VINGS-Mono：大场景下的视觉惯性高斯溅射单目SLAM
VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes
AI总结：
针对大场景单目SLAM需求，提出包含VIO前端、2D高斯地图等组件的VINGS-Mono框架，实现高精度定位与高质量建图渲染，支持公里级户外场景且可在移动端实时运行。
摘要：
VINGS-Mono是一款面向大场景设计的单目（惯性）高斯溅射（Gaussian Splatting，GS）SLAM框架，包含四大核心组件：VIO前端、2D高斯地图、NVS回环检测与动态擦除器。在VIO前端中，RGB帧经密集光束法平差与不确定性估计处理，以提取场景几何信息与位姿。建图模块基于该输出增量式构建并维护2D高斯地图，其核心组成包括基于采样的光栅化器、分数管理器与位姿优化模块，三者协同提升建图速度与定位精度，使SLAM系统可处理包含多达5000万个高斯椭球的大规模城市场景。为保障大场景下的全局一致性，我们设计了回环检测模块，创新性地利用高斯溅射的新视角合成（Novel View Synthesis，NVS）能力实现回环检测并校正高斯地图。此外，我们提出动态擦除器，以解决真实户外场景中不可避免的动态物体问题。在室内外环境下的大量评估表明，本文方法的定位性能与视觉惯性里程计相当，且优于最新的GS/NeRF类SLAM方法；在建图与渲染质量方面也显著优于所有现有方法。我们还开发了一款移动应用，验证了该框架仅借助智能手机摄像头与低频IMU传感器即可实时生成高质量高斯地图。据我们所知，VINGS-Mono是首个可在户外环境运行、支持公里级大场景的单目高斯SLAM方法。
英文摘要：
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.
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。
