  Many modern simultaneous localization and mapping (SLAM) techniques rely on
sparse landmark-based maps due to their real-time performance. However, these
techniques frequently assert that these landmarks are fixed in position over
time, known as the static-world assumption. This is rarely, if ever, the case
in most real-world environments. Even worse, over long deployments, robots are
bound to observe traditionally static landmarks change, for example when an
autonomous vehicle encounters a construction zone. This work addresses this
challenge, accounting for changes in complex three-dimensional environments
with the creation of a probabilistic filter that operates on the features that
give rise to landmarks. To accomplish this, landmarks are clustered into
cliques and a filter is developed to estimate their persistence jointly among
observations of the landmarks in a clique. This filter uses estimated
spatial-temporal priors of geometric objects, allowing for dynamic and
semi-static objects to be removed from a formally static map. The proposed
algorithm is validated in a 3D simulated environment.
