多智能体协同3D高斯点云建图，实现大场景高精度定位与渲染。
GRAND-SLAM: Local Optimization for Globally Consistent Large-Scale Multi-Agent Gaussian SLAM
- 基于子地图局部优化的隐式跟踪，提升定位稳定性。
- 在室外大场景数据集上追踪误差降低91%，渲染效果更优。
- 适合需要快速大范围建图的多机器人系统应用。
3D高斯点阵已成为RGB-D视觉SLAM中一种高效的场景表示方法，但其在大规模多智能体室外环境中的应用尚未探索。多智能体高斯SLAM有望实现环境的快速探索与重建，提供可扩展的场景表示，但现有方法仅限于小规模室内环境。为此，我们提出基于多智能体密集SLAM的高斯重建方法GRAND-SLAM，集成：i）基于子地图局部优化的隐式跟踪模块；ii）融入位姿图优化框架的跨机器人与机器人内部回环检测方法。实验表明，GRAND-SLAM在Replica室内数据集上达到领先追踪性能，相比现有方法PSNR提升28%；在大规模室外Kimera-Multi数据集上，多智能体追踪误差降低91%，渲染质量显著优于现有方法。
原文摘要 · Abstract (English)
3D Gaussian splatting has emerged as an expressive scene representation for RGB-D visual SLAM, but its application to large-scale, multi-agent outdoor environments remains unexplored. Multi-agent Gaussian SLAM is a promising approach to rapid exploration and reconstruction of environments, offering scalable environment representations, but existing approaches are limited to small-scale, indoor environments. To that end, we propose Gaussian Reconstruction via Multi-Agent Dense SLAM, or GRAND-SLAM, a collaborative Gaussian splatting SLAM method that integrates i) an implicit tracking module based on local optimization over submaps and ii) an approach to inter- and intra-robot loop closure integrated into a pose-graph optimization framework. Experiments show that GRAND-SLAM provides state-of-the-art tracking performance and 28% higher PSNR than existing methods on the Replica indoor dataset, as well as 91% lower multi-agent tracking error and improved rendering over existing multi-agent methods on the large-scale, outdoor Kimera-Multi dataset.
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