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).

翻译：本文探究了在多种场景与条件下，将5G新空口（NR）到达时间（TOA）数据与惯性测量单元（IMU）数据融合，用于室内无人机定位的潜力。我们的方法通过执行基于图的优化，从多个传感器测量值中估计无人机的位置与姿态。由于缺乏真实世界数据，我们使用Matlab 5G工具箱和QuaDRiGa（准确定性无线信道生成器）信道模拟器，针对提供IMU读数与飞行无人机真实6自由度位姿的EuRoC MAV室内数据集，生成TOA测量值。据此，我们构建了十二个序列，每个序列结合QuaDRiGa的三种预设室内场景设置与2至5个基站天线。实验结果表明，在基站数量充足且采用高带宽5G配置的条件下，位姿图优化方法能够实现精确的无人机定位，整个轨迹的平均误差小于15厘米。此外，所采用的基于图的优化算法速度快，易于在微型飞行器（MAV）上实现机载实时位姿追踪。