This paper proposes a hierarchical visual architecture for perceiving garments’ configuration independently from their type for the robotic unfolding task. Special focus is given on the decomposition of folded configurations into low- and high-level features. The low-level features comprise junctions of edges, which act as localized indicators of the clothing article’s state, while the high-level components refer to its layers and the axis that unites them. The proposed methodology extracts and classifies the low-level components into indicators of folds, overlaps, garment’s edges and corners and through their combination reconstructs the axis and the layers of the garment. The methodology is independent from the garment’s shape while it uses depth sensors so that it can deal with garments of various colours, patterns and decorative features. Experiments showed the effectiveness of the method in scenarios with onefold or twofold and in different datasets, proving the extensibility of the approach.
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Triantafyllou, D., Koustoumpardis, P. & Aspragathos, N. Type independent hierarchical analysis for the recognition of folded garments’ configuration. Intel Serv Robotics (2021). https://doi.org/10.1007/s11370-021-00365-8
- Layer detection
- Axis detection
- Depth images