Graph-Based Vs. Error State Kalman Filter-Based Fusion of 5G and Inertial Data for MAV Indoor Pose Estimation

5G New Radio Time of Arrival (ToA) data has the potential to revolutionizeindoor localization for micro aerial vehicles (MAVs). However, its performanceunder varying network setups, especially when combined with IMU data forreal-time localization, has not been fully explored so far. In this study, wedevelop an error state Kalman filter (ESKF) and a pose graph optimization (PGO)approach to address this gap. We systematically evaluate the performance of thederived approaches for real-time MAV localization in realistic scenarios with5G base stations in Line-Of-Sight (LOS), demonstrating the potential of 5Gtechnologies in this domain. In order to experimentally test and compare ourlocalization approaches, we augment the EuRoC MAV benchmark dataset forvisual-inertial odometry with simulated yet highly realistic 5G ToAmeasurements. Our experimental results comprehensively assess the impact ofvarying network setups, including varying base station numbers and networkconfigurations, on ToA-based MAV localization performance. The findings showpromising results for seamless and robust localization using 5G ToAmeasurements, achieving an accuracy of 15 cm throughout the entire trajectorywithin a graph-based framework with five 5G base stations, and an accuracy ofup to 34 cm in the case of ESKF-based localization. Additionally, we measurethe run time of both algorithms and show that they are both fast enough forreal-time implementation.