Motion and dynamic environments, especially under challenging lighting conditions, are still an open issue for robust robotic applications. In this paper, we propose an end-to-end pipeline for real-time, low latency, 6 degrees-of-freedom pose estimation of fiducial markers. Instead of achieving a pose estimation through a conventional frame-based approach, we employ the high-speed abilities of event-based sensors to directly refine the spatial transformation, using consecutive events. Furthermore, we introduce a novel two-way verification process for detecting tracking errors by backtracking the estimated pose, allowing us to evaluate the quality of our tracking. This approach allows us to achieve pose estimation at a rate up to 156~kHz, while only relying on CPU resources. The average end-to-end latency of our method is 3~ms. Experimental results demonstrate outstanding potential for robotic tasks, such as visual servoing in fast action-perception loops.
翻译:动态和动态环境,特别是在具有挑战性的照明条件下,对于强大的机器人应用来说,仍然是一个尚未解决的问题。在本文件中,我们提议对实时、低潜值、6度自由度的终端到终端管道进行实时、低潜值、6度自由度的标记估计。我们不是通过传统的基于框架的方法实现构成性估计,而是利用事件感应器的高速能力直接改进空间转换,使用连续的事件。此外,我们引入了一种新的双向核查程序,通过反跟踪估计的外形来探测跟踪错误,从而使我们能够评估跟踪质量。这一方法使我们能够以高达156~kHz的速率作出估计,同时只依靠CPU资源。我们方法的平均端到终端的悬浮度是3~ms。实验结果显示机器人任务的突出潜力,例如快速行动感知循环中的视觉振荡。