Real-Time Relative Positioning System Implementation Employing Signals of Opportunity, Inertial, and Optical Flow Modalities

Current navigation technologies are relying on global navigation satellite system (GNSS) information. As in terms of reliability and precision next-generation autonomous vehicle requirements cannot be fully satisfied by GNSS, a sensor information fusion must be employed, leading to the exploration of new positioning methods. In this work, a reliable relative positioning solution in GNSS-challenged areas is investigated, using a combination of signals of opportunity (SOPs), inertial, and optical flow data. The proposed real-time relative positioning system exploits the fused data in the absence of GNSS signals for localization, employing a tracking algorithm to estimate the agent's trajectory in space and time. Extensive outdoor experiments employing an Unmanned Aerial Vehicle (UAV) are carried out to demonstrate the applicability of the proposed technique, validating its performance against various positioning approaches, including GNSS.