  Ground texture based localization is a promising approach to achieve
high-accuracy positioning of vehicles. We present a self-contained method that
can be used for global localization as well as for subsequent local
localization updates, i.e. it allows a robot to localize without any knowledge
of its current whereabouts, but it can also take advantage of a prior pose
estimate to reduce computation time significantly. Our method is based on a
novel matching strategy, which we call identity matching, that is based on
compact binary feature descriptors. Identity matching treats pairs of features
as matches only if their descriptors are identical. While other methods for
global localization are faster to compute, our method reaches higher
localization success rates, and can switch to local localization after the
initial localization.
