论文标题
量角：致密形状重建的光学触觉传感器
DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction
论文作者
论文摘要
在机器人中提高触觉传感的性能可以实现多功能，手持操作。基于视觉的触觉传感器已被广泛使用，因为已证明丰富的触觉反馈与操纵任务的性能提高相关。具有高分辨率的现有触觉传感器解决方案具有限制，包括较低的精度，昂贵的组件或缺乏可扩展性。在本文中，提出了一种廉价，可扩展和紧凑的触觉传感器，其具有高分辨率的表面变形模型，用于3D传感器表面的表面重建。通过测量Fisheye摄像机的图像，可以通过使用深卷积神经网络实时（1.8ms）成功估计表面变形。该传感器的设计和感应能力是朝着更好的对象本地化，分类和表面估计迈出的重要一步，这都是由高分辨率形状重建实现的。
Increasing the performance of tactile sensing in robots enables versatile, in-hand manipulation. Vision-based tactile sensors have been widely used as rich tactile feedback has been shown to be correlated with increased performance in manipulation tasks. Existing tactile sensor solutions with high resolution have limitations that include low accuracy, expensive components, or lack of scalability. In this paper, an inexpensive, scalable, and compact tactile sensor with high-resolution surface deformation modeling for surface reconstruction of the 3D sensor surface is proposed. By measuring the image from the fisheye camera, it is shown that the sensor can successfully estimate the surface deformation in real-time (1.8ms) by using deep convolutional neural networks. This sensor in its design and sensing abilities represents a significant step toward better object in-hand localization, classification, and surface estimation all enabled by high-resolution shape reconstruction.
