Inward-region-growing-based accurate partitioning of closely stacked objects for bin-picking
Inward-region-growing-based accurate partitioning of closely stacked objects for bin-picking
复制标题
基于向内区域生长的紧密堆叠对象的精确划分以进行分箱拣选
DOI：
10.1088/1361-6501/aba283
复制
发表时间：
2020
影响因子：
2.4
通讯作者：
Jianrong Tan
中科院分区：
文献类型：
--
作者：
Zaixing He;Hongyuan Wang;Xinyue Zhao;Shuyou Zhang;Jianrong Tan
对象分割是装箱中的常见任务。基于区域增长的方法已被证明适用于普通任务，但它们不适合紧密相邻和堆叠的场景。本文提出了一种基于向内区域生长的精确分区方法来进行垃圾箱拾取。提出了一种边界芽生成算法，用于检测区域生长中相邻物体的边界初始点。在此基础上，提出了一种简化的生长算法，即有向无约束生长算法，将生长方向限制在向内的方向，加快了生长过程。实验结果表明，该方法可以实现更高的精度和速度比现有的方法，特别是在紧密相邻的场景。
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.