VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915858301845504 |
|---|---|
| author | Zhu, Zihan Zhang, Wei Li, Moyang Haala, Norbert Pollefeys, Marc Barath, Daniel |
| author_facet | Zhu, Zihan Zhang, Wei Li, Moyang Haala, Norbert Pollefeys, Marc Barath, Daniel |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02293 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM Zhu, Zihan Zhang, Wei Li, Moyang Haala, Norbert Pollefeys, Marc Barath, Daniel Robotics Computer Vision and Pattern Recognition 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 |
| title | VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.02293 |