Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generalize to different scenarios due to unmodeled effects. To tackle these deficiencies, we introduce the first data-driven feature tracker for event cameras, which leverages low-latency events to track features detected in an intensity frame. We achieve robust performance via a novel frame attention module, which shares information across feature tracks. Our tracker is designed to operate in two distinct configurations: solely with events or in a hybrid mode incorporating both events and frames. The hybrid model offers two setups: an aligned configuration where the event and frame cameras share the same viewpoint, and a hybrid stereo configuration where the event camera and the standard camera are positioned side-by-side. This side-by-side arrangement is particularly valuable as it provides depth information for each feature track, enhancing its utility in applications such as visual odometry and simultaneous localization and mapping.

翻译：事件相机因其高时间分辨率、增强的运动模糊鲁棒性及极稀疏的输出特性，已被证明是实现低延迟、低带宽特征跟踪的理想传感器，即使在复杂场景中亦表现出色。现有的事件相机特征跟踪方法多为基于手工设计或第一原理推导，但普遍存在参数调优复杂、对噪声敏感，以及因未建模效应导致泛化能力受限的问题。为克服这些不足，我们提出了首个数据驱动的事件相机特征跟踪器，该跟踪器利用低延迟事件流对强度图像帧中检测到的特征进行持续跟踪。通过引入一种新颖的帧注意力模块——该模块能够在不同特征轨迹间共享信息——我们实现了鲁棒的跟踪性能。本跟踪器设计为可在两种独立配置下运行：纯事件模式，或融合事件与帧数据的混合模式。混合模式进一步提供两种设置：事件相机与帧相机共享同一视点的对齐配置，以及事件相机与标准相机并排放置的混合立体配置。这种并排布局尤其具有价值，因为它能为每条特征轨迹提供深度信息，从而显著提升其在视觉里程计、同步定位与建图等应用中的实用性。