  In search and rescue missions, time is an important factor; fast navigation
and quickly acquiring situation awareness might be matters of life and death.
Hence, the use of robots in such scenarios has been restricted by the time
needed to explore and build a map. One way to speed up exploration and mapping
is to reason about unknown parts of the environment using prior information.
While previous research on using external priors for robot mapping mainly
focused on accurate maps or aerial images, such data are not always possible to
get, especially indoor. We focus on emergency maps as priors for robot mapping
since they are easy to get and already extensively used by firemen in rescue
missions. However, those maps can be outdated, information might be missing,
and the scales of rooms are typically not consistent. We have developed a
formulation of graph-based SLAM that incorporates information from an emergency
map. The graph-SLAM is optimized using a combination of robust kernels, fusing
the emergency map and the robot map into one map, even when faced with scale
inaccuracies and inexact start poses. We typically have more than 50% of wrong
correspondences in the settings studied in this paper, and the method we
propose correctly handles them. Experiments in an office environment show that
we can handle up to 70% of wrong correspondences and still get the expected
result. The robot can navigate and explore while taking into account places it
has not yet seen. We demonstrate this in a test scenario and also show that the
emergency map is enhanced by adding information not represented such as closed
doors or new walls.
