融合视觉与惯性信息，实现鲁棒实时定位与高保真重建
VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM
- 统一优化框架融合视觉与惯性数据，联合估计位姿、深度和IMU状态
- 在五个挑战性数据集上优于现有最先进方法，尤其在运动模糊等条件下表现突出
- 适合需要高精度重建的移动机器人、AR/VR应用
我们提出VIGS-SLAM，一种基于视觉-惯性3D高斯点云的SLAM系统，实现了鲁棒的实时跟踪与高保真重建。尽管近期基于3DGS的SLAM方法能生成稠密且逼真的地图，但其纯视觉设计在运动模糊、低纹理和光照变化等复杂条件下性能下降。本方法在统一优化框架中紧密耦合视觉与惯性信号，联合优化相机位姿、深度及IMU状态。系统具备鲁棒的IMU初始化、时变偏差建模以及带一致高斯更新的回环检测。在五个挑战性数据集上的实验表明，该方法显著优于现有最先进方法。
原文摘要 · Abstract (English)
We present VIGS-SLAM, a visual-inertial 3D Gaussian Splatting SLAM system that achieves robust real-time tracking and high-fidelity reconstruction. Although recent 3DGS-based SLAM methods achieve dense and photorealistic mapping, their purely visual design degrades under challenging conditions such as motion blur, low texture, and exposure variations. Our method tightly couples visual and inertial cues within a unified optimization framework, jointly optimizing camera poses, depths, and IMU states. It features robust IMU initialization, time-varying bias modeling, and loop closure with consistent Gaussian updates. Experiments on five challenging datasets demonstrate our superiority over state-of-the-art methods. Project page: https://vigs-slam.github.io
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