Vision-based tactile sensors equipped with planar contact structures acquire the shape, force, and motion states of objects in contact. The limited planar contact area presents a challenge in acquiring information about larger target objects. In contrast, vision-based tactile sensors with cylindrical contact structures could extend the contact area by rolling, which can acquire much tactile information that exceeds the sensing projection area in a single contact. However, the tactile data acquired by cylindrical structures does not consistently correspond to the same depth level. Therefore, stitching and analyzing the data in an extended contact area is a challenging problem. In this work, we propose an image fusion method based on cylindrical vision-based tactile sensors. The method takes advantage of the changing characteristics of the contact depth of cylindrical structures, extracts the effective information of different contact depths in the frequency domain, and performs differential fusion for the information characteristics. The results show that in object contact confronting an area larger than single sensing, the images fused with our proposed method have higher information and structural similarity compared with the method of stitching based on motion distance sampling. Meanwhile, it is robust to sampling time. We complement this method with a deep neural network to illustrate its potential for fusing and recognizing object contact information using cylindrical vision-based tactile sensors.

翻译：配备平面接触结构的视觉触觉传感器可获取接触物体的形状、力及运动状态。然而，有限的平面接触面积对获取更大目标物体的信息构成挑战。相比之下，具有圆柱形接触结构的视觉触觉传感器可通过滚动扩展接触面积，在单次接触中获取超出传感投影面积的丰富触觉信息。但圆柱形结构获取的触觉数据并不始终对应同一深度层级，因此在扩展接触区域内拼接与分析数据是一项具有挑战性的问题。本文提出一种基于圆柱形视觉触觉传感器的图像融合方法。该方法利用圆柱形结构接触深度变化的特性，在频域中提取不同接触深度的有效信息，并针对信息特征进行差异化融合。结果表明，在接触面积大于单次传感范围的目标物体时，与基于运动距离采样的拼接方法相比，采用本文方法融合的图像具有更高的信息量和结构相似性，同时该方法对采样时间具有鲁棒性。我们通过深度神经网络对该方法进行了补充，展示了其在圆柱形视觉触觉传感器中融合与识别物体接触信息的潜力。