intensity reconstruction scheme, event-based single integration (ESI), to address such implementation challenges. This method guarantees the portability of conventional frame-based vision methods to event-based scenarios and maintains the intrinsic advantages of event cameras. The ESI approach reconstructs intensity images by performing a single integration of the event streams combined with an enhanced decay algorithm. Such a method enables real-time intensity reconstruction at a high frame rate, typically 100 FPS. Furthermore, the relatively low computation load of ESI fits onboard implementation suitably, such as in UAV-based visual tracking scenarios. Extensive experiments have been conducted to evaluate the performance comparison of ESI and state-of-the-art algorithms. Compared to state-of-the-art algorithms, ESI demonstrates remarkable runtime efficiency improvements, superior reconstruction quality, and a high frame rate. As a result, ESI enhances UAV onboard perception significantly under visual adversary surroundings. In-flight tests, ESI demonstrates effective performance for UAV onboard visual tracking under extremely low illumination conditions(2-10lux), whereas other comparative algorithms fail due to insufficient frame rate, poor image quality, or limited real-time performance.