  Tactile perception is central to robot manipulation in unstructured
environments. However, it requires contact, and a mature implementation must
infer object models while also accounting for the motion induced by the
interaction. In this work, we present a method to estimate both object shape
and pose in real-time from a stream of tactile measurements. This is applied
towards tactile exploration of an unknown object by planar pushing. We consider
this as an online SLAM problem with a nonparametric shape representation. Our
formulation of tactile inference alternates between Gaussian process implicit
surface regression and pose estimation on a factor graph. Through a combination
of local Gaussian processes and fixed-lag smoothing, we infer object shape and
pose in real-time. We evaluate our system across different objects in both
simulated and real-world planar pushing tasks.
