Integrating Point Spread Function Into Taxel-Based Tactile Pattern Super Resolution

Abstract

The past decade has witnessed the development of tactile sensors, which have been increasingly considered as an essential equipment in robotics, especially the dexterous manipulation and collaborative human-robot interactions. There are two major types of tactile sensors, i.e., the vision-based and taxel-based sensors. The latter is capable of achieving lower integration complexity with existing robotic systems, but unable to provide high-resolution (HR) tactile information as that of the vision-based counterpart due to the manufacturing limitations. Therefore, we propose a novel tactile pattern super-resolution (SR) scheme for taxel-based sensors, which is a data-driven scheme enabling customized selection on the number of applied "tapping" actions to achieve improvable performance from single tapping SR (STSR) to the multi-tapping SR (MTSR). In addition, we develop a new dataset for the proposed tactile SR scheme. In order to obtain scalable resolutions (e.g. ×4, ×10, ×20, etc.) of ground-truth HR tactile patterns, we propose a novel tactile point spread function (PSF) scheme to generate HR tactile patterns by leveraging the low-resolution (LR) data gathered directly from the taxel-based sensor and the depth information of contact surfaces. This is in strong contrast to the conventional ground-truth generation approach with overlapped multi-sampling and registration strategy, which can only provide a fixed resolution. Experimental results confirm the efficiency of the proposed scheme.