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.
@article{arxiv.2309.13311,
title = {Tag-based Visual Odometry Estimation for Indoor UAVs Localization},
author = {Massimiliano Bertoni and Simone Montecchio and Giulia Michieletto and Roberto Oboe and Angelo Cenedese},
journal= {arXiv preprint arXiv:2309.13311},
year = {2023}
}