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:Translated abstract:
向环境变化自适应机器人程序是工业领域一个公认的难题,也是中小型企业(SMEs)往往无法自动化执行繁琐任务的原因。利用点云创建机器人未知环境的语义化世界模型,是中小型企业自动化组装任务的一种方式。由于缺乏合适的数据集,工业机器人或协作机器人在工业环境下的点云语义分割鲜有研究。本文描述了一个具体用例的流程,用于生成特定用例的合成点云,并使用它们来训练点云语义分割模型。我们证明,使用我们的数据训练的模型,对未见过的真实数据进行点云语义分割时,每个类别的准确率大于90%。我们的方法不仅适用于用于训练数据生成的3D相机,还适用于基于不同技术的其他深度相机。本文测试的应用程序是与工业相关的插销孔拼装过程,利用我们的方法,机器人的启动和调试可以尽量减少对用户的帮助。