  Simultaneous localization and mapping (SLAM), i.e., the reconstruction of the
environment represented by a (3D) map and the concurrent pose estimation, has
made astonishing progress. Meanwhile, large scale applications aiming at the
data collection in complex environments like factory halls or construction
sites are becoming feasible. However, in contrast to small scale scenarios with
building interiors separated to single rooms, shop floors or construction areas
require measures at larger distances in potentially texture less areas under
difficult illumination. Pose estimation is further aggravated since no GNSS
measures are available as it is usual for such indoor applications. In our
work, we realize data collection in a large factory hall by a robot system
equipped with four stereo cameras as well as a 3D laser scanner. We apply our
state-of-the-art LiDAR and visual SLAM approaches and discuss the respective
pros and cons of the different sensor types for trajectory estimation and dense
map generation in such an environment. Additionally, dense and accurate depth
maps are generated by 3D Gaussian splatting, which we plan to use in the
context of our project aiming on the automatic construction and site
monitoring.
