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.

本文介绍了MG-SLAM，一种单目高斯SLAM系统，其特点是具有语义扩展的环路闭合模块，能够在进行漂移校正的同时实现高保真度的重建和对环境的高层次理解。该系统通过将全局地图表示为3D高斯，并将其用于指导场景几何估计，从而减轻缺失深度信息的工作量。此外，基于CLIP特征的语义扩展环路闭合模块可以持续进行全局优化，以纠正系统运行期间累积的漂移错误。我们的系统在多个具有挑战性的数据集上取得了令人期待的跟踪和建图结果，甚至超过了一些现有的RGB-D方法。

单目高斯定位与地图构建中的语义循环闭环