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This paper explores the potential of 5G new radio (NR) Time-of-Arrival (TOA)data for indoor drone localization under different scenarios and conditionswhen fused with inertial measurement unit (IMU) data. Our approach involvesperforming graph-based optimization to estimate the drone's position andorientation from the multiple sensor measurements. Due to the lack ofreal-world data, we use Matlab 5G toolbox and QuaDRiGa (quasi-deterministicradio channel generator) channel simulator to generate TOA measurements for theEuRoC MAV indoor dataset that provides IMU readings and ground truths 6DoFposes of a flying drone. Hence, we create twelve sequences combining threepredefined indoor scenarios setups of QuaDRiGa with 2 to 5 base stationantennas. Therefore, experimental results demonstrate that, for a sufficientnumber of base stations and a high bandwidth 5G configuration, the pose graphoptimization approach achieves accurate drone localization, with an averageerror of less than 15 cm on the overall trajectory. Furthermore, the adoptedgraph-based optimization algorithm is fast and can be easily implemented foronboard real-time pose tracking on a micro aerial vehicle (MAV).

Pose Graph Optimization for a MAV Indoor Localization Fusing 5GNR TOA with an IMU