FGS-SLAM：基于傅里叶的高斯泼溅用于稀疏与稠密地图融合的实时SLAM
FGS-SLAM: Fourier-based Gaussian Splatting for Real-time SLAM with Sparse and Dense Map Fusion
AI总结：
提出FGS-SLAM，通过基于傅里叶频域分析的自适应致密化方法建立高斯先验以实现快速收敛，并构建稀疏与稠密地图，在Replica和TUM RGB-D数据集上以47 FPS实现高精度的实时定位与建图。
摘要：
3D高斯泼溅通过实现实时定位和构建高保真地图，推动了同步定位与建图（SLAM）技术。然而，高斯分布的不确定性与初始化参数带来了挑战，常需要大量的建图收敛，并导致高斯表示冗余或不足。为解决此问题，我们提出一种基于傅里叶频域分析的新型自适应致密化方法，以建立高斯先验实现快速收敛。此外，我们提出构建独立且统一的稀疏与稠密地图，其中稀疏地图通过广义迭代最近点（GICP）支持高效跟踪，稠密地图创建高保真视觉表示。这是首个利用频域分析实现实时高质量高斯建图的SLAM系统。实验结果表明，在Replica和TUM RGB-D数据集上平均帧率达到47 FPS，在定位和建图方面均达到了具有竞争力的精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
