  Gaussian SLAM systems have made significant advancements in improving the
efficiency and fidelity of real-time reconstructions. However, these systems
often encounter incomplete reconstructions in complex indoor environments,
characterized by substantial holes due to unobserved geometry caused by
obstacles or limited view angles. To address this challenge, we present
Manhattan Gaussian SLAM (MG-SLAM), an RGB-D system that leverages the Manhattan
World hypothesis to enhance geometric accuracy and completeness. By seamlessly
integrating fused line segments derived from structured scenes, MG-SLAM ensures
robust tracking in textureless indoor areas. Moreover, The extracted lines and
planar surface assumption allow strategic interpolation of new Gaussians in
regions of missing geometry, enabling efficient scene completion. Extensive
experiments conducted on both synthetic and real-world scenes demonstrate that
these advancements enable our method to achieve state-of-the-art performance,
marking a substantial improvement in the capabilities of Gaussian SLAM systems.
