A synchronous positioning and mapping method combining semantic information based on monocular vision and lidar was proposed.The pose data relative to the keyframe were obtained based on the features extracted from the monocular vision and the correspondence in the lidar depth map,and the image was semantically segmented. Semantic information was refined by the semantic features obtained from deep convolutional neural network (CNN),and each point in the map was associated with a semantic feature to perform local and global pose optimization by semantically guiding.The coupling of proposed semantic markup and SLAM is robust and accurate. Mobile robots equipped with monocular vision and lidar were tested in indoor environments. The experimental results show that the proposed method can improve the navigation accuracy of robot,realize intelligent autonomous navigation of robot, and also provide image data with semantic information.