Clothes state recognition using 3D observed data

Authors:
Yasuyo Kita;Toshio Ueshiba;Ee Sian Neo;Nobuyuki Kita
Affiliations:
Information Technology Institute, National Institute of Advanced Industrial Science and Technology;Information Technology Institute, National Institute of Advanced Industrial Science and Technology;Intelligent Systems Institute, National Institute of Advanced Industrial Science and Technology;Intelligent Systems Institute, National Institute of Advanced Industrial Science and Technology
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

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Abstract

In this paper, we propose a deformable-model-driven method to recognize the state of hanging clothes using three-dimensional (3D) observed data. For the task to pick up a specific part of the clothes, it is indispensable to obtain the 3D position and posture of the part. In order to robustly obtain such information from 3D observed data of the clothes, we take a deformable-model-driven approach[4], that recognizes the clothes state by comparing the observed data with candidate shapes which are predicted in advance. To carry out this approach despite large shape variation of the clothes, we propose a two-staged method. First, small number of representative 3D shapes are calculated through physical simulations of hanging the clothes. Then, after observing clothes, each representative shape is deformed so as to fit the observed 3D data better. The consistency between the adjusted shapes and the observed data is checked to select the correct state. Experimental results using actual observations have shown the good prospect of the proposed method.