  Simultaneous Localization and Mapping (SLAM) with dense representation plays
a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR)
applications. Recent advancements in dense representation SLAM have highlighted
the potential of leveraging neural scene representation and 3D Gaussian
representation for high-fidelity spatial representation. In this paper, we
propose a novel dense representation SLAM approach with a fusion of Generalized
Iterative Closest Point (G-ICP) and 3D Gaussian Splatting (3DGS). In contrast
to existing methods, we utilize a single Gaussian map for both tracking and
mapping, resulting in mutual benefits. Through the exchange of covariances
between tracking and mapping processes with scale alignment techniques, we
minimize redundant computations and achieve an efficient system. Additionally,
we enhance tracking accuracy and mapping quality through our keyframe selection
methods. Experimental results demonstrate the effectiveness of our approach,
showing an incredibly fast speed up to 107 FPS (for the entire system) and
superior quality of the reconstructed map.
