This paper focuses on comparing two visual Simultaneous Localization and Mapping (vSLAM) algorithms: ORB-SLAM3 and DynaSLAM, utilizing simulations with a monocular camera. ORB-SLAM3 is an open-source library known for its monocular vSLAM capabilities and is an evolution of the well-regarded ORB-SLAM2. In contrast, DynaSLAM extends ORB-SLAM2 by incorporating Mask R-CNN for dynamic object detection, filtering, and segmentation. Both algorithms were tested and evaluated using sequences of monocular images from two popular datasets, KITTI and TUM RGB-D. The experiments demonstrate the efficiency of the vSLAM algorithms. The results reveal that DynaSLAM consistently outperforms ORB-SLAM3 in the majority of cases. Overall, this research contributes to the understanding of these vSLAM methods, providing insights into their performance and highlighting the advantages of DynaSLAM over ORB-SLAM3 in various scenarios.