Accurate garment surface analysis using an active stereo robot head with application to dual-arm flattening

Abstract

We present a visually guided, dual-arm, industrial robot system that is capable of autonomously flattening garments by means of a novel visual perception pipeline that fully interprets high-quality RGB-D images of a clothing scene based on an active stereo robot head. A segmented clothing range map is B-Spline smoothed prior to being parsed by means of shape and topology analysis into ‘wrinkle’ structures. The length, width and height of each wrinkle is used to quantify the topology of each wrinkle and thereby rank wrinkles by size such that a greedy algorithm can identify the largest wrinkle present. A flattening plan optimised for the largest detected wrinkle is formulated based on dual-arm manipulation. We report the validation of our autonomous flattening behaviour and observe that dual-arm flattening requires significantly fewer manipulation iterations than single-arm flattening. Our experimental results also reveal that the flattening process is heavily influenced by the quality of the RGB-D sensor: use of a custom off-the-shelf high-resolution stereo-based sensor system outperformed a commercial low-resolution kinect-like camera in terms of required flattening iterations.