Recognition of Grasp Points for Clothes Manipulation under unconstrained Conditions

TL;DR

This paper presents a system for recognizing grasp points on clothes in RGB-D images, aiding domestic robots in handling garments by detecting key parts and wrinkles to determine optimal grasp locations.

Contribution

The system uniquely combines key part detection with wrinkle analysis using vessel enhancement filters for improved grasp point recognition on clothes.

Findings

Effective grasp point detection demonstrated on various clothing types.

System outperforms baseline methods in grasp accuracy.

Robustness shown under unconstrained, real-world conditions.

Abstract

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.