Real-time applications for autonomous operations depend largely on fast and robust vision-based localization systems. Since image processing tasks require processing large amounts of data, the computational resources often limit the performance of other processes. To overcome this limitation, traditional marker-based localization systems are widely used since they are easy to integrate and achieve reliable accuracy. However, classical marker-based localization systems significantly depend on standard cameras with low frame rates, which often lack accuracy due to motion blur. In contrast, event-based cameras provide high temporal resolution and a high dynamic range, which can be utilized for fast localization tasks, even under challenging visual conditions. This paper proposes a simple but effective event-based pose estimation system using active LED markers (ALM) for fast and accurate pose estimation. The proposed algorithm is able to operate in real time with a latency below \SI{0.5}{\milli\second} while maintaining output rates of \SI{3}{\kilo \hertz}. Experimental results in static and dynamic scenarios are presented to demonstrate the performance of the proposed approach in terms of computational speed and absolute accuracy, using the OptiTrack system as the basis for measurement.

翻译：自主操作的实时应用在很大程度上依赖于快速且鲁棒的基于视觉的定位系统。由于图像处理任务需要处理大量数据，计算资源常常限制了其他过程的性能。为克服这一限制，传统的基于标记物的定位系统因其易于集成且能实现可靠精度而被广泛使用。然而，经典的基于标记物的定位系统严重依赖于帧率较低的标准相机，这常因运动模糊而缺乏精度。相比之下，事件相机具有高时间分辨率和高动态范围，即使在具有挑战性的视觉条件下也能用于快速定位任务。本文提出了一种简单但有效的事件相机位姿估计系统，该系统利用主动式LED标记物实现快速且准确的位姿估计。所提算法能够以低于0.5毫秒的延迟实时运行，同时保持3千赫兹的输出速率。文中展示了静态和动态场景下的实验结果，并以OptiTrack系统作为测量基准，从计算速度和绝对精度两方面验证了所提方法的性能。