用傅里叶分析加速3D高斯点云融合，实现实时高精度定位与建图
FGS-SLAM: Fourier-based Gaussian Splatting for Real-time SLAM with Sparse and Dense Map Fusion
- 基于傅里叶频域分析自适应添加高斯点，加快收敛速度
- 同时构建稀疏与稠密地图，帧率高达36 FPS（Replica/TUM数据集）
- 适合需要实时高保真建图的机器人导航与AR应用
3D高斯点云已推动同时定位与建图（SLAM）技术发展，实现实时定位与高保真地图构建。然而，高斯点位置与初始化参数的不确定性导致迭代收敛困难，常出现冗余或不足的表示。为此，我们提出一种基于傅里叶频域分析的自适应稠密化方法，用于快速建立高斯先验。同时，构建独立且统一的稀疏与稠密地图：稀疏地图通过广义迭代最近点（GICP）支持高效跟踪，稠密地图生成高保真视觉表征。本系统是首个利用频域分析实现实时高质量高斯映射的SLAM框架。实验结果表明，在Replica和TUM RGB-D数据集上平均帧率达36 FPS，定位与建图精度达到竞争水平。
原文摘要 · Abstract (English)
3D gaussian splatting has advanced simultaneous localization and mapping (SLAM) technology by enabling real-time positioning and the construction of high-fidelity maps. However, the uncertainty in gaussian position and initialization parameters introduces challenges, often requiring extensive iterative convergence and resulting in redundant or insufficient gaussian representations. To address this, we introduce a novel adaptive densification method based on Fourier frequency domain analysis to establish gaussian priors for rapid convergence. Additionally, we propose constructing independent and unified sparse and dense maps, where a sparse map supports efficient tracking via Generalized Iterative Closest Point (GICP) and a dense map creates high-fidelity visual representations. This is the first SLAM system leveraging frequency domain analysis to achieve high-quality gaussian mapping in real-time. Experimental results demonstrate an average frame rate of 36 FPS on Replica and TUM RGB-D datasets, achieving competitive accuracy in both localization and mapping.
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