Autonomous　micro　aerial　vehicles　(MAVs)　equipped　with　onboard　sensors,　are　idea　platforms　for　missions　in　complex　and　confined　environments　for　its　low　cost,　small　size　and　agile　maneuver.　Due　to　the　size,　power,　weight　and　computation　constraints　inherent　in　the　filed　of　MAVs,　monocular　visual-inertial　system　that　consist　of　one　camera　and　an　inertial　measurement　(IMU)　are　the　most　suitable　sensor　suit　for　MAVs.　In　this　paper,　we　proposed　a　monocular　visual-inertial　algorithm　for　estimating　the　state　of　a　MAV.　Firstly,　the　Semi-Direct　Visual　Odometry　(SVO)　algorithm　used　as　the　vision　front-end　of　our　framework　was　modified　so　that　it　can　be　used　for　forward-looking　camera　case.　Second,　an　Error-state　Kalman　Filter　was　designed　so　that　it　can　fuse　the　output　of　the　SVO　and　IMU　data　to　estimate　the　full　state　of　the　MAVs.　We　evaluated　the　proposed　method　with　EuRoc　Dataset　and　compare　the　results　to　the　state-of-the-art　visual-inertial　algorithm,　VINS-Mono.　Experiments　show　that　our　estimator　can　achieve　comparable　accurate　results.　©　The　Authors,　published　by　EDP　Sciences,　2017.