多智能体单目密集SLAM与3D重建先验
Multi-Agent Monocular Dense SLAM With 3D Reconstruction Priors
AI总结：
本文提出首个多智能体单目密集SLAM系统，通过学习3D重建先验提升效率和精度。
摘要：
单目同时定位与建图（SLAM）旨在利用单个摄像头同时估计机器人的位姿并重建未知的3D场景。尽管现有的单目SLAM系统通过密集场景表示生成详细的3D几何结构，但它们由于需要迭代优化而计算成本较高。为了解决这一挑战，MASt3R-SLAM利用学习的3D重建先验，使得3D结构和相机位姿的估计更加高效和准确。然而，MASt3R-SLAM仅限于单智能体操作。在本文中，我们扩展MASt3R-SLAM，引入首个多智能体单目密集SLAM系统。每个智能体使用3D重建先验进行局部SLAM，其个体地图通过基于循环闭合的映射融合机制融合成一个全局一致的地图。我们的方法在与最新方法相比时，提高了计算效率，同时在真实世界数据集上保持了相似的映射精度。
英文摘要：
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.
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。
