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Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/55915
Title: 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
URI: http://hdl.handle.net/10261/55915
ISSN: 1050-4729
DOI: 10.1109/ICRA.2012.6225045
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