Geo2DGS‐SLAM: Geometry‐Driven SLAM With 2D Gaussian Splatting

ABSTRACT Recent advances in SLAM systems based on 3D Gaussian Splatting have enabled dense and photorealistic scene reconstruction. However, 3DGS‐based methods still suffer from limited geometric awareness due to the lack of normal rendering support, view inconsistency and weak depth constraints, ultimately hindering high‐fidelity geometric reconstruction and robust pose tracking. In this paper, we present Geo2DGS‐SLAM, a novel dense RGB‐D SLAM framework that uses a geometry‐aware 2D Gaussian representation for scene modelling, featuring a hybrid tracking framework, 2DGS‐based submap construction and a mesh‐binding submap merging strategy. Unlike prior approaches that rely solely on 3DGS appearance modelling, our 2DGS representation enables more accurate rendering of both depth and surface normals, facilitating high‐quality and geometrically consistent reconstruction. To further enhance robustness and global consistency, the hybrid tracking framework integrates local bundle adjustment, loop closure and global bundle adjustment, which jointly mitigate accumulated drift and improve trajectory stability, particularly in long or challenging sequences. Extensive experiments on public indoor RGB‐D datasets demonstrate that Geo2DGS‐SLAM achieves competitive performance in geometry‐aware mapping, rendering fidelity and pose estimation accuracy among recent Gaussian‐based SLAM methods. Codes and additional experimental results will be available at https://github.com/Able1231/Geo2DGS‐SLAM .