LD3DGS-SLAM: Long-Distance Monocular SLAM With 3-D Gaussian Splatting and GNSS-Aided Localization for UAVs

Integrating neural rendering with simultaneous localization and mapping (SLAM) has shown great promise for achieving high-precision localization and photorealistic scene reconstruction. However, the dynamic perspectives of uncrewed aerial vehicles (UAVs), compounded by Global Navigation Satellite System (GNSS) signal interference and the accumulation of localization errors, pose significant challenges to existing monocular simultaneous localization and mapping (SLAM) methods in complex environments. To address these limitations, we propose LD3DGS-SLAM, a novel framework that integrates monocular SLAM, GNSS, and 3D Gaussian splatting (3DGS) [1] to enhance UAV-based localization and mapping. The system integrates traditional geometric feature-based localization with the multiview synthesis capability of 3D Gaussian rendering, achieving a breakthrough in accuracy while enhancing robustness. First, we construct a multisensor fusion graph optimization model that tightly integrates GNSS and monocular vision data, effectively mitigating the cumulative drift typical of traditional SLAM pipelines. Afterward, we introduce a 3D Gaussian-based mapping strategy that incrementally refines a dense scene representation using SLAM-generated point clouds, fusing geometric and texture information to reconstruct highly accurate and detailed maps. To further enhance localization performance in GNSS-denied scenarios, we develop a dynamic frame interpolation and tracking approach based on 3D Gaussian rendering. By synthesizing novel viewpoints, this method improves observation matching and strengthens loop closure detection, enabling robust relocalization in large-scale environments. To validate the effectiveness of the proposed framework, we implement a UAV test system and evaluate its performance on both the publicly available TUM [2] dataset and a self-collected aerial dataset spanning several kilometers. Experimental results show that LD3DGS-SLAM achieves a state-of-the-art average localization error of only 0.8 meters using just a monocular camera, even during long-range flight missions exceeding tens of kilometers and altitudes up to 500 meters. Overall, LD3DGS-SLAM effectively addresses the limitations of monocular SLAM in aerial scenarios, providing a robust, accurate, and cost-effective localization solution for urban air mobility and aerial Internet of Things (IoT) applications.