GCD-SLAM: Geometric-Correction Decoupling for RGB-D Gaussian Splatting SLAM

Localization refinement in evolving scene representations can become inherently coupled with the representations used for optimization, when these representations are constructed from previously estimated states. Such coupling makes localization correction dependent on the underlying representation and its construction history, potentially limiting the effectiveness of state refinement. We identify this representation-dependent optimization issue in RGB-D Gaussian Splatting SLAM and propose **GCD-SLAM**, a Geometric-Correction Decoupling framework that introduces representation-independent localization correction beyond the native Gaussian tracking-mapping loop. Specifically, GCD-SLAM develops **Independent Geometric Guidance** to derive correction cues directly from temporal depth observations rather than trajectory-conditioned Gaussian representations. **Reliability-aware Correction Selection** identifies reliable correction conditions and enables selective localization refinement. Finally, **Localization State Separation** decouples corrected poses from the native tracking-mapping process, preserving Gaussian reconstruction consistency while providing corrected localization outputs. Experiments on challenging RGB-D SLAM scenarios demonstrate that GCD-SLAM achieves effective selective localization improvement while preserving the native Gaussian tracking-mapping process and maintaining robustness under challenging near-range conditions.