Current approaches for robotic garment folding require a full view of an
extended garment, in order to successfully apply a model-based folding
sequence. In this paper, we present a garment-agnostic algorithm that
requires no model to unfold clothes and works using only depth data.
Once the garment is unfolded, state of the art approaches for folding
may be applied. The algorithm presented is divided into 3 main stages.
First, a Segmentation stage extracts the garment data from the
background, and approximates its contour into a polygon. Then, a
Clustering stage groups regions of similar height within the garment,
corresponding to different overlapped regions. Finally, a Pick and Place
Points stage finds the most suitable points for grasping and releasing
the garment for the unfolding process, based on a bumpiness value
defined as the accumulated difference in height along selected candidate
paths. Experiments for evaluation of the vision algorithm have been
performed over a dataset of 30 samples from a total of 6 different
garment categories with one and two folds. The whole unfolding algorithm
has also been validated through experiments with an industrial robot
platform over a subset of the dataset garments.