  The agility and versatility offered by UAV platforms still encounter
obstacles for full exploitation in industrial applications due to their indoor
usage limitations. A significant challenge in this sense is finding a reliable
and cost-effective way to localize aerial vehicles in a GNSS-denied
environment. In this paper, we focus on the visual-based positioning paradigm:
high accuracy in UAVs position and orientation estimation is achieved by
leveraging the potentials offered by a dense and size-heterogenous map of tags.
In detail, we propose an efficient visual odometry procedure focusing on
hierarchical tags selection, outliers removal, and multi-tag estimation fusion,
to facilitate the visual-inertial reconciliation. Experimental results show the
validity of the proposed localization architecture as compared to the state of
the art.
