GS-GVINS: 一种集成GNSS-视觉-惯性导航系统的系统，结合3D高斯点分布
GS-GVINS: A Tightly-integrated GNSS-Visual-Inertial Navigation System Augmented by 3D Gaussian Splatting
- Department of Geomatics Engineering, Shulich School of Engineering, University of Calgary（测绘工程系、谢里尔工程学院、卡尔加里大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
GS-GVINS是一种结合3D高斯点分布的GNSS-视觉-惯性导航系统，通过提升导航精度实现大规模户外导航。
AI中文摘要：
最近，3D高斯点分布（3DGS）的出现引起了三维地图重建和视觉SLAM领域广泛关注。尽管已有大量研究探索了3DGS在仅使用视觉传感器或结合激光雷达（LiDAR）和惯性测量单元（IMU）进行室内轨迹跟踪的应用，但其在大规模户外导航中与GNSS的整合仍处于探索阶段。为了解决这些问题，我们提出了GS-GVINS：一种集成了GNSS-视觉-惯性导航系统的系统，结合了3DGS。该系统利用3D高斯作为连续可微的场景表示，在大规模户外环境中增强导航性能。值得注意的是，GS-GVINS是首个直接利用SE3相机姿态相对于3D高斯的解析雅可比矩阵的GNSS-视觉-惯性导航应用。为了在极端动态状态下保持3DGS渲染的质量，我们引入了运动感知的3D高斯剪枝机制，根据相对姿态平移和相机射线上累积的不透明度更新地图。为了验证，我们在不同的驾驶环境中测试了我们的系统：开放天空、次城市和城市。使用了自采数据和公开数据集进行评估。结果表明，GS-GVINS在不同驾驶环境中增强了导航精度。
英文摘要：
Recently, the emergence of 3D Gaussian Splatting (3DGS) has drawn significant attention in the area of 3D map reconstruction and visual SLAM. While extensive research has explored 3DGS for indoor trajectory tracking using visual sensor alone or in combination with Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU), its integration with GNSS for large-scale outdoor navigation remains underexplored. To address these concerns, we proposed GS-GVINS: a tightly-integrated GNSS-Visual-Inertial Navigation System augmented by 3DGS. This system leverages 3D Gaussian as a continuous differentiable scene representation in largescale outdoor environments, enhancing navigation performance through the constructed 3D Gaussian map. Notably, GS-GVINS is the first GNSS-Visual-Inertial navigation application that directly utilizes the analytical jacobians of SE3 camera pose with respect to 3D Gaussians. To maintain the quality of 3DGS rendering in extreme dynamic states, we introduce a motionaware 3D Gaussian pruning mechanism, updating the map based on relative pose translation and the accumulated opacity along the camera ray. For validation, we test our system under different driving environments: open-sky, sub-urban, and urban. Both self-collected and public datasets are used for evaluation. The results demonstrate the effectiveness of GS-GVINS in enhancing navigation accuracy across diverse driving environments.
