LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments

TL;DR

LiV-GS introduces a novel LiDAR-visual SLAM system that uses 3D Gaussian maps for accurate, large-scale outdoor scene mapping and view synthesis, outperforming traditional methods.

Contribution

It is the first to directly align sparse LiDAR data with continuous Gaussian maps in outdoor environments, enabling fast and precise SLAM and mapping.

Findings

Achieves 7.98 FPS in mapping and view synthesis.

Outperforms existing SLAM and mapping methods in experiments.

Demonstrates effective cross-modal radar-LiDAR localization.

Abstract

We present LiV-GS, a LiDAR-visual SLAM system in outdoor environments that leverages 3D Gaussian as a differentiable spatial representation. Notably, LiV-GS is the first method that directly aligns discrete and sparse LiDAR data with continuous differentiable Gaussian maps in large-scale outdoor scenes, overcoming the limitation of fixed resolution in traditional LiDAR mapping. The system aligns point clouds with Gaussian maps using shared covariance attributes for front-end tracking and integrates the normal orientation into the loss function to refines the Gaussian map. To reliably and stably update Gaussians outside the LiDAR field of view, we introduce a novel conditional Gaussian constraint that aligns these Gaussians closely with the nearest reliable ones. The targeted adjustment enables LiV-GS to achieve fast and accurate mapping with novel view synthesis at a rate of 7.98 FPS. Extensive comparative experiments demonstrate LiV-GS's superior performance in SLAM, image rendering and mapping. The successful cross-modal radar-LiDAR localization highlights the potential of LiV-GS for applications in cross-modal semantic positioning and object segmentation with Gaussian maps.