# GLC-SLAM: Gaussian Splatting SLAM with Efficient Loop Closure

arXiv: https://arxiv.org/abs/2409.10982 · HTML: https://mlsanity.com/paper/2409.10982/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2024.

## Built on by (most cited first, 7 of 7)

- [GauS-SLAM: Dense RGB-D SLAM with Gaussian Surfels](https://mlsanity.com/paper/2505.01934/) (2025)
- [GRAND-SLAM: Local Optimization for Globally Consistent Large-Scale Multi-Agent Gaussian SLAM](https://mlsanity.com/paper/2506.18885/) (2025)
- [VarSplat: Uncertainty-aware 3D Gaussian Splatting for Robust RGB-D SLAM](https://mlsanity.com/paper/2603.09673/) (2026)
- [DiskChunGS: Large-Scale 3D Gaussian SLAM Through Chunk-Based Memory Management](https://mlsanity.com/paper/2511.23030/) (2025)
- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [IRIS-SLAM: Unified Geo-Instance Representations for Robust Semantic Localization and Mapping](https://mlsanity.com/paper/2602.18709/) (2026)
- [VGGT-GS SLAM: Uncalibrated Monocular Gaussian Splatting SLAM with Feed-Forward Priors](https://mlsanity.com/paper/2609.19628/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) has gained significant attention for its application in dense Simultaneous Localization and Mapping (SLAM), enabling real-time rendering and high-fidelity mapping. However, existing 3DGS-based SLAM methods often suffer from accumulated tracking errors and map drift, particularly in large-scale environments. To address these issues, we introduce GLC-SLAM, a Gaussian Splatting SLAM system that integrates global optimization of camera poses and scene models. Our approach employs frame-to-model tracking and triggers hierarchical loop closure using a global-to-local strategy to minimize drift accumulation. By dividing the scene into 3D Gaussian submaps, we facilitate efficient map updates following loop corrections in large scenes. Additionally, our uncertainty-minimized keyframe selection strategy prioritizes keyframes observing more valuable 3D Gaussians to enhance submap optimization. Experimental results on various datasets demonstrate that GLC-SLAM achieves superior or competitive tracking and mapping performance compared to state-of-the-art dense RGB-D SLAM systems.