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.
我们提出了 LiV-GS，一种用于户外环境的 LiDAR-视觉 SLAM 系统，该系统利用 3D 高斯作为可微分的空间表示。值得注意的是，LiV-GS 是首个能够在大规模户外场景中直接对齐离散稀疏的 LiDAR 数据与连续可微的高斯地图的方法，克服了传统 LiDAR 映射中固定分辨率的局限性。 该系统通过共享的协方差属性将点云与高斯地图对齐，用于前端跟踪，并将法向量方向引入损失函数以优化高斯地图。此外，为了可靠且稳定地更新 LiDAR 视场之外的高斯，我们引入了一种新颖的 条件高斯约束，使这些高斯能够与最近的可靠高斯紧密对齐。这种有针对性的调整使 LiV-GS 能够以 7.98 FPS 的速率实现快速且准确的映射和新视图合成。 大量对比实验表明，LiV-GS 在 SLAM、图像渲染和映射方面表现优异。尤其是跨模态雷达-LiDAR 定位的成功，凸显了 LiV-GS 在基于高斯地图的跨模态语义定位和物体分割应用中的潜力。