Recognition of Grasp Points for Clothes Manipulation under unconstrained Conditions
This addresses the challenge of domestic robots handling clothes in random positions, which is an incremental improvement in robotic manipulation.
The paper tackles the problem of recognizing grasp points for clothes manipulation by domestic robots under unconstrained conditions, proposing a system that detects key clothing parts and wrinkles to identify grasp points, validated with realistic RGB-D images of various cloth types.
In this work a system for recognizing grasp points in RGB-D images is proposed. This system is intended to be used by a domestic robot when deploying clothes lying at a random position on a table. By taking into consideration that the grasp points are usually near key parts of clothing, such as the waist of pants or the neck of a shirt. The proposed system attempts to detect these key parts first, using a local multivariate contour that adapts its shape accordingly. Then, the proposed system applies the Vessel Enhancement filter to identify wrinkles in the clothes, allowing to compute a roughness index for the clothes. Finally, by mixing (i) the key part contours and (ii) the roughness information obtained by the vessel filter, the system is able to recognize grasp points for unfolding a piece of clothing. The recognition system is validated using realistic RGB-D images of different cloth types.