UAV-based LiDAR Mapping with Galileo-GPS PPP Processing and Cooperative Navigation

This paper deals with the problem of electrical asset mapping with LiDAR-equipped UAVs. Compared with standard solutions relying on ground-based augmentation systems and high value payloads integrated on single assets, two main innovations are proposed and discussed. First, the possibility to fulfill georeferencing accuracy and precision requirements without ground-based GNSS stations/networks is explored, exploiting multi-frequency multi-constellation receivers and the added value of the European GNSS Galileo. Precise Point Positioning processing is used to mimic the High Accuracy Service, which will be made available by Galileo in the near future providing decimeter-level absolute accuracy. GNSS estimates are fused with inertial measurements to the aim of positioning and attitude reconstruction. Second, the application potential of multi-drone systems is analyzed. A cooperative navigation strategy is adopted which exploits drone-to-drone visual tracking and differential GNSS processing to provide high accuracy attitude information. Formation geometry of the cooperative platforms is investigated with the aim of minimizing the attitude error. Navigation and georeferencing performance are tested on synthetic and experimental data using error metrics relevant to powerline reconstruction.