  Recently,3DGaussianSplattinghasshowngreatpotentialin visual Simultaneous
Localization And Mapping (SLAM). Existing methods have achieved encouraging
results on RGB-D SLAM, but studies of the monocular case are still scarce.
Moreover, they also fail to correct drift errors due to the lack of loop
closure and global optimization. In this paper, we present MG-SLAM, a monocular
Gaussian SLAM with a language-extended loop closure module capable of
performing drift-corrected tracking and high-fidelity reconstruction while
achieving a high-level understanding of the environment. Our key idea is to
represent the global map as 3D Gaussian and use it to guide the estimation of
the scene geometry, thus mitigating the efforts of missing depth information.
Further, an additional language-extended loop closure module which is based on
CLIP feature is designed to continually perform global optimization to correct
drift errors accumulated as the system runs. Our system shows promising results
on multiple challenging datasets in both tracking and mapping and even
surpasses some existing RGB-D methods.
