Recently, the event camera has become a popular and promising vision sensor in the research of simultaneous localization and mapping and computer vision owing to its advantages: low latency, high dynamic range, and high temporal resolution. As a basic part of the feature-based SLAM system, the feature tracking method using event cameras is still an open question. In this article, we present a novel asynchronous event feature generation and tracking algorithm operating directly on event-streams to fully utilize the natural asynchronism of event cameras. The proposed algorithm consists of an event-corner detection unit, a descriptor construction unit, and an event feature tracking unit. The event-corner detection unit addresses a fast and asynchronous corner detector to extract event-corners from event-streams. For the descriptor construction unit, we propose a novel asynchronous gradient descriptor inspired by the scale-invariant feature transform descriptor, which helps to achieve quantitative measurement of similarity between event feature pairs. The construction of the gradient descriptor can be decomposed into three stages: speed-invariant time surface maintenance and extraction, principal orientation calculation, and descriptor generation. The event feature tracking unit combines the constructed gradient descriptor and an event feature matching method to achieve asynchronous feature tracking. We implement the proposed algorithm in C++ and evaluate it on a public event dataset. The experimental results show that our proposed method achieves improvement in terms of tracking accuracy and real-time performance when compared with the state-of-the-art asynchronous event-corner tracker and with no compromise on the feature tracking lifetime.

近年来，事件相机由于其低延迟、高动态范围和高时间分辨率等优点，已成为同时定位和映射以及计算机视觉研究中一种流行且有前途的视觉传感器。作为基于特征的SLAM系统的基础部分，使用事件相机的特征跟踪方法仍然是一个悬而未决的问题。在本文中，我们提出了一种直接在事件流上运行的新型异步事件特征生成和跟踪算法，以充分利用事件相机的自然异步性。所提出的算法由事件角检测单元、描述符构建单元和事件特征跟踪单元组成。事件角检测单元处理快速异步角检测器以从事件流中提取事件角。对于描述符构造单元，我们提出了一种受尺度不变特征变换描述符启发的新型异步梯度描述符，它有助于实现事件特征对之间相似性的定量测量。梯度描述符的构建可以分解为三个阶段：速度不变的时间表面维护和提取、主方向计算和描述符生成。事件特征跟踪单元结合构建的梯度描述符和事件特征匹配方法来实现异步特征跟踪。我们在 C++ 中实现了所提出的算法，并在公共事件数据集上对其进行了评估。