Thesis: Machine Learning-based Indoor Localization for Micro Aerial Vehicles
In the world of automation, micro aerial vehicles (MAVs) provide unprecedented perspectives for domestic and industrial applications. They can serve as mobile surveillance cameras, flexible transport platforms, or even as waiters in restaurants. However, indoor employment of these vehicles is still hindered by the lack of real-time positions estimates. The focus of this thesis is, thus, the development of accurate and fast indoor localization for MAVs combining computer vision and machine learning techniques.