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。

MGS-SLAM: 单目稀疏跟踪与高斯建图并带深度平滑正则化

MGS-SLAM: Monocular Sparse Tracking and Gaussian Mapping with Depth  Smooth Regularization

Our paper proposes a direct sparse visual odometry method that combines event
and RGB-D data to estimate the pose of agile-legged robots during dynamic
locomotion and acrobatic behaviors. Event cameras offer high temporal
resolution and dynamic range, which can eliminate the issue of blurred RGB
images during fast movements. This unique strength holds a potential for
accurate pose estimation of agile-legged robots, which has been a challenging
problem to tackle. Our framework leverages the benefits of both RGB-D and event
cameras to achieve robust and accurate pose estimation, even during dynamic
maneuvers such as jumping and landing a quadruped robot, the Mini-Cheetah. Our
major contributions are threefold: Firstly, we introduce an adaptive time
surface (ATS) method that addresses the whiteout and blackout issue in
conventional time surfaces by formulating pixel-wise decay rates based on scene
complexity and motion speed. Secondly, we develop an effective pixel selection
method that directly samples from event data and applies sample filtering
through ATS, enabling us to pick pixels on distinct features. Lastly, we
propose a nonlinear pose optimization formula that simultaneously performs
3D-2D alignment on both RGB-based and event-based maps and images, allowing the
algorithm to fully exploit the benefits of both data streams. We extensively
evaluate the performance of our framework on both public datasets and our own
quadruped robot dataset, demonstrating its effectiveness in accurately
estimating the pose of agile robots during dynamic movements.

本文提出了一种直接使用事件和 RGB-D 数据相结合的稀疏视觉里程法，用于估计活泼的四足机器人在动态运动和杂技行为中的姿态。