Event cameras are bioinspired sensors with reaction times in the order of microseconds. This property makes them appealing for use in highly-dynamic computer vision applications. In this work,we explore the limits of this sensing technology and present an ultra-fast tracking algorithm able to estimate six-degree-of-freedom motion with dynamics over 25.8 g, at a throughput of 10 kHz,processing over a million events per second. Our method is capable of tracking either camera motion or the motion of an object in front of it, using an error-state Kalman filter formulated in a Lie-theoretic sense. The method includes a robust mechanism for the matching of events with projected line segments with very fast outlier rejection. Meticulous treatment of sparse matrices is applied to achieve real-time performance. Different motion models of varying complexity are considered for the sake of comparison and performance analysis

翻译：事件摄像头是按微秒顺序排列的反应时间的生物感应器。 此属性使得它们能够吸引用于高动态计算机视觉应用。 在这项工作中, 我们探索了这种遥感技术的极限, 并提出了一个超快的跟踪算法, 能够以10千赫兹的速率对动态超过25.8克的六度自由运动进行估计, 每秒处理100万次事件。 我们的方法能够跟踪镜头运动或一个物体在它面前的动作, 使用一个错误状态的卡尔曼过滤器, 以测谎为根据。 方法包括一个强大的机制, 将事件与预测的线段相匹配, 并快速断开。 对稀有的矩阵进行中度处理, 以实现实时性能。 为了比较和性能分析, 考虑不同复杂的运动模型。