VIGS SLAM: IMU-based Large-Scale 3D Gaussian Splatting SLAM

TL;DR

This paper introduces VIGS SLAM, a novel IMU-assisted 3D Gaussian Splatting SLAM system that enables large-scale indoor mapping with improved accuracy and efficiency by integrating sensor fusion and ICP-based tracking.

Contribution

It is the first to demonstrate large-scale Gaussian Splatting SLAM by effectively integrating IMU sensors with RGB-D data for enhanced performance.

Findings

Achieves SLAM performance comparable to state-of-the-art methods in large-scale environments.

Reduces computational load through ICP-based tracking with IMU preintegration.

Extends Gaussian Splatting SLAM capabilities beyond room-scale scenarios.

Abstract

Recently, map representations based on radiance fields such as 3D Gaussian Splatting and NeRF, which excellent for realistic depiction, have attracted considerable attention, leading to attempts to combine them with SLAM. While these approaches can build highly realistic maps, large-scale SLAM still remains a challenge because they require a large number of Gaussian images for mapping and adjacent images as keyframes for tracking. We propose a novel 3D Gaussian Splatting SLAM method, VIGS SLAM, that utilizes sensor fusion of RGB-D and IMU sensors for large-scale indoor environments. To reduce the computational load of 3DGS-based tracking, we adopt an ICP-based tracking framework that combines IMU preintegration to provide a good initial guess for accurate pose estimation. Our proposed method is the first to propose that Gaussian Splatting-based SLAM can be effectively performed in large-scale environments by integrating IMU sensor measurements. This proposal not only enhances the performance of Gaussian Splatting SLAM beyond room-scale scenarios but also achieves SLAM performance comparable to state-of-the-art methods in large-scale indoor environments.