When working in complex scenarios， traditional simultaneous localization and mapping（SLAM） systems cannot distinguish whether the visible objects are moving. Moving objects in the images may lead to wrong feature association， resulting in the inaccuracy of positioning and the deviation of mapping. To improve the robustness and reliability of the SLAM system in dynamic scenarios， an enhanced visual SLAM system with dynamic object perception is proposed in this paper. Firstly， the object detector is used to initially detect the dynamic objects in each image， and then the multi-view geometry method is further used to extract the dynamic regions that cannot be determined by the object detection. The robustness of the system is improved by eliminating feature points belonging to dynamic objects. The proposed method is tested in public datasets TUM and KITTI. The results show that the localization accuracy of the proposed method in dynamic scenes has been significantly improved， especially in high dynamic sequences. Compared with the original algorithm， the accuracy is improved by more than 92%. Compared with other SLAM systems in dynamic scenarios， the proposed method not only maintains the accuracy advantage， but also improves the stability of running results and time efficiency.