<p>Recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated promising results in dense visual SLAM. However, existing Gaussian-based SLAM systems struggle to generate Gaussian maps in sparse views, constrained by memory limitations and real-time performance requirements. This typically leads to two key problems: First, the rendering quality and localization accuracy of Gaussian primitives are highly sensitive to unstable initial point clouds, with errors accumulating during the densification process. Second, when observing Gaussians from multiple views, the attributes of the Gaussian primitives can become overly influenced by the final training view, leading to a forgetting problem where earlier views are not adequately retained. To address these issues, we propose a robust RGB-D SLAM framework that incorporates enhanced spatial distribution and view-consistency optimization. Specifically, we introduce: (1) a texture-density-driven sampling and graph-based structure densification method that uses geometry information to improve Gaussian primitives accuracy, and (2) an optimization strategy based on Gaussian attributes fusion of view-consistency, which explores Gaussian attributes in different views, enabling Gaussian primitives to adapt to various scene perspectives. Our evaluation on Replica and TUM RGB-D datasets demonstrates superior performance, offering new insights for robust 3D reconstruction in resource-constrained systems.</p>