Model-Based Segmentation-Supported Camera Tracking in Fab’s Indoor Environments

Currently, it is a norm to design a semiconductor fab using building information models (BIMs), which refer to a digital representation of a building’s physical and functional characteristics. The comprehensive data provided by BIMs include 3D geometric models. This paper presents a 3D model-based camera tracking method, which is targeted at navigating a fab’s wide indoor environment. The key observation made in designing the method is that there are a number of fixed objects in such an indoor environment. The columns are the representative among them. Our method extracts the columns from the input image and matches them to their BIMs to estimate the camera pose. The estimation accuracy is significantly increased by adopting an instance segmentation network. It is trained with a dataset, which is extracted from the target indoor environment and processed by our own data engine. The test results show that our tracking method is drift-free, accurate and robust. We envision that it can be used in many applications such as AR-based visual inspection.