  Reliable and accurate localization and mapping are key components of most
autonomous systems. Besides geometric information about the mapped environment,
the semantics plays an important role to enable intelligent navigation
behaviors. In most realistic environments, this task is particularly
complicated due to dynamics caused by moving objects, which can corrupt the
mapping step or derail localization. In this paper, we propose an extension of
a recently published surfel-based mapping approach exploiting three-dimensional
laser range scans by integrating semantic information to facilitate the mapping
process. The semantic information is efficiently extracted by a fully
convolutional neural network and rendered on a spherical projection of the
laser range data. This computed semantic segmentation results in point-wise
labels for the whole scan, allowing us to build a semantically-enriched map
with labeled surfels. This semantic map enables us to reliably filter moving
objects, but also improve the projective scan matching via semantic
constraints. Our experimental evaluation on challenging highways sequences from
KITTI dataset with very few static structures and a large amount of moving cars
shows the advantage of our semantic SLAM approach in comparison to a purely
geometric, state-of-the-art approach.
