  Highly automated driving functions currently often rely on a-priori knowledge
from maps for planning and prediction in complex scenarios like cities. This
makes map-relative localization an essential skill. In this paper, we address
the problem of localization with automotive-grade radars, using a real-time
graph-based SLAM approach. The system uses landmarks and odometry information
as an abstraction layer. This way, besides radars, all kind of different sensor
modalities including cameras and lidars can contribute. A single, semantic
landmark map is used and maintained for all sensors. We implemented our
approach using C++ and thoroughly tested it on data obtained with our test
vehicles, comprising cars and trucks. Test scenarios include inner cities and
industrial areas like container terminals. The experiments presented in this
paper suggest that the approach is able to provide a precise and stable pose in
structured environments, using radar data alone. The fusion of additional
sensor information from cameras or lidars further boost performance, providing
reliable semantic information needed for automated mapping.
