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}.

翻译：本文提出了一种使用多个RGB-D相机进行视觉同时定位与建图（SLAM）的新方法。所提出的方法——Multicam-SLAM——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。该方法能够准确确定多个相机之间的位姿关系，而无需重叠的视场。所提出的Multicam-SLAM包含一个独特的多相机模型、一个多关键帧结构以及多个并行的SLAM线程。多相机模型允许融合来自多个相机的数据，而多关键帧与并行SLAM线程则确保了高效且准确的位姿估计与地图构建。在不同环境中的大量实验证明，与传统的单相机SLAM系统相比，所提方法具有更优的精度与鲁棒性。结果凸显了所提出的Multicam-SLAM在更复杂、更具挑战性应用中的潜力。代码发布于 \url{https://github.com/AlterPang/Multi_ORB_SLAM}。