  Knowledge of 3-D object shape is of great importance to robot manipulation
tasks, but may not be readily available in unstructured environments. While
vision is often occluded during robot-object interaction, high-resolution
tactile sensors can give a dense local perspective of the object. However,
tactile sensors have limited sensing area and the shape representation must
faithfully approximate non-contact areas. In addition, a key challenge is
efficiently incorporating these dense tactile measurements into a 3-D mapping
framework. In this work, we propose an incremental shape mapping method using a
GelSight tactile sensor and a depth camera. Local shape is recovered from
tactile images via a learned model trained in simulation. Through efficient
inference on a spatial factor graph informed by a Gaussian process, we build an
implicit surface representation of the object. We demonstrate visuo-tactile
mapping in both simulated and real-world experiments, to incrementally build
3-D reconstructions of household objects.
