多智能体协同实现高效单目稠密建图，提升实时性与一致性。
Multi-Agent Monocular Dense SLAM With 3D Reconstruction Priors
- 各智能体用预训练3D重建先验进行局部建图，降低计算开销。
- 通过基于回环检测的地图融合机制，实现全局一致的联合建图。
- 在真实数据集上比现有方法更高效，精度相当，适合多机器人场景。
单目同步定位与建图（SLAM）旨在仅使用单个摄像头同时估计机器人位姿并重建未知三维场景。尽管现有单目SLAM系统通过稠密场景表示生成精细三维结构，但其依赖迭代优化导致计算成本高昂。为应对这一挑战，MASt3R-SLAM采用学习得到的3D重建先验，实现更高效、更精确的三维结构与相机位姿估计。然而，该方法仅支持单智能体运行。本文将MASt3R-SLAM拓展为首个多智能体单目稠密SLAM系统：每个智能体利用3D重建先验执行本地SLAM，再通过基于回环检测的地图融合机制将个体地图合并为全局一致地图。在真实世界数据集上的评估表明，该方法相比当前最优方法显著提升计算效率，同时保持相近的建图精度。
原文摘要 · Abstract (English)
Monocular Simultaneous Localization and Mapping (SLAM) aims to estimate a robot's pose while simultaneously reconstructing an unknown 3D scene using a single camera. While existing monocular SLAM systems generate detailed 3D geometry through dense scene representations, they are computationally expensive due to the need for iterative optimization. To address this challenge, MASt3R-SLAM utilizes learned 3D reconstruction priors, enabling more efficient and accurate estimation of both 3D structures and camera poses. However, MASt3R-SLAM is limited to single-agent operation. In this paper, we extend MASt3R-SLAM to introduce the first multi-agent monocular dense SLAM system. Each agent performs local SLAM using a 3D reconstruction prior, and their individual maps are fused into a globally consistent map through a loop-closure-based map fusion mechanism. Our approach improves computational efficiency compared to state-of-the-art methods, while maintaining similar mapping accuracy when evaluated on real-world datasets.
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