  Despite the number of works published in recent years, vehicle localization
remains an open, challenging problem. While map-based localization and SLAM
algorithms are getting better and better, they remain a single point of failure
in typical localization pipelines. This paper proposes a modular localization
architecture that fuses sensor measurements with the outputs of off-the-shelf
localization algorithms. The fusion filter estimates model uncertainties to
improve odometry in case absolute pose measurements are lost entirely. The
architecture is validated experimentally on a real robot navigating
autonomously proving a reduction of the position error of more than 90% with
respect to the odometrical estimate without uncertainty estimation in a
two-minute navigation period without position measurements.
