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/

翻译：3D高斯溅射（3DGS）对运动模糊和相机噪声的鲁棒性有限，加之其实时性能较差，限制了其在机器人SLAM任务中的应用。经分析，这些问题的主要成因在于存在大量带有运动模糊的视角，以及基于含噪原始图像和渲染结果计算损失时产生的密集位姿估计累积误差，这些因素增加了3DGS渲染收敛的难度。为此，本文引入了一种基于3DGS的尖端SLAM系统，它利用3DGS的高效性和灵活性来实现实时性能，同时保持对传感器噪声、运动模糊以及长时程SLAM所带来挑战的鲁棒性。该方法的核心理念是融合桥接模块，该模块无缝集成了以跟踪为中心的ORB视觉里程计和以建图为中心的在线3DGS。该模块通过重投影损失与渲染损失的联合优化以及策略性的视角选择，实现了精确的位姿初始化，从而提升了大规模场景中的渲染收敛性。大量实验证明了该系统在渲染质量和定位精度方面达到了最先进水平，使其成为需要稳定、近实时性能的实际机器人应用的一个有前景的解决方案。我们的项目发布于 https://ZeldaFromHeaven.github.io/TAMBRIDGE/