Graph-Based vs. Error State Kalman Filter-Based Fusion Of 5G And Inertial Data For MAV Indoor Pose Estimation

TL;DR

This paper compares graph-based and error state Kalman filter-based methods for 5G and inertial data fusion to improve indoor MAV localization, demonstrating promising accuracy and real-time performance in realistic scenarios.

Contribution

It introduces and evaluates two novel fusion approaches for 5G and inertial data in MAV indoor localization, with comprehensive experimental validation.

Findings

Graph-based approach achieves 15 cm accuracy.

ESKF-based approach achieves up to 34 cm accuracy.

Both methods are suitable for real-time implementation.

Abstract

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.