With their high-fidelity scene representation capability, the attention of
SLAM field is deeply attracted by the Neural Radiation Field (NeRF) and 3D
Gaussian Splatting (3DGS). Recently, there has been a Surge in NeRF-based SLAM,
while 3DGS-based SLAM is sparse. A novel 3DGS-based SLAM approach with a fusion
of deep visual feature, dual keyframe selection and 3DGS is presented in this
paper. Compared with the existing methods, the proposed selectively tracking is
achieved by feature extraction and motion filter on each frame. The joint
optimization of pose and 3D Gaussian runs through the entire mapping process.
Additionally, the coarse-to-fine pose estimation and compact Gaussian scene
representation are implemented by dual keyfeature selection and novel loss
functions. Experimental results demonstrate that the proposed algorithm not
only outperforms the existing methods in tracking and mapping, but also has
less memory usage.

通过深度视觉特征、双关键帧选择和 3D 高斯喷洒，本文提出了一种基于三维高斯喷洒的 SLAM 方法。该方法通过特征提取和运动滤波在每一帧上实现了选择性跟踪，通过整个映射过程进行姿态和三维高斯的联合优化。此外，通过双关键特征选择和新颖的损失函数实现了粗糙到精细的姿态估计和紧凑的高斯场景表示。实验结果表明，该算法不仅在跟踪和建图方面优于现有方法，而且内存使用更少。