Robust Gaussian Splatting SLAM by Leveraging Loop Closure

TL;DR

This paper introduces a robust Gaussian Splatting SLAM system that effectively leverages loop closure and multiple RGB-D cameras to improve localization accuracy and rendering quality, addressing drift issues in traditional methods.

Contribution

The paper proposes a novel Gaussian Splatting SLAM architecture with a loop closure module and pose optimization techniques tailored for rotating RGB-D camera setups, enhancing accuracy and robustness.

Findings

Outperforms state-of-the-art in pose estimation

Achieves high-quality photorealistic rendering

Effectively reduces pose drift in experiments

Abstract

3D Gaussian Splatting algorithms excel in novel view rendering applications and have been adapted to extend the capabilities of traditional SLAM systems. However, current Gaussian Splatting SLAM methods, designed mainly for hand-held RGB or RGB-D sensors, struggle with tracking drifts when used with rotating RGB-D camera setups. In this paper, we propose a robust Gaussian Splatting SLAM architecture that utilizes inputs from rotating multiple RGB-D cameras to achieve accurate localization and photorealistic rendering performance. The carefully designed Gaussian Splatting Loop Closure module effectively addresses the issue of accumulated tracking and mapping errors found in conventional Gaussian Splatting SLAM systems. First, each Gaussian is associated with an anchor frame and categorized as historical or novel based on its timestamp. By rendering different types of Gaussians at the same viewpoint, the proposed loop detection strategy considers both co-visibility relationships and distinct rendering outcomes. Furthermore, a loop closure optimization approach is proposed to remove camera pose drift and maintain the high quality of 3D Gaussian models. The approach uses a lightweight pose graph optimization algorithm to correct pose drift and updates Gaussians based on the optimized poses. Additionally, a bundle adjustment scheme further refines camera poses using photometric and geometric constraints, ultimately enhancing the global consistency of scenarios. Quantitative and qualitative evaluations on both synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art methods in camera pose estimation and novel view rendering tasks. The code will be open-sourced for the community.