GRAND-SLAM：全局一致的大规模多智能体高斯SLAM的局部优化
GRAND-SLAM: Local Optimization for Globally Consistent Large-Scale Multi-Agent Gaussian SLAM
AI总结：
GRAND-SLAM提出一种协作式高斯泼溅SLAM方法，通过局部优化和回环检测实现大规模多智能体室外重建，在Replica和Kimera-Multi数据集上显著提升跟踪精度与渲染质量。
摘要：
3D高斯泼溅已成为RGB-D视觉SLAM的一种富有表现力的场景表示，但其在大规模、多智能体室外环境中的应用仍未得到探索。多智能体高斯SLAM是一种快速探索和重建环境的有前景的方法，提供可扩展的环境表示，但现有方法仅限于小规模室内环境。为此，我们提出了通过多智能体稠密SLAM进行高斯重建的方法，即GRAND-SLAM，一种协作式高斯泼溅SLAM方法，它整合了i）基于子图局部优化的隐式跟踪模块，以及ii）集成到位姿图优化框架中的机器间和机器内回环检测方法。实验表明，GRAND-SLAM在Replica室内数据集上提供了最先进的跟踪性能，PSNR比现有方法高28%，在大型室外Kimera-Multi数据集上，多智能体跟踪误差比现有多智能体方法低91%，并改善了渲染效果。
英文摘要：
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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