High-fidelity reconstruction is crucial for dense SLAM. Recent popular methods utilize 3D gaussian splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. However, these methods ignore issues of detail and consistency in different parts of the scene. To address this, we propose RGBDS-SLAM, a RGB-D semantic dense SLAM system based on 3D multi-level pyramid gaussian splatting, which enables high-fidelity dense reconstruction of scene RGB, depth, and semantics. In this system, we introduce a 3D multi-level pyramid gaussian splatting method that restores scene details by extracting multi-level image pyramids for gaussian splatting training, ensuring consistency in RGB, depth, and semantic reconstructions. Additionally, we design a tightly-coupled multifeatures reconstruction optimization mechanism, allowing the reconstruction accuracy of RGB, depth, and semantic features to mutually enhance each other during the rendering optimization process. Extensive quantitative, qualitative, and ablation experiments on the Replica and ScanNet public datasets demonstrate that our proposed method outperforms current state-of-the-art methods, which achieves great improvement by 11.13% in PSNR and 68.57% in LPIPS.
高保真重建对于密集SLAM至关重要。近年来流行的方法利用3D高斯散点（3D Gaussian Splatting, 3D GS）技术对场景的RGB、深度和语义进行重建。然而，这些方法在场景不同部分的细节和一致性问题上存在忽视。为了解决这些问题，我们提出了RGBDS-SLAM，这是一种基于3D多级金字塔高斯散点的RGB-D语义密集SLAM系统，实现了场景RGB、深度和语义的高保真密集重建。 在该系统中，我们引入了一种3D多级金字塔高斯散点方法，通过提取多级图像金字塔进行高斯散点训练，恢复场景细节，并确保RGB、深度和语义重建的一致性。此外，我们设计了一种紧耦合的多特征重建优化机制，使RGB、深度和语义特征在渲染优化过程中能够相互增强重建精度。 在Replica和ScanNet公共数据集上的大量定量、定性和消融实验表明，我们提出的方法优于现有最先进方法，在PSNR指标上提升了11.13%，在LPIPS指标上提升了68.57%。