VPGS-SLAM: Voxel-based Progressive 3D Gaussian SLAM in Large-Scale Scenes

• URL: http://arxiv.org/abs/2505.18992v1
• Date: Sun, 25 May 2025 06:27:29 GMT
• Title: VPGS-SLAM: Voxel-based Progressive 3D Gaussian SLAM in Large-Scale Scenes
• Authors: Tianchen Deng, Wenhua Wu, Junjie He, Yue Pan, Xirui Jiang, Shenghai Yuan, Danwei Wang, Hesheng Wang, Weidong Chen,
• Abstract summary: VPGS-SLAM is the first 3DGS-based large-scale RGBD SLAM framework for both indoor and outdoor scenarios.<n>We design a novel voxel-based progressive 3D Gaussian mapping method with multiple submaps for compact and accurate scene representation.<n>In addition, we propose a 2D-3D fusion camera tracking method to achieve robust and accurate camera tracking in both indoor and outdoor large-scale scenes.
• Score: 26.06908154350295
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 3D Gaussian Splatting has recently shown promising results in dense visual SLAM. However, existing 3DGS-based SLAM methods are all constrained to small-room scenarios and struggle with memory explosion in large-scale scenes and long sequences. To this end, we propose VPGS-SLAM, the first 3DGS-based large-scale RGBD SLAM framework for both indoor and outdoor scenarios. We design a novel voxel-based progressive 3D Gaussian mapping method with multiple submaps for compact and accurate scene representation in large-scale and long-sequence scenes. This allows us to scale up to arbitrary scenes and improves robustness (even under pose drifts). In addition, we propose a 2D-3D fusion camera tracking method to achieve robust and accurate camera tracking in both indoor and outdoor large-scale scenes. Furthermore, we design a 2D-3D Gaussian loop closure method to eliminate pose drift. We further propose a submap fusion method with online distillation to achieve global consistency in large-scale scenes when detecting a loop. Experiments on various indoor and outdoor datasets demonstrate the superiority and generalizability of the proposed framework. The code will be open source on https://github.com/dtc111111/vpgs-slam.

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