  A key proficiency an autonomous mobile robot must have to perform high-level
tasks is a strong understanding of its environment. This involves information
about what types of objects are present, where they are, what their spatial
extend is, and how they can be reached, i.e., information about free space is
also crucial. Semantic maps are a powerful instrument providing such
information. However, applying semantic segmentation and building 3D maps with
high spatial resolution is challenging given limited resources on mobile
robots. In this paper, we incorporate semantic information into efficient
occupancy normal distribution transform (NDT) maps to enable real-time semantic
mapping on mobile robots. On the publicly available dataset Hypersim, we show
that, due to their sub-voxel accuracy, semantic NDT maps are superior to other
approaches. We compare them to the recent state-of-the-art approach based on
voxels and semantic Bayesian spatial kernel inference~(S-BKI) and to an
optimized version of it derived in this paper. The proposed semantic NDT maps
can represent semantics to the same level of detail, while mapping is 2.7 to
17.5 times faster. For the same grid resolution, they perform significantly
better, while mapping is up to more than 5 times faster. Finally, we prove the
real-world applicability of semantic NDT maps with qualitative results in a
domestic application.
