  Distances to compact sets are widely used in the field of Topological Data
Analysis for inferring geometric and topological features from point clouds. In
this context, the distance to a probability measure (DTM) has been introduced
by Chazal et al. (2011) as a robust alternative to the distance a compact set.
In practice, the DTM can be estimated by its empirical counterpart, that is the
distance to the empirical measure (DTEM). In this paper we give a tight control
of the deviation of the DTEM. Our analysis relies on a local analysis of
empirical processes. In particular, we show that the rates of convergence of
the DTEM directly depends on the regularity at zero of a particular quantile
fonction which contains some local information about the geometry of the
support. This quantile function is the relevant quantity to describe precisely
how difficult is a geometric inference problem. Several numerical experiments
illustrate the convergence of the DTEM and also confirm that our bounds are
tight.
