  Inspired by humans' ability to perceive the surface texture of unfamiliar
objects without relying on vision, the sense of touch can play a crucial role
in robots exploring the environment, particularly in scenes where vision is
difficult to apply, or occlusion is inevitable. Existing tactile surface
reconstruction methods rely on external sensors or have strong prior
assumptions, making the operation complex and limiting their application
scenarios. This paper presents a framework for low-drift surface reconstruction
through multiple tactile measurements, Tac2Structure. Compared with existing
algorithms, the proposed method uses only a new vision-based tactile sensor
without relying on external devices. Aiming at the difficulty that
reconstruction accuracy is easily affected by the pressure at contact, we
propose a correction algorithm to adapt it. The proposed method also reduces
the accumulative errors that occur easily during global object surface
reconstruction. Multi-frame tactile measurements can accurately reconstruct
object surfaces by jointly using the point cloud registration algorithm,
loop-closure detection algorithm based on deep learning, and pose graph
optimization algorithm. Experiments verify that Tac2Structure can achieve
millimeter-level accuracy in reconstructing the surface of objects, providing
accurate tactile information for the robot to perceive the surrounding
environment.
