In-Hand Object Localization Using a Novel High-Resolution Visuotactile Sensor
Shaowei Cui, Rui Wang, Jingyi Hu, Junhang Wei, Shuo Wang, Zheng Lou
Abstract
In-hand object localization and manipulation has always been a challenging task in robotic community. In this article, we address this problem by vision-based tactile sensing with high-spatial resolution. Specifically, we design a novel tactile sensor based on stereo vision, named GelStereo, which can perceive tactile point cloud with high-spatial resolution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$<\!1$</tex-math></inline-formula> mm). A tactile-based in-hand object localization pipeline composed of saliency detection and probabilistic point-set registration algorithms of the perceived contact point cloud is presented. Furthermore, extensive qualitative and quantitative analyses of perceived tactile point cloud and in-hand localization and insertion experiments of small parts are performed on our robot platform. The experimental results verify the accuracy and robustness of the tactile point cloud sensed by the novel GelStereo tactile sensor and the proposed in-hand object localization pipeline. This novel high-resolution visuotactile sensing technology has predictable application potential in the field of dexterous robotic manipulation.