Large-scale compact 3D Gaussian splatting SLAM

3D Gaussian splatting (3DGS) offers significant advantages in real-time rendering, scene control, and editing within simultaneous localization and mapping (SLAM) systems. By using 3D Gaussian representations and splatting rendering techniques, 3DGS strikes a balance between speed and visual quality. However, many Gaussian ellipsoids and their associated parameters lead to high memory usage and storage demands. This poses challenges for large-scale applications and resource-constrained hardware such as lightweight GPUs. To address these issues, we introduce a novel, to our knowledge, large-scale compact 3D Gaussian splatting SLAM framework designed to minimize memory and storage requirements. First, we use dynamic masks and a geometric codebook to eliminate redundant Gaussians. Second, we propose a post-tuning and Gaussian pruning module that adjusts the Gaussian representations during post-tuning, enhancing scene reconstruction and further reducing the number of Gaussians. Experimental results on both RGB-D and monocular datasets show that our approach achieves a five-fold reduction in storage costs while delivering superior rendering quality compared to state-of-the-art algorithms., demonstrating improved tracking accuracy and reduced training time. Additionally, a preliminary evaluation under noisy outdoor-like conditions shows that the proposed framework retains competitive accuracy, indicating robustness beyond controlled indoor environments.