GS-GVINS: A Tightly-integrated GNSS-Visual-Inertial Navigation System Augmented by 3D Gaussian Splatting

TL;DR

This paper introduces GS-GVINS, a novel GNSS-Visual-Inertial navigation system that integrates 3D Gaussian Splatting for improved large-scale outdoor navigation accuracy, utilizing differentiable scene representation and dynamic map pruning.

Contribution

It presents the first integration of 3D Gaussian Splatting with GNSS-Visual-Inertial systems, including analytical Jacobians and a motion-aware pruning mechanism for outdoor navigation.

Findings

Enhanced navigation accuracy in diverse environments

Effective 3D scene representation with 3D Gaussian Splatting

Robust performance in open-sky, sub-urban, and urban settings

Abstract

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.