Convolutional neural networks and particle filter for UAV localization

Unmanned aerial vehicles (UAV) are now used in a large number of applications. In order to accomplish autonomous navigation, UAVs must be equipped with robust and accurate localization systems. Most localization solutions available today rely on global navigation satellite systems (GNSS). However, such systems are known to introduce instabilities as a result of interference. More advanced solutions now use computer vision. While deep learning has now become the state-of-the-art in many areas, few attempts were made to use it for localization. In this paper, we present an entirely new type of approach based on convolutional neural networks (CNN). The network is trained with a new purpose-built dataset constructed using publicly available aerial imagery. Features extracted with the model are integrated in a particle filter for localization. Initial validation using real-world data, indicated that the approach is able to accurately estimate the localization of a quadcopter.