EventBoost: Event-based Acceleration Platform for Real-time Drone Localization and Tracking

摘要

Drones have demonstrated their pivotal role in various applications such as search-and-rescue, smart logistics, and industrial inspection, with accurate localization playing an indispensable part. However, in high dynamic range and rapid motion scenarios, traditional visual sensors often face challenges in pose estimation. Event cameras, with their high temporal resolution, present a fresh opportunity for perception in such challenging environments. Current efforts resort to event-visual fusion to enhance the drone’s sensing capability. Yet, the lack of efficient event-visual fusion algorithms and corresponding acceleration hardware causes the potential of event cameras to remain underutilized. In this paper, we introduce EventBoost, an acceleration platform designed for drone-based applications with event-image fusion. We propose a suit of novel algorithms through software-hardware co-design on Zynq SoC, aimed at enhancing real-time localization precision and speed. EventBoost achieves enhanced visual fusion precision and markedly elevated processing efficiency. The performance comparison with two state-of-the-art systems shows EventBoost achieves 24.33% improvement in accuracy with 30 ms latency on resource-constrained platforms.