  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/
