Adapting robot programmes to changes in the environment is a well-known industry problem, and it is the reason why many tedious tasks are not automated in small and medium-sized enterprises (SMEs). A semantic world model of a robot's previously unknown environment created from point clouds is one way for these companies to automate assembly tasks that are typically performed by humans. The semantic segmentation of point clouds for robot manipulators or cobots in industrial environments has received little attention due to a lack of suitable datasets. This paper describes a pipeline for creating synthetic point clouds for specific use cases in order to train a model for point cloud semantic segmentation. We show that models trained with our data achieve high per-class accuracy (> 90%) for semantic point cloud segmentation on unseen real-world data. Our approach is applicable not only to the 3D camera used in training data generation but also to other depth cameras based on different technologies. The application tested in this work is a industry-related peg-in-the-hole process. With our approach the necessity of user assistance during a robot's commissioning can be reduced to a minimum.

翻译：适应环境变化是工业领域中的经典难题，也是中小型企业中许多繁琐任务未能实现自动化的原因。基于机器人未知环境点云构建的语义世界模型，为这些企业实现通常由人类完成的装配任务自动化提供了可行方案。由于缺乏合适的数据集，面向工业环境中机器人操作器或协作机器人的点云语义分割研究鲜有关注。本文提出一种针对特定应用场景生成合成点云的流水线，用于训练点云语义分割模型。实验表明，使用我们的数据训练的模型在未见过的真实世界数据上，能达到每类超过90%的高准确率语义点云分割。该方法不仅适用于生成训练数据时使用的3D摄像头，还可推广至基于不同技术的其他深度摄像头。本研究测试的应用场景为工业相关的轴孔装配工艺。采用本方法，可最大限度减少机器人调试过程中人工辅助的必要性。