MotionGS：基于运动滤波器的紧凑型高斯泼溅SLAM
MotionGS : Compact Gaussian Splatting SLAM by Motion Filter
AI总结：
本文提出MotionGS，一种基于3DGS的紧凑型SLAM方法，通过融合深度视觉特征、运动滤波器与双关键帧选择实现高精度跟踪、位姿联合优化和低内存占用的场景表示。
摘要：
凭借高保真的场景表示能力，SLAM领域深受Neural Radiation Field (NeRF)和3D Gaussian Splatting (3DGS)的吸引。近期基于NeRF的SLAM激增，而基于3DGS的SLAM则较为稀少。本文提出了一种融合深度视觉特征、双关键帧选择和3DGS的新型基于3DGS的SLAM方法。与现有方法相比，所提出的跟踪通过对每帧进行特征提取和运动滤波来实现。位姿与3D高斯的联合优化贯穿整个建图过程。此外，通过双关键帧选择和新型损失函数实现了由粗到细的位姿估计和紧凑的高斯场景表示。实验结果表明，所提出的算法不仅在跟踪和建图上优于现有方法，而且内存占用更少。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
