SupGS-SLAM: Gaussian Splatting SLAM with Efficient Keyframe Strategy and Supplementary Mapping

Abstract

Gaussian Splatting SLAM methods have exhibited impressive high-fidelity rendering performance. Existing methods maintain high rendering quality around the current camera viewpoint, but the rendering quality degrades in previously observed regions as the camera moves away, particularly in real-world scenarios. We identify two core factors for high-quality rendering: keyframes should efficiently cover the entire scene while minimizing redundancy, and the mapping strategy should effectively select critical keyframes for full scene optimization. To address these issues, we propose SupGS-SLAM to improve rendering quality across the entire scene. For effective keyframe management, we propose an efficient keyframe strategy, which reduces redundant keyframe selection and prioritizes the optimization of critical keyframes by assigning high weights. For enhanced mapping, we propose a supplementary mapping strategy comprising three components: supplementary densification, supplementary global mapping, and supplementary depth mapping. In supplementary densification, we add supplementary Gaussian primitives to previous regions with insufficient representation. In supplementary global mapping, we select keyframes globally to optimize the full scene. In supplementary depth mapping, we use estimated depth to optimize regions without ground-truth depth. Extensive experiments demonstrate that SupGS-SLAM achieves excellent performance on both synthetic and real-world datasets. The project page is available at https://github.com/rucliushuai/SupGS-SLAM.