Recent advancements in monocular neural depth estimation, particularly those achieved by the UniDepth network, have prompted the investigation of integrating UniDepth within a Gaussian splatting framework for monocular SLAM.This study presents UDGS-SLAM, a novel approach that eliminates the necessity of RGB-D sensors for depth estimation within Gaussian splatting framework. UDGS-SLAM employs statistical filtering to ensure local consistency of the estimated depth and jointly optimizes camera trajectory and Gaussian scene representation parameters. The proposed method achieves high-fidelity rendered images and low ATERMSE of the camera trajectory. The performance of UDGS-SLAM is rigorously evaluated using the TUM RGB-D dataset and benchmarked against several baseline methods, demonstrating superior performance across various scenarios. Additionally, an ablation study is conducted to validate design choices and investigate the impact of different network backbone encoders on system performance.
最近，单目神经深度估计方面的进展，特别是 UniDepth 网络的成果，促使了在单目 SLAM 中集成 UniDepth 的研究。本研究提出了 UDGS-SLAM，这是一种新颖的方法，消除了在高斯点喷射框架中进行深度估计时对 RGB-D 传感器的需求。UDGS-SLAM 使用统计滤波来确保估计深度的局部一致性，并联合优化相机轨迹和高斯场景表示参数。该方法实现了高保真度的渲染图像和较低的相机轨迹 ATERMSE。UDGS-SLAM 的性能通过 TUM RGB-D 数据集进行了严格评估，并与多个基准方法进行了比较，展示了在各种场景下的优越性能。此外，还进行了消融研究，以验证设计选择并探讨不同网络主干编码器对系统性能的影响。