TAMBRIDGE：连接以帧为中心的跟踪与3D高斯泼溅以增强SLAM
TAMBRIDGE: Bridging Frame-Centered Tracking and 3D Gaussian Splatting for Enhanced SLAM
AI总结：
针对3DGS在SLAM中对模糊和噪声鲁棒性差且实时性低的问题，提出基于3DGS的TAMBRIDGE系统，通过Fusion Bridge模块融合ORB视觉里程计与在线3DGS，实现精确位姿初始化与策略性视图选择，显著提升了大规模场景的渲染收敛、质量与定位精度。
摘要：
3D高斯泼溅（3DGS）对运动模糊和相机噪声的鲁棒性有限，且其实时性能较差，这限制了其在机器人SLAM任务中的应用。经分析，这些问题的主要原因是存在运动模糊的视图密度，以及基于含噪原始图像和渲染结果计算损失所导致的密集位姿估计中的累积误差，这些因素增加了3DGS渲染收敛的难度。因此，我们引入了一种先进的基于3DGS的SLAM系统，该系统利用3DGS的高效性和灵活性来实现实时性能，同时对传感器噪声、运动模糊以及长时SLAM带来的挑战保持鲁棒性。该方法的核心是Fusion Bridge模块，它将跟踪为中心的ORB视觉里程计与建图为中心的在线3DGS无缝集成。该模块通过对重投影和渲染损失进行联合优化以及策略性视图选择，实现了精确的位姿初始化，从而增强了大规模场景中的渲染收敛。大量实验表明，该系统具有最先进的渲染质量和定位精度，使其成为需要稳定、近乎实时性能的现实世界机器人应用的一种极具前景的解决方案。我们的项目可在https://ZeldaFromHeaven.github.io/TAMBRIDGE/获取。
英文摘要：
The limited robustness of 3D Gaussian Splatting (3DGS) to motion blur and camera noise, along with its poor real-time performance, restricts its application in robotic SLAM tasks. Upon analysis, the primary causes of these issues are the density of views with motion blur and the cumulative errors in dense pose estimation from calculating losses based on noisy original images and rendering results, which increase the difficulty of 3DGS rendering convergence. Thus, a cutting-edge 3DGS-based SLAM system is introduced, leveraging the efficiency and flexibility of 3DGS to achieve real-time performance while remaining robust against sensor noise, motion blur, and the challenges posed by long-session SLAM. Central to this approach is the Fusion Bridge module, which seamlessly integrates tracking-centered ORB Visual Odometry with mapping-centered online 3DGS. Precise pose initialization is enabled by this module through joint optimization of re-projection and rendering loss, as well as strategic view selection, enhancing rendering convergence in large-scale scenes. Extensive experiments demonstrate state-of-the-art rendering quality and localization accuracy, positioning this system as a promising solution for real-world robotics applications that require stable, near-real-time performance. Our project is available at https://ZeldaFromHeaven.github.io/TAMBRIDGE/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
