MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis

TL;DR

MG-SLAM introduces a Manhattan World-based approach to Gaussian SLAM, improving geometric accuracy, scene completeness, and robustness in indoor environments by integrating line segments and planar assumptions.

Contribution

The paper presents a novel Manhattan Gaussian SLAM system that leverages structured scene cues for enhanced reconstruction quality and robustness in complex indoor environments.

Findings

Achieves state-of-the-art performance on synthetic and real-world scenes.

Improves scene completeness by interpolating missing geometry.

Enhances robustness in textureless indoor areas.

Abstract

Gaussian Splatting SLAMs 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, 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, our method 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.