LiV-GS：面向室外环境的激光雷达-视觉融合三维高斯溅射SLAM
LiV-GS: LiDAR-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments
AI总结：
LiV-GS提出一种室外激光雷达-视觉SLAM系统，首次直接对齐稀疏激光雷达数据与可微分三维高斯地图，并通过条件高斯约束实现视场外更新，以7.98 FPS实现高精度建图与新视角合成。
摘要：
我们提出LiV-GS，一种面向室外环境的激光雷达-视觉SLAM系统，利用三维高斯作为可微分的空间表示。值得注意的是，LiV-GS是首个在大规模室外场景中直接对齐离散稀疏激光雷达数据与连续可微分高斯地图的方法，克服了传统激光雷达建图中固定分辨率的局限。该系统利用共享协方差属性将点云与高斯地图对齐以进行前端跟踪，并将法线方向融入损失函数以优化高斯地图。为了在激光雷达视场之外可靠且稳定地更新高斯，我们引入了一种新颖的条件高斯约束，使这些高斯与最近邻的可靠高斯紧密对齐。该针对性调整使LiV-GS能够以7.98 FPS的速率实现快速准确的建图和新视角合成。大量对比实验表明，LiV-GS在SLAM、图像渲染和建图方面具有优越性能。跨模态雷达-激光雷达定位的成功凸显了LiV-GS在基于高斯地图的跨模态语义定位和目标分割应用中的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
