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 five real-world indoor datasets collected with RGB-D cameras and inertial measurement units (IMUs), complemented by simulated 5G ToA measurements at 28 GHz and 78 GHz frequencies using MATLAB and QuaDRiGa. Extensive experiments across four SLAM configurations (RGB-D, RGB-D-Inertial, Monocular, and Monocular-Inertial) demonstrate that ToA integration enables consistent global positioning across all modes while significantly improving local accuracy in minimal sensor setups. Notably, ToA-enhanced monocular SLAM achieves superior local accuracy (6.3 cm average) compared to the RGB-D baseline (11.5 cm), and enables reliable operation of monocular-inertial SLAM in scenarios where the baseline system fails completely. While ToA integration offers limited local accuracy improvements for sensor-rich configurations like RGB-D SLAM, it consistently enables robust global localization.

翻译：本文提出了一种新颖方法，将5G到达时间测量集成到ORB-SLAM3中，以实现室内无人机导航的全局定位并增强建图能力。我们扩展了ORB-SLAM3的优化流程，在估计系统偏差的同时联合处理来自5G基站的ToA数据以及视觉和惯性测量。这种集成将固有的局部SLAM估计转换为全局参考轨迹，并有效解决了单目配置中的尺度模糊性问题。我们使用RGB-D相机和惯性测量单元采集的五个真实室内数据集进行评估，并通过MATLAB和QuaDRiGa模拟28 GHz和78 GHz频率的5G ToA测量作为补充。在四种SLAM配置（RGB-D、RGB-D-惯性、单目和单目-惯性）上的大量实验表明，ToA集成能够在所有模式下实现一致的全局定位，同时显著提升最小传感器配置下的局部精度。值得注意的是，与RGB-D基线（11.5厘米）相比，ToA增强的单目SLAM实现了更优的局部精度（平均6.3厘米），并使单目-惯性SLAM在基线系统完全失效的场景中能够可靠运行。虽然ToA集成对RGB-D SLAM等传感器丰富的配置提供的局部精度改进有限，但它始终能够实现鲁棒的全局定位。