Rad-GS：用于户外环境的雷达-视觉融合3D高斯点云SLAM
Rad-GS: Radar-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments
AI总结：
Rad-GS通过融合雷达与视觉信息，实现公里级户外环境的3D高斯点云SLAM，提升定位精度与场景重建能力。
摘要：
我们提出了Rad-GS，一种用于公里级户外环境的4D雷达-相机SLAM系统，利用3D高斯作为可微分的空间表示。Rad-GS结合了原始雷达点云与多普勒信息以及几何增强的点云，以指导同步图像中的动态物体遮挡，从而缓解渲染伪影并提高定位精度。此外，未同步的图像帧被用来全局优化3D高斯表示，增强纹理一致性和新视角合成的保真度。进一步地，全局八叉树结构与针对高斯体素的管理策略进一步抑制噪声并在大规模环境中显著减少内存消耗。广泛的实验和消融研究证明，Rad-GS在性能上与基于相机或LiDAR的3D高斯方法相当，突显了使用4D毫米波雷达进行稳健户外测绘的可行性。现实世界中公里级的重建验证了Rad-GS在大规模场景重建中的潜力。
英文摘要：
We present Rad-GS, a 4D radar-camera SLAM system designed for kilometer-scale outdoor environments, utilizing 3D Gaussian as a differentiable spatial representation. Rad-GS combines the advantages of raw radar point cloud with Doppler information and geometrically enhanced point cloud to guide dynamic object masking in synchronized images, thereby alleviating rendering artifacts and improving localization accuracy. Additionally, unsynchronized image frames are leveraged to globally refine the 3D Gaussian representation, enhancing texture consistency and novel view synthesis fidelity. Furthermore, the global octree structure coupled with a targeted Gaussian primitive management strategy further suppresses noise and significantly reduces memory consumption in large-scale environments. Extensive experiments and ablation studies demonstrate that Rad-GS achieves performance comparable to traditional 3D Gaussian methods based on camera or LiDAR inputs, highlighting the feasibility of robust outdoor mapping using 4D mmWave radar. Real-world reconstruction at kilometer scale validates the potential of Rad-GS for large-scale scene reconstruction.
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