Inward-region-growing-based accurate partitioning of closely stacked objects for bin-picking
Zaixing He, Hongyuan Wang, Xinyue Zhao, Shuyou Zhang, Jianrong Tan · Measurement Science and Technology · 2020
Abstract Object segmentation is a common task in bin-picking. Region-growing-based methods have been proven to be applicable for ordinary tasks, but they are not suitable for closely adjacent and stacked scenes. In this paper, we propose an inward-region-growing-based accurate partitioning method for bin-picking. A boundary bud generation algorithm is proposed for detecting the boundary initial points of closely adjacent objects for region growing. Then, a simplified growing algorithm, namely, the oriented unrestrained growing algorithm, is proposed for limiting the growing direction to the inward direction and accelerating the growing process. These experimental results demonstrate that the proposed method can achieve higher accuracy and speed than existing methods, especially in closely adjacent scenes.