  To operate in an urban environment, an automated vehicle must be capable of
accurately estimating its position within a global map reference frame. This is
necessary for optimal path planning and safe navigation. To accomplish this
over an extended period of time, the global map requires long-term maintenance.
This includes the addition of newly observable features and the removal of
transient features belonging to dynamic objects. The latter is especially
important for the long-term use of the map as matching against a map with
features that no longer exist can result in incorrect data associations, and
consequently erroneous localisation. This paper addresses the problem of
removing features from the map that correspond to objects that are no longer
observable/present in the environment. This is achieved by assigning a single
score which depends on the geometric distribution and characteristics when the
features are re-detected (or not) on different occasions. Our approach not only
eliminates ephemeral features, but also can be used as a reduction algorithm
for highly dense maps. We tested our approach using half a year of weekly
drives over the same 500-metre section of road in an urban environment. The
results presented demonstrate the validity of the long-term approach to map
maintenance.
