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 tracking is achieved by feature extraction and motion filter on each frame. The joint optimization of poses and 3D Gaussians runs through the entire mapping process. Additionally, the coarse-to-fine pose estimation and compact Gaussian scene representation are implemented by dual keyframe 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.

翻译：凭借其高保真场景表示能力，神经辐射场（NeRF）与三维高斯溅射（3DGS）已深度吸引同步定位与建图（SLAM）领域的关注。近期，基于NeRF的SLAM方法大量涌现，而基于3DGS的SLAM方案则相对稀缺。本文提出一种融合深度视觉特征、双关键帧选择与3DGS的新型3DGS-SLAM方法。相较于现有方法，本方案通过逐帧特征提取与运动滤波实现跟踪，并将位姿与三维高斯的联合优化贯穿于整个建图过程。此外，通过双关键帧选择机制与新型损失函数，实现了从粗到精的位姿估计与紧凑的高斯场景表示。实验结果表明，所提算法不仅在跟踪与建图性能上优于现有方法，同时具有更低的内存占用。