  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}.
