Aiming at the problem that the simultaneous localization and mapping ( SLAM) algorithm has a large pose error and inconsistent map construction when a moving object appears in mobile robot’ s operating scene, an improved visual SLAM algorithm based on feature point motion vector is proposed. Firstly, the algorithm of motion points based on feature point motion vector is introduced. The motion vector can be calculated by combining the initial camera pose, and the Gaussian mixture model parameters of its angle are solved by using the expectation maximization method. And the motion point detection result of the previous frame is used to distinguish motion points in the current image. Secondly, the camera pose will be optimized based on results of the motion point detection. Then the image is pre-processed, and images with a number of motion points and higher similarity to the previous frame will be eliminated, which can improve the calculation efficiency of loop closure detection. Finally, the scene is described by using feature points after excluding dynamic features, and the similarity score calculation function of two images at a single node is improved. After loop closure confirmation, the correct loop is obtained. The datasets experimental results show that the improved algorithm has better robustness and higher accuracy in the pose estimation. And it can effectively detect the existence of loops in the scene and has a good mapping.