Enhancing Visual SLAM Performances With Compact 3-D Gaussian Splatting Representation

The dense simultaneous localization and mapping (SLAM) algorithm plays a crucial role in comprehending unknown environments. Recent studies have demonstrated the effectiveness of a dense SLAM system based on 3D Gaussian Splatting, enabling high-quality reconstruction and real-time rendering of scenes. However, many methods often rely on numerous Gaussian ellipsoids to represent the scene, leading to high memory consumption and time-consuming training process. To overcome this challenge, we propose a sampling method based on image gradients for initializing and expanding Gaussains that represent the scene map. This innovative method represents the scene map with compact 3D Gaussian distributions without degrading rendering quality. Moreover, to simplify keyframe management and improve the rendering performance in the mapping, we introduce a keyframe selection strategy that computes Gaussian covisibility according to the camera pose of the candidate frames in the mapping process. Our method achieves competitive performance compared to other advanced visual SLAM algorithms in camera pose estimation and map reconstruction, highlighting its efficiency in optimizing memory usage and improvement in algorithm performance.