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.

翻译：高斯SLAM系统在提升实时重建效率与保真度方面取得了显著进展。然而，这些系统在复杂室内环境中常面临重建不完整的问题，主要表现为因障碍物或视角受限导致的未观测几何结构所形成的大面积空洞。为应对这一挑战，我们提出曼哈顿高斯SLAM（MG-SLAM），这是一个利用曼哈顿世界假设来提升几何精度与完整性的RGB-D系统。通过无缝集成从结构化场景提取的融合线段，MG-SLAM确保了在无纹理室内区域的鲁棒跟踪。此外，提取的线段与平面表面假设允许在缺失几何区域对新高斯点进行策略性插值，从而实现高效场景补全。在合成场景与真实场景上进行的大量实验表明，这些改进使我们的方法达到了最先进的性能水平，标志着高斯SLAM系统能力的重要提升。