LSGS-Loc: Towards Robust 3DGS-Based Visual Localization for Large-Scale UAV Scenarios

TL;DR

LSGS-Loc introduces a robust visual localization method for large-scale UAV scenarios using 3D Gaussian Splatting, combining scale-aware pose initialization and artifact mitigation to achieve state-of-the-art accuracy.

Contribution

It presents a novel localization pipeline with a scale-aware initialization and a Laplacian-based masking mechanism, improving robustness without scene-specific training.

Findings

Achieves state-of-the-art accuracy on large-scale UAV benchmarks.

Significantly outperforms existing 3DGS-based localization methods.

Demonstrates robustness to environmental variations and reconstruction artifacts.

Abstract

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