Global SLAM Using 5G ToA Integration: Performance Analysis with Unknown Base Stations and Loop Closure Alternatives

This paper presents a novel approach that integrates 5G Time of Arrival (ToA) measurements into ORB-SLAM3 to enable global localization and enhance mapping capabilities for indoor drone navigation. We extend ORB-SLAM3's optimization pipeline to jointly process ToA data from 5G base stations alongside visual and inertial measurements while estimating system biases. This integration transforms the inherently local SLAM estimates into globally referenced trajectories and effectively resolves scale ambiguity in monocular configurations. Our method is evaluated using both Aerolab indoor datasets with RGB-D cameras and the EuRoC MAV benchmark, complemented by simulated 5G ToA measurements at 28 GHz and 78 GHz frequencies using MATLAB and QuaDRiGa. Extensive experiments across multiple SLAM configurations demonstrate that ToA integration enables consistent global positioning across all modes while maintaining local accuracy. For monocular configurations, ToA integration successfully resolves scale ambiguity and improves consistency. We further investigate scenarios with unknown base station positions and demonstrate that ToA measurements can effectively serve as an alternative to loop closure for drift correction. Comparative analysis with state-of-the-art methods, including UWB-VO, confirms our approach's robustness even with lower measurement frequencies and sequential base station operation. The results validate that 5G ToA integration provides substantial benefits for global SLAM applications, particularly in challenging indoor environments where accurate positioning is critical.