Visual Odometry and Trajectory Reconstruction for UAVs

The growing popularity of systems based on Unmanned Aerial Vehicles (UAVs) is highlighting their vulnerability particularly in relation to the positioning system used. Typically, UAV architectures use the civilian GPS which is exposed to a number of different attacks, such as jamming or spoofing. This is why it is important to develop alternative methodologies to accurately estimate the actual UAV position without relying on GPS measurements only. In this paper we propose a position estimate method for UAVs based on monocular visual odometry. We have developed a flight control system capable of keeping track of the entire trajectory travelled, with a reduced dependency on the availability of GPS signal. Moreover, the simplicity of the developed solution makes it applicable to a wide range of commercial drones. The final goal is to allow for safer flights in all conditions, even under cyber-attacks trying to deceive the drone

Software Tool for Evaluation of Multi-Sensor Object Tracking in ADAS systems

Nowadays, the innovations of AI and other automated decision-making software are spreading to many different areas. The automotive field in particular is rapidly shifting towards the concepts of Advanced Driver Assistance Systems (ADAS) and Obstacle Detection and Avoidance Systems (ODAS), which could bring huge benefits in the future. However, before being able to use these tools, many assurances are required regarding their functioning and safety. To this end, several control techniques exist to evaluate the performance of these software, but a reliable and repeatable method for evaluating complex scenarios and corner cases is still lacking. In this paper we propose a suite of tools for the generation and analysis of synthetic tests, aimed at evaluating and analyzing the functioning of autonomous driving systems in order to measure their effectiveness and to drive their development