The invention discloses a laser monocular vision fusion positioning mapping method in a dynamic scene, and the method comprises the steps: 1, detecting dynamic obstacle information according to point cloud data of a current frame and pose priori information predicted by a visual odometer of a previous frame; 2, forming an image mask on monocular vision 2 with mutually calibrated external parameters, extracting ORB feature points on the image mask and matching the ORB feature points with ORB feature points of a previous frame, estimating the depth of the ORB feature points, obtaining a relative pose through pose calculation, and outputting key frame information meeting requirements; 3, inserting the key frame into the common view, updating the connection relation between the key frame and other key frames, the growth tree and the bag-of-word model according to the common view degree of map points, generating new map points, finding the adjacent key frame according to the essential map of the current key frame, constructing a nonlinear optimization problem, and optimizing the pose and the map points of the key frame; 4, judging whether the similarity between the image data of each key frame and the image data of the current key frame reaches a threshold value or not, if yes, judging that loopback occurs, replacing or filling map points where conflicts exist between the current key frame and the loopback key frame, then connecting the current key frame and the loopback key frame on the essential map, updating the essential map, and finally, carrying out global BA to obtain optimized key frame poses, feature point maps and point cloud maps. According to the invention, the SLAM purpose can be executed in a dynamic scene.