This letter introduces a novel framework for dense Visual Simultaneous
Localization and Mapping (VSLAM) based on Gaussian Splatting. Recently Gaussian
Splatting-based SLAM has yielded promising results, but rely on RGB-D input and
is weak in tracking. To address these limitations, we uniquely integrates
advanced sparse visual odometry with a dense Gaussian Splatting scene
representation for the first time, thereby eliminating the dependency on depth
maps typical of Gaussian Splatting-based SLAM systems and enhancing tracking
robustness. Here, the sparse visual odometry tracks camera poses in RGB stream,
while Gaussian Splatting handles map reconstruction. These components are
interconnected through a Multi-View Stereo (MVS) depth estimation network. And
we propose a depth smooth loss to reduce the negative effect of estimated depth
maps. Furthermore, the consistency in scale between the sparse visual odometry
and the dense Gaussian map is preserved by Sparse-Dense Adjustment Ring (SDAR).
We have evaluated our system across various synthetic and real-world datasets.
The accuracy of our pose estimation surpasses existing methods and achieves
state-of-the-art performance. Additionally, it outperforms previous monocular
methods in terms of novel view synthesis fidelity, matching the results of
neural SLAM systems that utilize RGB-D input.

基于高斯喷洒的稠密视觉同时定位与建图（VSLAM）引入了一种新的框架，通过独特地将先进的稀疏视觉里程计与稠密高斯喷洒场景表示相结合，从而消除了依赖于深度图的限制并增强了跟踪的稳健性。我们的系统在各种合成和真实数据集上的评估证明了其姿态估计的准确性超越了现有方法，达到了最先进的性能，并且在新颖视角合成保真度方面胜过以前的单目方法，与利用 RGB-D 输入的神经 SLAM 系统的结果相匹配。