Aiming at the problem that maps may be not consistent caused by accumulation of errors in visual Simultaneous Location and Mapping (SLAM), a Visual SLAM (V-SLAM) system based on improved closed-loop detection algorithm was proposed. To reduce the cumulative error caused by long operation of mobile robots, an improved closed-loop detection algorithm was introduced. By improving the similarity score function, the perceived ambiguity was reduced and finally the closed-loop recognition rate was improved. At the same time, to reduce the computational complexity, the environment image and depth information were directly obtained by Kinect, and feature extraction and matching was carried out by using small and robust ORB (Oriented FAST and Rotated BRIEF) features. RANdom SAmple Consensus (RANSAC) algorithm was used to delete mismatching pairs to obtain more accurate matching pairs, and then the camera poses were calculated by PnP. More stable and accurate initial estimation poses are critical to back-end processing, which were attained by g2o to carry on unstructured iterative optimization for camera poses. Finally, in the back-end Bundle Adjustment (BA) was used as the core of the map optimization method to optimize poses and road signs. The experimental results show that the system can meet the real-time requirements, and can obtain more accurate pose estimation.