VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM

We present VIGS-SLAM, a visual-inertial 3D Gaussian Splat-ting SLAM system that achieves robust real-time tracking and high-fidelity reconstruction. Although recent 3DGS-based SLAM methodsachieve dense and photorealistic mapping, their purely visual design de-grades under challenging conditions such as motion blur, low texture,and exposure variations. Our method tightly couples visual and inertialcues within a unified optimization framework, jointly optimizing cam-era poses, depths, and IMU states. It features robust IMU initialization,time-varying bias modeling, and loop closure with consistent Gaussianupdates. Experiments on five challenging datasets demonstrate our su-periority over state-of-the-art methods. The code will be made public.