In this paper, we present a probabilistic spatial approach to build compact 3-D representations of unknown objects probed by tactile sensors. Our approach exploits the high frame rates provided by modern tactile sensors and utilizes Kalman filters to build a probabilistic model of the contact point cloud that is efficiently stored in a kd-tree. The quality of generated shape representations is compared with a naive averaging approach, and we show that our method provides superior accuracy. We also evaluate the feasibility of object classification combining the generated object representations, together with the iterative closest point algorithm.

We present a database of 2D pressure profile time series as a testbed for tactile object and surface recognition. The tactile database captures the surfaces of household and toy objects by moving a 2D pressure sensor mounted to an industrial robot arm around the objects using real-time trajectory calculation. Thus, it represents different "views" of the objects in a similar way as the well known Columbia Object Image Library (COIL) captures different views of an object by a camera. As a first application, objects in the database are classified using a neural network architecture.

We propose a neural architecture for the recognition of objects by haptics. We demonstrate its performance for a set of household objects and toys using a low cost 2D pressure sensor of coarse resolution, which is moved by a robot arm guided by contact points. The approach transfers the well known view-based method from computer vision to the domain of tactile sensing. However, in contrast to computer vision, not static frames but entire time series of 2D pressure profiles are evaluated.