Digital.CSIC > Ciencia y Tecnologías Físicas > Instituto de Robótica e Informática Industrial (IRII) > (IRII) Libros y partes de libros >




Open Access item Using depth and appearance features for informed robot grasping of highly wrinkled clothes

Authors:Ramisa, Arnau
Alenyà, Guillem
Moreno-Noguer, F.
Torras, Carme
Keywords:Feature extraction, Object detection, Robot vision
Issue Date:May-2012
Publisher:Institute of Electrical and Electronics Engineers
Citation:IEEE International Conference on Robotics and Automation: 1703-1708 (2012)
Abstract:Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple re-grasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, we circumvent the need for multiple re-graspings by building a robust detector that identifies the grasping points, generally in one single step, even when clothes are highly wrinkled. In order to handle the large variability a deformed cloth may have, we build a Bag of Features based detector that combines appearance and 3D geometry features. An image is scanned using a sliding window with a linear classifier, and the candidate windows are refined using a non-linear SVM and a “grasp goodness” criterion to select the best grasping point. We demonstrate our approach detecting collars in deformed polo shirts, using a Kinect camera. Experimental results show a good performance of the proposed method not only in identifying the same trained textile object part under severe deformations and occlusions, but also the corresponding part in other clothes, exhibiting a degree of generalization.
Description:Trabajo presentado al ICRA celebrado en Minnesota del 14 al 18 de mayo 2012.
Publisher version (URL):http://dx.doi.org/10.1109/ICRA.2012.6225045
Appears in Collections:(IRII) Libros y partes de libros

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.