We present a method for performing object recognition using multiple images acquired from a tactile sensor. The method relies on using the tactile sensor as an imaging device, and builds an object representation based on mosaics of tactile measurements. We then describe an algorithm that is able to recognize an object using a small number of tactile sensor readings. Our approach makes extensive use of sequential state estimation techniques from the mobile robotics literature, whereby we view the object recognition problem as one of estimating a consistent location within a set of object maps. We examine and test approaches based on both traditional particle filtering and histogram filtering. We demonstrate both the mapping and recognition / localization techniques on a set of raised letter shapes using real tactile sensor data.

This paper explores the connection between sensor-based perception and exploration in the context of haptic object identification. The proposed approach combines 1) object recognition from tactile appearance with 2) purposeful haptic exploration of unknown objects to extract appearance information. The recognition component brings to bear computer-vision techniques by viewing tactile-sensor readings as images. We present a bag-of-features framework that uses several tactile-image descriptors, some that are adapted from the vision domain and others that are novel, to estimate a probability distribution over object identity as an unknown object is explored. Haptic exploration is treated as a search problem in a continuous space to take advantage of sampling-based motion planning to explore the unknown object and construct its tactile appearance. Simulation experiments of a robot arm equipped with a haptic sensor at the end-effector provide promising validation, thereby indicating high accuracy in identifying complex shapes from tactile information gathered during exploration. The proposed approach is also validated by using readings from actual tactile sensors to recognize real objects.