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Knowledge of 3-D object shape is of great importance to robot manipulationtasks, but may not be readily available in unstructured environments. Whilevision is often occluded during robot-object interaction, high-resolutiontactile sensors can give a dense local perspective of the object. However,tactile sensors have limited sensing area and the shape representation mustfaithfully approximate non-contact areas. In addition, a key challenge isefficiently incorporating these dense tactile measurements into a 3-D mappingframework. In this work, we propose an incremental shape mapping method using aGelSight tactile sensor and a depth camera. Local shape is recovered fromtactile images via a learned model trained in simulation. Through efficientinference on a spatial factor graph informed by a Gaussian process, we build animplicit surface representation of the object. We demonstrate visuo-tactilemapping in both simulated and real-world experiments, to incrementally build3-D reconstructions of household objects.

ShapeMap 3-D: Efficient shape mapping through dense touch and vision