Many classic visual monocular SLAM (simultaneous localization and mapping) systems have been developed over the past decades, yet most of them fail when dynamic scenarios dominate. DM-SLAM is proposed for handling dynamic objects in environments based on ORB-SLAM2. This article mainly concentrates on two aspects. Firstly, we proposed a distribution and local-based RANSAC (Random Sample Consensus) algorithm (DLRSAC) to extract static features from the dynamic scene based on awareness of the nature difference between motion and static, which is integrated into initialization of DM-SLAM. Secondly, we designed a candidate map points selection mechanism based on neighborhood mutual exclusion to balance the accuracy of tracking camera pose and system robustness in motion scenes. Finally, we conducted experiments in the public dataset and compared DM-SLAM with ORB-SLAM2. The experiments corroborated the superiority of the DM-SLAM.