Multicam-SLAM：面向间接视觉定位与导航的非重叠多相机SLAM
Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation
AI总结：
该研究提出无需视场重叠的Multicam-SLAM方法，通过多相机模型、多关键帧及并行线程实现多RGB-D相机的SLAM，经多环境实验验证其精度与鲁棒性优于传统单相机SLAM，可支撑更复杂的应用。
摘要：
本文提出了一种使用多台RGB-D相机的视觉同步定位与建图（SLAM）新方法。所提出的Multicam-SLAM方法通过从不同视角捕获更全面的空间信息，显著提升了SLAM系统的鲁棒性与精度。该方法无需相机视场重叠，即可准确确定多台相机之间的位姿关系。Multicam-SLAM包含独特的多相机模型、多关键帧结构以及若干并行SLAM线程：多相机模型支持整合多台相机的数据，多关键帧与并行SLAM线程则保证了高效准确的位姿估计与建图。在多种环境中开展的大量实验表明，与传统单相机SLAM系统相比，该方法具备更优的精度与鲁棒性。实验结果凸显了Multicam-SLAM在更复杂、更具挑战性的应用场景中的潜力。相关代码可在https://github.com/AlterPang/Multi_ORB_SLAM 获取。
英文摘要：
This paper presents a novel approach to visual simultaneous localization and mapping (SLAM) using multiple RGB-D cameras. The proposed method, Multicam-SLAM, significantly enhances the robustness and accuracy of SLAM systems by capturing more comprehensive spatial information from various perspectives. This method enables the accurate determination of pose relationships among multiple cameras without the need for overlapping fields of view. The proposed Muticam-SLAM includes a unique multi-camera model, a multi-keyframes structure, and several parallel SLAM threads. The multi-camera model allows for the integration of data from multiple cameras, while the multi-keyframes and parallel SLAM threads ensure efficient and accurate pose estimation and mapping. Extensive experiments in various environments demonstrate the superior accuracy and robustness of the proposed method compared to conventional single-camera SLAM systems. The results highlight the potential of the proposed Multicam-SLAM for more complex and challenging applications. Code is available at \url{https://github.com/AlterPang/Multi_ORB_SLAM}.
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。
