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There are a wide range of features that tactile contact provides, each withdifferent aspects of information that can be used for object grasping,manipulation, and perception. In this paper inference of some key tactilefeatures, tip displacement, contact location, shear direction and magnitude, isdemonstrated by introducing a novel method of transducing a third dimension tothe sensor data via Voronoi tessellation. The inferred features are displayedthroughout the work in a new visualisation mode derived from the Voronoitessellation; these visualisations create easier interpretation of data from anoptical tactile sensor that measures local shear from displacement of internalpins (the TacTip). The output values of tip displacement and shear magnitudeare calibrated to appropriate mechanical units and validate the direction ofshear inferred from the sensor. We show that these methods can infer thedirection of shear to $\sim$2.3$^{\circ}$ without the need for training aclassifier or regressor. The approach demonstrated here will increase theversatility and generality of the sensors and thus allow sensor to be used inmore unstructured and unknown environments, as well as improve the use of thesetactile sensors in more complex systems such as robot hands.

Voronoi Features for Tactile Sensing: Direct Inference of Pressure, Shear, and Contact Locations