5G New Radio Time of Arrival (ToA) data has the potential to revolutionize indoor localization for micro aerial vehicles (MAVs). However, its performance under varying network setups, especially when combined with IMU data for real-time localization, has not been fully explored so far. In this study, we develop an error state Kalman filter (ESKF) and a pose graph optimization (PGO) approach to address this gap. We systematically evaluate the performance of the derived approaches for real-time MAV localization in realistic scenarios with 5G base stations in Line-Of-Sight (LOS), demonstrating the potential of 5G technologies in this domain. In order to experimentally test and compare our localization approaches, we augment the EuRoC MAV benchmark dataset for visual-inertial odometry with simulated yet highly realistic 5G ToA measurements. Our experimental results comprehensively assess the impact of varying network setups, including varying base station numbers and network configurations, on ToA-based MAV localization performance. The findings show promising results for seamless and robust localization using 5G ToA measurements, achieving an accuracy of 15 cm throughout the entire trajectory within a graph-based framework with five 5G base stations, and an accuracy of up to 34 cm in the case of ESKF-based localization. Additionally, we measure the run time of both algorithms and show that they are both fast enough for real-time implementation.

翻译：5G新空口到达时间（ToA）数据有望彻底改变微型飞行器（MAV）的室内定位，但其在不同网络配置下，特别是与惯性测量单元（IMU）数据结合用于实时定位时的性能尚未得到充分探索。本研究开发了误差状态卡尔曼滤波（ESKF）和位姿图优化（PGO）方法以填补这一空白。我们系统评估了所提方法在真实场景中基于5G基站的视距（LOS）条件下对MAV进行实时定位的性能，展示了5G技术在该领域的潜力。为实验测试并比较我们的定位方法，我们在EuRoC MAV基准数据集（用于视觉惯性里程计）中增加了模拟但高度逼真的5G ToA测量值。实验结果全面评估了不同网络配置（包括基站数量及网络配置变化）对基于ToA的MAV定位性能的影响。研究表明，利用5G ToA测量可实现无缝鲁棒的定位：在基于图优化的框架中，当使用五个5G基站时，整个轨迹的定位精度达15厘米；而基于ESKF的定位精度可达34厘米。此外，我们测量了两种算法的运行时间，证明其均满足实时实现的速度要求。