DGS-SLAM: A Visual Dense SLAM Based on Gaussian Splatting in Dynamic Environments

Abstract

Visual dense SLAM can facilitate pose estimation and map reconstruction for sensor carriers in unknown environments. However, in uncontrolled environments such as offices, shopping malls, and train stations, frequent occurrences of people walking back and forth or temporary movement of objects within the scene are common. Most existing visual dense SLAM systems do not account for these dynamic factors, leading to localization drift and map distortion. In this paper, we propose DGS-SLAM, a system capable of achieving robust localization and high-fidelity static map reconstruction in dynamic environments. We utilize semantic 3D Gaussians for scene representation, effectively eliminating interference from dynamic objects and refining the reconstruction of static background. We enhance the tracking accuracy and mapping quality of dense SLAM by using a distance distribution-based Gaussian pruning algorithm and implementing a coarse-to-fine tracking strategy with bundle adjustment and differentiable rendering. We perform qualitative and quantitative evaluations on two publicly available dynamic environment datasets. The results indicate that our method effectively reduces the interference caused by dynamic objects, enabling visual dense SLAM to maintain competitive tracking accuracy and mapping performance in dynamic environments.