Institut de Robòtica
i Informàtica Industrial

Using Depth and Appearance Features for Informed Robot Grasping of Highly Wrinkled Clothes

A. Ramisa, G. Alenya, F. Moreno-Noguer and C. Torras

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 (SIFT) and 3D geometry features (Geodesic-Depth Histogram, GDH). 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 detection 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.