VSLAM (visual simultaneous localization and mapping) technology makes rapid progress in recent years. However, most traditional VSLAM systems have poor robustness, while VSLAM based on deep learning have low accuracy. In order to improve the performance of VSLAM systems, an unsupervised deep learning based monocular VSLAM was proposed. The depth estimation network was designed to complete the depth estimation of the image and the pose estimation network was applied to estimate the pose of the camera. Finally, a reasonable loss function was used to ensure the effective convergence of the network during the iterative process. The performance of the system is verified on KITTI data set. The results show that ATE (absolute trajectory error) is reduced by 50% compared to SfMLearner. Compared with traditional VSLAM system, the translation part of APE (absolute pose error) is also significantly reduced and the robustness is also improved.