Structure Gaussian Splatting SLAM

Recent Gaussian Splatting SLAM (GS-SLAM) methods alignrendered and observed images through photometric and geometric resid-uals to achieve high-quality reconstruction and rendering. However, thesesignals weaken in low-texture and repetitive environments, which leadsto floaters, inconsistent geometry, and drift. Existing systems mitigatedrift through loop closure or global layout assumptions, but these mech-anisms are opportunistic or lack persistent structural entities that pro-vide reliable constraints throughout mapping. We introduce a structure-aware GS-SLAM framework that models planar structures as persistentPlanar Gaussian Instances (PGIs) within a 3D Gaussian map. EachPGI represents a planar surface associated with Gaussian primitives andmaintains a consistent identity across frames. This representation pro-motes planes from transient observations to map entities and convertsplanar structures into stable geometric constraints. Building on PGIs,we propose structure closure, which estimates relative camera pose frommultiple non-parallel planes without requiring trajectory revisits. Wefurther integrate PGIs into a joint optimization that combines photo-metric–depth supervision with plane-instance consistency to improvemulti-view geometric coherence and suppress floater growth. Experi-ments on public benchmarks show that the proposed system achievesstate-of-the-art performance in camera tracking, dense reconstruction,and novel-view rendering, with strong robustness in scenes with largetextureless surfaces and repetitive layouts. Our code is publicly availableat https://github.com/yanyan-li/StructureGS-SLAM.