VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes

TL;DR

VINGS-Mono introduces a monocular SLAM framework using Gaussian Splatting for large-scale outdoor scenes, achieving real-time high-quality mapping and localization with a novel loop closure and dynamic object handling.

Contribution

It is the first monocular Gaussian SLAM system capable of large outdoor scene mapping and real-time operation on a smartphone, integrating novel loop closure and dynamic object removal.

Findings

Achieves localization comparable to visual-inertial odometry.

Outperforms recent Gaussian Splatting/NeRF SLAM methods in mapping quality.

Operates in real-time on mobile devices.

Abstract

VINGS-Mono is a monocular (inertial) Gaussian Splatting (GS) SLAM framework designed for large scenes. The framework comprises four main components: VIO Front End, 2D Gaussian Map, NVS Loop Closure, and Dynamic Eraser. In the VIO Front End, RGB frames are processed through dense bundle adjustment and uncertainty estimation to extract scene geometry and poses. Based on this output, the mapping module incrementally constructs and maintains a 2D Gaussian map. Key components of the 2D Gaussian Map include a Sample-based Rasterizer, Score Manager, and Pose Refinement, which collectively improve mapping speed and localization accuracy. This enables the SLAM system to handle large-scale urban environments with up to 50 million Gaussian ellipsoids. To ensure global consistency in large-scale scenes, we design a Loop Closure module, which innovatively leverages the Novel View Synthesis (NVS) capabilities of Gaussian Splatting for loop closure detection and correction of the Gaussian map. Additionally, we propose a Dynamic Eraser to address the inevitable presence of dynamic objects in real-world outdoor scenes. Extensive evaluations in indoor and outdoor environments demonstrate that our approach achieves localization performance on par with Visual-Inertial Odometry while surpassing recent GS/NeRF SLAM methods. It also significantly outperforms all existing methods in terms of mapping and rendering quality. Furthermore, we developed a mobile app and verified that our framework can generate high-quality Gaussian maps in real time using only a smartphone camera and a low-frequency IMU sensor. To the best of our knowledge, VINGS-Mono is the first monocular Gaussian SLAM method capable of operating in outdoor environments and supporting kilometer-scale large scenes.