  The 3D modelling of indoor environments and the generation of process
simulations play an important role in factory and assembly planning. In
brownfield planning cases existing data are often outdated and incomplete
especially for older plants, which were mostly planned in 2D. Thus, current
environment models cannot be generated directly on the basis of existing data
and a holistic approach on how to build such a factory model in a highly
automated fashion is mostly non-existent. Major steps in generating an
environment model in a production plant include data collection and
pre-processing, object identification as well as pose estimation. In this work,
we elaborate a methodical workflow, which starts with the digitalization of
large-scale indoor environments and ends with the generation of a static
environment or simulation model. The object identification step is realized
using a Bayesian neural network capable of point cloud segmentation. We
elaborate how the information on network uncertainty generated by a Bayesian
segmentation framework can be used in order to build up a more accurate
environment model. The steps of data collection and point cloud segmentation as
well as the resulting model accuracy are evaluated on a real-world data set
collected at the assembly line of a large-scale automotive production plant.
The segmentation network is further evaluated on the publicly available
Stanford Large-Scale 3D Indoor Spaces data set. The Bayesian segmentation
network clearly surpasses the performance of the frequentist baseline and
allows us to increase the accuracy of the model placement in a simulation scene
considerably.
