Sensor fusion techniques for service robotic positioning and flight in GNSS denied environments using UWB technology

Drones are usually designed to navigate outdoor spaces, they are often equipped with cameras and other sensors, making them a powerful tool for surveying large areas. In these environments a Global Navigation Satellite System (GNSS) is combined with an Inertial Measurement Unit (IMU) to achieve precise positioning, allowing successful navigation in a 3D open space. During the past years users started to use small size drones in challenging environments, indoor places, inside caves or near bridges, where a GNSS is not always reliable or reachable. Classical positioning techniques are no longer efficient in these cases, so it is necessary to develop new systems to adapt in these situations. Ultra WideBand (UWB) sensors are used to enhance the positioning in closed environments. These sensors allow two tags to exchange signals at high frequency in order to retrieve the distance between each other. It is possible to fuse this information with classical positioning methods to resolve the positioning problem in any scenario. The goal of this thesis is to design a system that allows drones to flight in both GNSS enabled and denied environments using UWB tags. This work explores the main positioning techniques used in literature and introduces several sensor fusion algorithms, aiming at comparing them in terms of both accuracy and precision. The final intent is to achieve reliable flight in any situation without disruption of service: referred as seamless flight. The capabilities of the proposed method are measured in a simulated environment and then validated on a real quadcopter. In order to easily implement the algorithms, all proposed codes are integrated in the PX4-Autopilot Robotics API. This allows to test the same instances of the system both on the companion computer, a Raspberry Pi 4 on the drone, and on the Gazebo simulation environment on a desktop computer, without changing a single line of code. The results of this work show the outstanding capabilities of Kalman filters to fuse sensor’s information, with a particular focus on their nonlinear variant. Thanks to these methods is possible to obtain a reliable pose estimate using the raw UWB ranging data and augmenting it with predicted velocity estimate, that is vital in achieving stable control.