As　a　popular　technology,　visual-inertial　odometry　(VIO)　has　been　widely　applied　in　many　fields　such　as　autonomous　robots　and　unmanned　aerial　vehicle　(UAV).　However,　the　trade-off　between　localization　accuracy　and　real-time　performance　still　needs　to　be　optimized.　This　paper　presents　a　real-time　tightly-coupled　monocular　VIO　system　using　point　and　line　interrelated　features　(PLI-VIO)　under　the　sliding　window　optimization　framework.　In　line　feature　extraction　part　of　PLI-VIO,　a　line　segment　extraction　and　coalescence　algorithm　based　on　EDlines　is　proposed,　which　extracts　line　features　in　real-time　without　concession　on　feature　quality.　At　the　same　time,　in　order　to　get　efficient　and　robust　line　tracking　effect,　PLI-VIO　presents　a　line-to-point　tracking　method　that　fully　utilizes　the　interrelation　between　point　and　line.　Specifically,　line　features　are　divided　as　a　group　of　points　and　tracked　by　pyramidal　implementation　of　Lucas　Kanade　feature　tracker.　The　proposed　line　feature　tracking　method　can　effectively　reduce　time　consumption　on　tracking　process　in　a　robust　way.　Extensive　evaluations　on　Euroc　and　TUM-VI　public　datasets　are　performed　to　demonstrate　the　preferable　performance　of　our　proposed　system,　and　the　results　show　that　PLI-VIO　obtains　better　localization　accuracy　with　less　computation　cost　compared　against　other　state-of-the-art　VIO　algorithms.