  Accurate maps are a prerequisite for virtually all autonomous vehicle tasks.
Most state-of-the-art maps assume a static world, and therefore dynamic objects
are filtered out of the measurements. However, this division ignores movable
but non-moving, i.e. semi-static, objects, which are usually recorded in the
map and treated as static objects, violating the static world assumption,
causing error in the localization. In this paper, we present a method for
modeling moving and movable objects for matching the map and the measurements
consistently. This reduces the error resulting from inconsistent categorization
and treatment of non-static measurements. A semantic segmentation network is
used to categorize the measurements into static and semi-static classes, and a
background subtraction-based filtering method is used to remove dynamic
measurements. Experimental comparison against a state-of-the-art baseline
solution using real-world data from Oxford Radar RobotCar data set shows that
consistent assumptions over dynamics increase localization accuracy.
