Unmanned aerial vehicles (UAVs) are quickly emerging as a feasible and low-cost technology for use in various indoor applications such as search-and-rescue missions, manufacturing and precision agriculture. These services and applications demand a high autonomy and control dynamics, both of which relies on accurate determination and localization of the unmanned system. In outdoor environments several global positioning systems are available (GPS, Galileo), with an accuracy of around 5 meters. Concerning indoors environments, however, GPS signals are typically marginal or unavailable because of signal loss/attenuation caused by obstructions or radio interferences.  Several ranging technologies are available for indoor positioning, but most of them lack in the accuracy for position and rate of position estimation. In our research we wanted to provide a precise and fast localization system for autonomous navigation of UAVs in indoor environments. In the first stage of the project, we analyzed several ranging techniques such as Wi-Fi, CSS (Chirp Spread Spectrum), UWB (Ultra-Wide Band). Based on our experiments, we found that the Wi-Fi and CSS are affected by a considerable multipath effect (that typically provides an over-estimation of the distance between two sensors) resulting in a poor quality of the position estimation (through trilateration). UWB, instead, provides the best performance in terms of accuracy. But the typical rate with which the UWB provides a position estimation is not enough to allow a fast refresh of the position estimate. We also found that another important drawback of the UWB technology is a lower accuracy on vertical axis, compared to the horizontal plane, mainly caused by the reflection of the UWB signal on the ground.
On the other side, inertial sensors provide a fast position estimation rate but with an error growing exponentially over the time.
Because of the need to provide precise and fast localization of unmanned systems in indoor environments, in this project we investigated the use of Ultra-WideBand technology, in conjunction with inertial sensors and vision sensors, for the development of high-accuracy and high-rate localization algorithms. We wanted to overcome the limitation in the position rate of the UWB exploiting the fast rate of inertial sensors. In addition, we wanted to limit the error growth of inertial based position estimation using the range measurements provided by the UWB system.
Based on our experimental results, we found that the integration of UWB with inertial data and vision data as additional aiding in the sensor fusion algorithm overcame the limitations of UWB and allowed a centimeter-level precision position-estimation necessary to carry out the critical task of autonomous landing.
The obtained results show that an accuracy of 10 cm in position estimation can be achieved.
Localization algorithms based on Extended Kalman Filter have been developed on real hardware and successfully tested in real scenarios. Some of the proposed models focus on the estimation of inertial sensor errors (such as accelerometers and gyroscopes biases) using the high accuracy of UWB technology.