  Many robotics applications require interest points that are highly repeatable
under varying viewpoints and lighting conditions. However, this requirement is
very challenging as the environment changes continuously and indefinitely,
leading to appearance changes of interest points with respect to time. This
paper proposes to predict the repeatability of an interest point as a function
of time, which can tell us the lifespan of the interest point considering daily
or seasonal variation. The repeatability predictor (RP) is formulated as a
regressor trained on repeated interest points from multiple viewpoints over a
long period of time. Through comprehensive experiments, we demonstrate that our
RP can estimate when a new interest point is repeated, and also highlight an
insightful analysis about this problem. For further comparison, we apply our RP
to the map summarization under visual localization framework, which builds a
compact representation of the full context map given the query time. The
experimental result shows a careful selection of potentially repeatable
interest points predicted by our RP can significantly mitigate the degeneration
of localization accuracy from map summarization.
