  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.
