LightSplat: Real-Time High-Fidelity 3D Gaussian SLAM with Loop Closure

2026-09-07

AI Summary

为解决3DGS系统在实际应用中的性能和地图适应性问题，提出LightSplat框架，结合稀疏特征与稠密高斯子图构建，并通过在线闭环优化实现高效实时的高保真3D重建。

Abstract

SLAM systems based on 3D Gaussian Splatting (3DGS) have recently demonstrated promising reconstruction accuracy for dense 3D scene representations. However, current 3DGS systems struggle to meet the strict demands of real-world deployments due to severe limitations in operational performance and map adaptability. To this end, we propose LightSplat, a hybrid-representation RGB-D SLAM framework. It synergizes local sparse features for robust and fast tracking with a dual-thread backend that progressively constructs dense Gaussian submaps. Crucially, we enable online loop closure through feature-accelerated 3DGS registration, refining overall map consistency through pose graph optimization. Ultimately, LightSplat achieves the online reconstruction of high-fidelity Gaussian map. Extensive experiments on multiple datasets and real-world robotic platform demonstrate that our method achieves near state-of-the-art reconstruction quality and the capability to accommodate practical camera motions, maintaining an average framerate of 8 FPS. Overall, LightSplat provides an efficient and robust foundation for deploying high-fidelity 3DGS in real-world environments.

Problem

• 3D Gaussian Splatting
• SLAM
• real-time performance
• map adaptability

Innovation

• 3D Gaussian Splatting
• Hybrid-representation
• Loop Closure
• Pose Graph Optimization
• Real-time Reconstruction