Point　features　are　predominantly　used　in　Visual　Simultaneous　Localization　and　Mapping(VSLAM).　However,　only　using　point　features　usually　lacks　the　robustness　in　the　scenes　such　as　low　texture,　illumination　variance,　and　structured　environments.　To　overcome　the　above　limitations,　line　features　were　actively　employed　in　previous　studies.　But　not　all　line　features　are　helpful　for　improving　the　robustness　of　SLAM,　since　they　are　easily　affected　by　image　noise　and　feature　detection　strategies.　Meanwhile,　line　features　are　easier　to　degenerate　than　point　features　due　to　parallel　to　epipolar　lines.　In　this　paper,　we　proposed　a　monocular　visual　SLAM　using　robust　line　features.　First,　a　Robust　Line　Segment　Detector　(RLSD)　algorithm　is　proposed　to　reduce　the　influence　of　noise　and　outliers　by　using　gradient　operators,　length　suppression,　and　line　segment　fusion.　And　a　line　feature　matching　method　based　on　optical-flow　is　employed　to　reduce　the　instability　in　similar　and　structured　environments.　Then,　a　direction　constraint　strategy　is　proposed　to　solve　the　problem　of　triangulation　failure　due　to　degradation,　which　is　conducive　to　subsequent　mapping　and　tracking.　Finally,　a　Visual-Inertial　Odometry(VIO)　with　combined　point-line　features　is　implemented.　Experimental　results　on　the　EuRoC　dataset　demonstrate　that　our　proposed　system　outperforms　other　state-of-the-art　methods.