@inbook{0293ef0814224917a6e662762ed5a323,
title = "Rotation and translation invariant object recognition with a tactile sensor",
abstract = "In this paper a novel approach is proposed to recognise different objects invariant to their translation and rotation by utilising a tactile sensor attached to a robotic arm. As the sensor is small compared to the tested objects, the robot needs to access those objects multiple times at different positions and is prone to move or rotate them. This inevitably increases difficulty in object recognition during manipulations. To solve this problem, it is proposed to extract tactile translation and rotation invariant local features to represent objects; a dictionary of k words is therefore learned by κ-means unsupervised learning and a histogram codebook is then used to identify objects. The proposed system has been validated by classifying real objects with data from an off-the-shelf tactile sensor. The average overall accuracy of 91.2% has been achieved with only 10 touches and a dictionary size of 50 clusters.",
author = "Shan Luo and Wenxuan Mou and Min Li and Kaspar Althoefer and Hongbin Liu",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 13th IEEE SENSORS Conference, SENSORS 2014 ; Conference date: 02-11-2014 Through 05-11-2014",
year = "2014",
month = dec,
day = "12",
doi = "10.1109/ICSENS.2014.6985179",
language = "English",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "December",
pages = "1030--1033",
editor = "Arregui, {Francisco J.}",
booktitle = "IEEE SENSORS 2014, Proceedings",
address = "United States",
edition = "December",
}