The invention discloses a graph-based vision SLAM (simultaneous localization and mapping) method. According to the method, the matching relation between an image and visual feature can be obtained based on the natural feature probability vector representation of the image, and the relative pose between two interframes can be calculated by utilizing the space geometry relation of images. Data association of visual odometry is obtained by utilizing the corresponding relation of continuous images, so that all constraints in an image sequence can be obtained. The camera relative pose is taken as a node in a map, the space constrained relation of image interframes is taken as an edge, so that an estimated track map based on the camera relative pose is constructed. Finally, a maximum likelihood method is employed for optimizing the map, and optimized pose estimation is obtained through a random gradient descent method. Related experiments are performed in the laboratory environment based on the provided method, also the moving track of a robot is displayed, and the validity of the algorithm is confirmed.