An Active Approach to Solving the Stereo Matching Problem using Event-Based Sensors
An Active Approach to Solving the Stereo Matching Problem using Event-Based Sensors
复制标题
使用基于事件的传感器解决立体匹配问题的主动方法
DOI：
10.1109/iscas.2018.8351411
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Yulia Sandamirskaya
中科院分区：
文献类型：
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作者：
Julien N. P. Martel;Jonathan Müller;J. Conradt;Yulia Sandamirskaya
推断从视觉传感器到场景中物体的距离的问题——称为深度估计——可以用各种方法解决。其中，立体视觉是两个传感器从不同视点观察同一场景的一种方法。为了恢复一个点的三维坐标，它的两个投影——每个视图一个——可以用于三角测量。但是，必须首先找到两个视图中相互对应的一对点。这就是所谓的立体匹配，通常是一个计算成本很高的操作。传统上，这是通过在第一个视图中使用其周围的一些信息（例如在特征向量中）来描述一个点，然后在另一个视图中搜索与以类似方式描述的点的匹配来实现的。在这项工作中，我们提出了一个简单的想法，以缓解这种立体匹配问题，使用一个有源组件：镜面振镜驱动激光器。激光束通过驱动两个镜子来偏转，从而在场景中产生一系列的“光点”。在这些点，对比变化很快。我们通过两个动态视觉传感器（DVS）捕捉这些对比度变化。这些传感器的高时间分辨率使其能够及时检测激光诱导事件并使用轻量级计算进行匹配。该方法实现了基于事件的深度估计，速度快，计算成本低，并且不需要精确的传感器同步。
The problem of inferring distances from a visual sensor to objects in a scene — referred to as depth estimation — can be solved in various ways. Among those, stereo vision is a method in which two sensors observe the same scene from different viewpoints. To recover the three-dimensional coordinates of a point, its two projections — one in each view — can be used for triangulation. However, the pair of points in the two views that correspond to each other has to be found first. This is known as stereo-matching and is usually a computationally expensive operation. Traditionally, this is performed by describing a point in the first view with some information from its surrounding, e.g. in a feature vector, and then searching for a match with a point described in a similar way in the other view. In this work, we propose a simple idea that alleviates this stereo-matching problem using an active component: a mirror-galvanometer driven laser. The laser beam is deflected by actuating two mirrors, thus creating a sequence of "light spots" in the scene. At these spots, contrast changes quickly. We capture those contrast changes by two Dynamic Vision Sensors (DVS). The high time-resolution of these sensors enables the detection of the laser-induced events in time and their matching using lightweight computation. This method enables event-based depth estimation at a high speed, low computational cost, and without exact sensor synchronization.