VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM

• URL: http://arxiv.org/abs/2512.02293v1
• Date: Tue, 02 Dec 2025 00:19:13 GMT
• Title: VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM
• Authors: Zihan Zhu, Wei Zhang, Norbert Haala, Marc Pollefeys, Daniel Barath,
• Abstract summary: We present VIGS-SLAM, a visual-inertial 3D Gaussian Splatting SLAM system.<n>It achieves robust real-time tracking and high-fidelity reconstruction.<n>Our method tightly couples visual and inertial cues within a unified optimization framework.
• Score: 75.55522219717137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 motion blur, low texture, and exposure variations. Our method tightly couples visual and inertial cues within a unified optimization framework, jointly refining camera poses, depths, and IMU states. It features robust IMU initialization, time-varying bias modeling, and loop closure with consistent Gaussian updates. Experiments on four challenging datasets demonstrate our superiority over state-of-the-art methods. Project page: https://vigs-slam.github.io

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