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}.

该研究论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，名为Multicam-SLAM。该方法通过从多个角度捕捉更全面的空间信息，显著提高了SLAM系统的鲁棒性和准确性。MultiKeyframes结构和并行SLAM线程保证了有效和准确的姿态估计和建图，实验结果证明了该方法相比传统的单相机SLAM系统具有更高的准确性和鲁棒性。

多摄像头间接视觉定位与导航的非重叠多摄像头SLAM