LOG-SLAM: Large-Scale Outdoor Gaussian SLAM for Dense Mapping and Loop Closure in Kilometer-Scale Scene Reconstruction

Abstract

The success of 3D Gaussian splatting in 3D reconstruction has recently led to efforts to integrate it with SLAM systems. However, most existing research has focused on indoor tracking and mapping, while outdoor Gaussian SLAM methods still heavily rely expensive LiDAR sensor. To address these challenges, we propose LOG-SLAM, a novel method for large-scale outdoor tracking and mapping using Gaussian Splatting. Our approach supports tracking through monocular or visual-inertial input, progressively constructing the 3D Gaussian map from depth and pose estimates obtained during the tracking process. Additionally, we introduce a submap-based strategy for managing large-scale maps, enabling the reconstruction of kilometer-scale environments. A loop closure detection module is also incorporated to reduce accumulated errors. Furthermore, we present a novel dynamic object removal method based on rendering loss that mitigates the interference of dynamic objects on the reconstruction. Our experiments on KITTI and KITTI-360 demonstrate that our method achieves localization performance comparable to traditional SLAM systems, while outperforming recent GS/NeRF-based SLAM approaches in terms of mapping and rendering quality.