关键词: 3D点云, 实例分割, 箱体拆垛, 中心预测, 聚类

Abstract: With the extensive deployment of deep learning technology in industry, automated systems applied in transportation, loading and unloading, packaging, sorting and other links have become a research hotspot in warehousing and logistics industry. Aiming at robot box unstacking scene, a point cloud center prediction-clustering network (CPCN) is proposed based on the deep learning method, which can segment the box stack and calculate the center coordinates of the upper surface of each box. Based on the traditional semantic-instance joint segmentation structure, CPCN designs a central prediction module and a central reinforcement module for the instance segmentation branch. The central prediction module avoids the error of central point segmentation by directly locating the instance center, and the central reinforcement module makes the points belonging to the same instance converge to the center in the feature space, both of which effectively enhance the identification ability of the instance features. In addition, the central-instance clustering method designed in the part of instance feature processing calculates the instance label by directly measuring the distance of the instance feature, which greatly reduces the computing time. Experiments on the box data set show that compared with the existing methods, the average accuracy of CPCN is improved by 0.7 percentage points at the lowest and 17.2 percentage points at the highest, the accuracy of instance center reaches 94.4%, the center offset is as low as 13.70?mm, and the reasoning speed is faster than that of the same type of joint division network. CPCN is more targeted for the box instance segmentation and has good application value.

Key words: 3D point cloud, instance segmentation, box unstacking, center prediction, clustering