  This paper explores the potential of 5G new radio (NR) Time-of-Arrival (TOA)
data for indoor drone localization under different scenarios and conditions
when fused with inertial measurement unit (IMU) data. Our approach involves
performing graph-based optimization to estimate the drone's position and
orientation from the multiple sensor measurements. Due to the lack of
real-world data, we use Matlab 5G toolbox and QuaDRiGa (quasi-deterministic
radio channel generator) channel simulator to generate TOA measurements for the
EuRoC MAV indoor dataset that provides IMU readings and ground truths 6DoF
poses of a flying drone. Hence, we create twelve sequences combining three
predefined indoor scenarios setups of QuaDRiGa with 2 to 5 base station
antennas. Therefore, experimental results demonstrate that, for a sufficient
number of base stations and a high bandwidth 5G configuration, the pose graph
optimization approach achieves accurate drone localization, with an average
error of less than 15 cm on the overall trajectory. Furthermore, the adopted
graph-based optimization algorithm is fast and can be easily implemented for
onboard real-time pose tracking on a micro aerial vehicle (MAV).
