Industry 4.0, smart homes, and the Internet of Things are boosting the employment of autonomous aerial vehicles in indoor environments, where localization is still challenging, especially in the case of close and cluttered areas. In this paper, we propose a Visual Inertial Odometry localization method based on fiducial markers. Our approach enables multi-rotor aerial vehicle navigation in indoor environments and tackles the most challenging aspects of image-based indoor localization. In particular, we focus on a proper and continuous pose estimation, working from take-off to landing, at several different flying altitudes. With this aim, we designed a map of fiducial markers that produces results that are both dense and heterogeneous. Narrowly placed tags lead to minimal information loss during rapid aerial movements while four different classes of marker size provide consistency when the camera zooms in or out according to the vehicle distance from the ground. We have validated our approach by comparing the output of the localization algorithm with the ground-truth information collected through an optoelectronic motion capture system, using two different platforms in different flying conditions. The results show that error mean and standard deviation can remain constantly lower than $0.11 \mathrm{m}$, so not degrading when the aerial vehicle increases its altitude and, therefore, strongly improving similar state-of-the-art solutions.

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.

Tilted hexarotors embody a technology that remains partially unexploited in terms of its potential, especially concerning precise and concurrent position and attitude control. Focusing on these aerial platforms, we propose two control architectures that can tackle the trajectory tracking task, ensuring also the attitude regulation: one is designed resting on the differential flatness property of the system, which is investigated in the paper, and the other is a hierarchical nonlinear controller. We comparatively discuss the performance of the two control schemes, in terms of the accuracy of both the tracking control action and the attitude regulation, the input effort, and the robustness in the presence of disturbances. Numerical results reveal both the robustness of the hierarchical approach in the case of external disturbance and the accuracy of the differential flatness-based controller in unwindy conditions.