The work addresses the problem of clothing perception and manipulation by a two armed industrial robot aiming at a real-time automated folding of a piece of garment spread out on a flat surface. A complete solution combining vision sensing, garment segmentation and understanding, planning of the manipulation and its real execution on a robot is proposed. A new polygonal model of a garment is introduced. Fitting the model into a segmented garment contour is used to detect garment landmark points. It is shown how folded variants of the unfolded model can be derived automatically. Universality and usefulness of the model is demonstrated by its favorable performance within the whole folding procedure which is applicable to a variety of garments categories (towel, pants, shirt, etc.) and evaluated experimentally using the two armed robot. The principal novelty with respect to the state of the art is in the new garment polygonal model and its manipulation planning algorithm which leads to the speed up by two orders of magnitude.
We present our recent model of a diagram recognition engine. It extends our previous work which approaches the structural recognition as an optimization problem of choosing the best subset of symbol candidates. The main improvement is the integration of our own text separator into the pipeline to deal with text blocks occurring in diagrams. Second improvement is splitting the symbol candidates detection into two stages: uniform symbols detection and arrows detection. Text recognition is left for postprocessing when the diagram structure is already known. Training and testing of the engine was done on a freely available benchmark database of flowcharts. We correctly segmented and recognized 93.0 % of the symbols having 55.1 % of the diagrams recognized without any error. Considering correct stroke labeling, we achieved the precision of 95.7 %. This result is superior to the state-of-the-art method with the precision of 92.4 %. Additionally, we demonstrate the generality of the proposed method by adapting the system to finite automata domain and evaluating it on own database of such diagrams.
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