  In this paper, we outline an interleaved acting and planning technique to
rapidly reduce the uncertainty of the estimated robot's pose by perceiving
relevant information from the environment, as recognizing an object or asking
someone for a direction.
  Generally, existing localization approaches rely on low-level geometric
features such as points, lines, and planes, while these approaches provide the
desired accuracy, they may require time to converge, especially with incorrect
initial guesses. In our approach, a task planner computes a sequence of action
and perception tasks to actively obtain relevant information from the robot's
perception system. We validate our approach in large state spaces, to show how
the approach scales, and in real environments, to show the applicability of our
method on real robots.
  We prove that our approach is sound, probabilistically complete, and
tractable in practical cases.
