将ORB特征与3D高斯点云结合，提升SLAM定位精度和重建质量。
GSORB-SLAM: Gaussian Splatting SLAM benefits from ORB features and Transmittance information
- 融合ORB特征与3D高斯点云，实现紧耦合优化跟踪
- 定位误差降低16.2%，重建峰值信噪比提升3.93dB
- 适合需要高精度实时三维建图的机器人应用
3D高斯点云（3DGS）的出现重新激发了稠密视觉SLAM的研究热潮。然而现有方法存在对噪声和伪影敏感、训练视角选择不佳以及缺乏全局优化等问题。本文提出GSORB-SLAM，一种将3DGS与ORB特征通过紧耦合优化流程结合的稠密SLAM框架。为减轻噪声和伪影影响，我们设计了一种新型几何表示与优化方法，显著提升定位精度与鲁棒性；针对高保真建模，提出自适应高斯扩展与正则化方法，实现紧凑且表达性强的场景建模，同时抑制冗余基元；此外，设计混合式图优化视角选择机制，有效降低过拟合并加速收敛。在多个数据集上的大量实验表明，本系统在跟踪精度上相比ORB-SLAM2基线降低16.2%的均方根误差（RMSE），在重建质量上相比3DGS-SLAM基线提升3.93 dB的峰值信噪比（PSNR）。项目主页：https://aczheng-cai.github.io/gsorb-slam.github.io/
原文摘要 · Abstract (English)
The emergence of 3D Gaussian Splatting (3DGS) has recently ignited a renewed wave of research in dense visual SLAM. However, existing approaches encounter challenges, including sensitivity to artifacts and noise, suboptimal selection of training viewpoints, and the absence of global optimization. In this paper, we propose GSORB-SLAM, a dense SLAM framework that integrates 3DGS with ORB features through a tightly coupled optimization pipeline. To mitigate the effects of noise and artifacts, we propose a novel geometric representation and optimization method for tracking, which significantly enhances localization accuracy and robustness. For high-fidelity mapping, we develop an adaptive Gaussian expansion and regularization method that facilitates compact yet expressive scene modeling while suppressing redundant primitives. Furthermore, we design a hybrid graph-based viewpoint selection mechanism that effectively reduces overfitting and accelerates convergence. Extensive evaluations across various datasets demonstrate that our system achieves state-of-the-art performance in both tracking precision-improving RMSE by 16.2% compared to ORB-SLAM2 baselines-and reconstruction quality-improving PSNR by 3.93 dB compared to 3DGS-SLAM baselines. The project: https://aczheng-cai.github.io/gsorb-slam.github.io/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。