To improve the localization accuracy and robustness of point features-based SLAM algorithm,a monocular visual inertial SLAM algorithm with fast point-line feature fusion was proposed.In the algorithm,high-quality line features were extracted fastly with ELSED (enhanced line segment drawing) algorithm; for non-keyframe tracking,fast line segment matching between consecutive images was implemented based on the assumption of tiny movements between consecutive frames with no line segment descriptors sub required.Line segment descriptors sub was extracted to complete the matching of line features between keyframes when new keyframes were inserted,which created new map lines.Finally,experiments were performed on a public dataset.The results demonstrate that the ELSED takes only 13% of the LSD algorithm while extracting high-quality line segments; compared with the traditional matching algorithm utilizing line segment descriptors,the proposed algorithm improves the time efficiency by 83% and reduces line segment mismatching to enhance the system positioning accuracy;the average tracking frame rate of the system is 33 frame per second,which ensures the real-time performance of the system.