  Efficient and accurate 3D object shape reconstruction contributes
significantly to the success of a robot's physical interaction with its
environment. Acquiring accurate shape information about unknown objects is
challenging, especially in unstructured environments, e.g. the vision sensors
may only be able to provide a partial view. To address this issue, tactile
sensors could be employed to extract local surface information for more robust
unknown object shape estimation. In this paper, we propose a novel approach for
efficient unknown 3D object shape exploration and reconstruction using a
multi-fingered hand equipped with tactile sensors and a depth camera only
providing a partial view. We present a multi-finger sliding touch strategy for
efficient shape exploration using a Bayesian Optimization approach and a
single-leader-multi-follower strategy for multi-finger smooth local surface
perception. We evaluate our proposed method by estimating the 3D shape of
objects from the YCB and OCRTOC datasets based on simulation and real robot
experiments. The proposed approach yields successful reconstruction results
relying on only a few continuous sliding touches. Experimental results
demonstrate that our method is able to model unknown objects in an efficient
and accurate way.
