融合激光雷达与视觉数据，实现户外大场景高精度3D高斯点云建图。
LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments
- 用可微分高斯表示法直接对齐稀疏激光点云与连续地图。
- 在7.98帧率下完成快速建图与新视角合成。
- 适用于跨模态定位与物体分割的高精度场景理解。
我们提出LiV-GS，一种面向室外环境的激光雷达-视觉SLAM系统，采用3D高斯作为可微分的空间表示。该方法首次在大规模室外场景中直接将离散稀疏的激光雷达数据与连续可微的高斯地图对齐，突破了传统激光雷达建图中固定分辨率的限制。系统通过共享协方差属性实现前端跟踪，并将法向方向融入损失函数以优化高斯地图。为可靠更新激光雷达视域外的高斯点，引入新型条件高斯约束，使其紧密对齐最近可靠的高斯点。该调整使系统在7.98帧率下实现快速精确建图与新视角合成。大量对比实验表明，LiV-GS在SLAM、图像渲染和建图方面均表现优异。跨模态雷达-激光雷达定位的成功验证了其在基于高斯地图的跨模态语义定位与物体分割中的应用潜力。
原文摘要 · Abstract (English)
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.
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