Folding clothes is a current trend in robotics. Previously to folding clothes, they have to be unfolded. It is not realistic to perform model-based unfolding, as every garment has a different shape, size, color, texture, etc. In this paper we present a garment-agnostic algorithm to unfold clothes that works using 3D sensor information. The depth information provided by the sensor is converted into a grayscale image. This image is segmented using watershed algorithm. This algorithm provide us with labeled regions, each having a different height. In this labeled image, we assume that the highest height region belongs to the fold. Starting on this region, and ending in the garment border, tentative paths are created in several directions to analyze the height profile. For each profile, a bumpiness value is computed, and the lowest one is selected as the unfolding direction. A final extension on this line is performed to create a pick point on the fold border, and a place point outside the garment. The proposed algorithm is tested with a small set of clothes in different positions.
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