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The paper introduces LSGS-Loc, a novel visual localization pipeline designed for large-scale UAV scenarios using 3D Gaussian Splatting (3DGS). It addresses limitations in pose initialization and sensitivity to rendering artifacts by incorporating a scale-aware pose initialization strategy and a Laplacian-based reliability masking mechanism. Experiments on large-scale UAV benchmarks demonstrate state-of-the-art accuracy and robustness compared to existing 3DGS-based approaches.
Achieve robust UAV visual localization in complex, large-scale environments by grounding pose estimation in 3DGS scale constraints and mitigating reconstruction artifacts with Laplacian-guided refinement.
Visual localization in large-scale UAV scenarios is a critical capability for autonomous systems, yet it remains challenging due to geometric complexity and environmental variations. While 3D Gaussian Splatting (3DGS) has emerged as a promising scene representation, existing 3DGS-based visual localization methods struggle with robust pose initialization and sensitivity to rendering artifacts in large-scale settings. To address these limitations, we propose LSGS-Loc, a novel visual localization pipeline tailored for large-scale 3DGS scenes. Specifically, we introduce a scale-aware pose initialization strategy that combines scene-agnostic relative pose estimation with explicit 3DGS scale constraints, enabling geometrically grounded localization without scene-specific training. Furthermore, in the pose refinement, to mitigate the impact of reconstruction artifacts such as blur and floaters, we develop a Laplacian-based reliability masking mechanism that guides photometric refinement toward high-quality regions. Extensive experiments on large-scale UAV benchmarks demonstrate that our method achieves state-of-the-art accuracy and robustness for unordered image queries, significantly outperforming existing 3DGS-based approaches. Code is available at: https://github.com/xzhang-z/LSGS-Loc
