Point Based Deep Learning to Automate Automotive Assembly Simulation Model Generation with Respect to the Digital Factory

Abstract

One major challenge towards a fully digital factory is the understanding of complex and dynamic indoor scenes. This knowledge is needed to determine the as-is state in production plants and to set up factory simulations. While in recent years laser scanning and photogrammetry techniques have facilitated the digitalization of factory environments, the actual simulation model generation is to the greatest possible extent still manual work. In this paper we propose a cross-industry simulation model generation framework that takes in raw point clouds from laser scanners and photogrammetry and outputs a simulation model. We present a deep learning architecture based on PointNet [1], which is capable of semantic scene segmentation, and integrate a CAD model placement routine based on unsupervised learning and point cloud registration. We prove the feasibility of our framework by collecting a scan and photogrammetry dataset at a German automotive assembly plant and applying our framework.