The emergence of 3D Gaussian Splatting (3DGS) has recently sparked a renewed wave of dense visual SLAM research. However, current methods face challenges such as sensitivity to artifacts and noise, sub-optimal selection of training viewpoints, and a lack of light global optimization. In this paper, we propose a dense SLAM system that tightly couples 3DGS with ORB features. We design a joint optimization approach for robust tracking and effectively reducing the impact of noise and artifacts. This involves combining novel geometric observations, derived from accumulated transmittance, with ORB features extracted from pixel data. Furthermore, to improve mapping quality, we propose an adaptive Gaussian expansion and regularization method that enables Gaussian primitives to represent the scene compactly. This is coupled with a viewpoint selection strategy based on the hybrid graph to mitigate over-fitting effects and enhance convergence quality. Finally, our approach achieves compact and high-quality scene representations and accurate localization. GSORB-SLAM has been evaluated on different datasets, demonstrating outstanding performance.
随着3D高斯散射（3DGS）的出现，密集视觉SLAM研究再次掀起了一股新的热潮。然而，当前的方法面临诸多挑战，例如对伪影和噪声的敏感性、训练视点选择不佳以及全局优化不足等问题。在本文中，我们提出了一种将3DGS与ORB特征紧密结合的密集SLAM系统。我们设计了一种联合优化方法，以实现鲁棒的跟踪，并有效减少噪声和伪影的影响。这种方法结合了基于累积透射率的几何观测与从像素数据中提取的ORB特征。此外，为了提升地图构建的质量，我们提出了一种自适应高斯扩展和正则化方法，使得高斯基元能够紧凑地表示场景。我们还引入了一种基于混合图的视点选择策略，以减少过拟合现象并增强收敛质量。最终，我们的方法实现了紧凑且高质量的场景表示和精确的定位。GSORB-SLAM在不同数据集上进行了评估，表现出了卓越的性能。