【泡泡一分钟】一种实时的基于事件相机的异步角点检测和跟踪方法

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【泡泡一分钟】一种实时的基于事件相机的异步角点检测和跟踪方法

2019 年 1 月 28 日 泡泡机器人SLAM

每天一分钟，带你读遍机器人顶级会议文章

标题：Asynchronous Corner Detection and Tracking for Event Cameras in Real-Time

作者：Ignacio Alzugaray, Margarita Chli

来源：IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)，2018

编译：陈世浪

审核：颜青松

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摘要

最近出现的事件相机为高频跟踪带来了新的可能性，为传统视觉中的常见问题（如光照变化、运动模糊）来来了鲁棒性。

为了利用事件相机这些有吸引力的属性，研究者们已经开始关注如何解决它们不寻常的输出：如事件的异步流。随着大多数现有技术对事件流进行离散化，本质上是根据事件的时间戳对事件进行分组，我们仍然在利用这些摄像机的强大功能。

基于这种启发，本文提出了一种新的、纯基于事件的角点检测器和新的角点跟踪器，证明了在事件流中直接检测和跟踪角点是可能的。在基准测试数据集的评估显示，即使在采用所提议的方法在具有挑战性的场景中，检测到的拐角的数量和此类检测的可重复性也会显著增加，同时与文献中最有效的算法相比，可以使检测速度提高4倍以上。我们的方法检测和跟踪角点的速度超过每秒750万次，有望在高速的场景中应用。

Abstract

The recent emergence of bio-inspired event cameras has opened up exciting new possibilities in high-frequeney tracking, bringing robustness to common problems in traditional vision, such as lighting changes and motion blur.

In order to leverage these attractive attributes of the event cameras, research has been focusing on understanding how to process their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream essentially forming frames of events grouped according to their timestamp, we are still to exploit the power of these cameras.

In this spirit, this paper proposes a new, purely event-based corner detector and a novel corner tracker, demonstrating that it is possible to detect corners and track them directly on the event-stream in real-time. Evaluation on benchmarking datasets reveals a significant boost in the number of detected corners and the repeatability of such detections over the state-of-the art even in challenging scenarios with the proposed approach, while enabling more than a 4× speed-up when compared to the most efficient algorithm in the literature. The proposed pipeline detects and tracks corners at a rate of more than 7.5 million events per second, promising great impact in high-speed applications.

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