针对大尺度无人机场景，提出更鲁棒的3D高斯泼溅定位方法。
LSGS-Loc: Towards Robust 3DGS-Based Visual Localization for Large-Scale UAV Scenarios
- 引入尺度感知的初始位姿估计，无需场景训练即可准确定位。
- 通过拉普拉斯可靠性掩码，有效规避模糊和伪影干扰。
- 在无序图像查询下表现领先，适合真实无人机导航场景。
大尺度无人机场景下的视觉定位对自主系统至关重要，但受几何复杂性和环境变化影响，仍具挑战性。尽管3D高斯泼溅（3DGS）已成为有前景的场景表示方式，现有基于3DGS的定位方法在大尺度下仍存在位姿初始化不稳、对渲染伪影敏感的问题。为此，我们提出LSGS-Loc，一种专为大规模3DGS场景设计的视觉定位新框架。首先，提出一种尺度感知的位姿初始化策略，结合与场景无关的相对位姿估计与显式的3DGS尺度约束，实现无需场景特定训练的几何合理定位。其次，在位姿优化阶段，为缓解模糊、浮点物等重建伪影的影响，设计基于拉普拉斯的可靠性掩码机制，引导光度优化聚焦于高质量区域。在多个大尺度无人机基准数据集上的实验表明，该方法在无序图像查询下达到当前最优精度与鲁棒性，显著优于已有3DGS基定位方法。代码已开源：https://github.com/xzhang-z/LSGS-Loc。
原文摘要 · Abstract (English)
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
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