The　outdoor　images　collected　by　monocular　camera　are　easily　affected　by　the　light　intensity.　The　scale　of　images　is　ambiguous.　In　addition,　the　pose　estimation　of　convolution　neural　networks　(CNNs)　is　not　accurate.　To　address　these　issues,　a　monocular　vision-inertial　odometry　using　CNN　and　the　extended　Kalman　filter　(EKF)　is　proposed.　The　CNN　is　used　to　replace　the　conventional　odometry　of　the　front-end　vision　based　geometric　constraints.　The　output　of　the　monocular　camera　is　used　as　the　EKF　measurement　to　correct　the　estimated　pose　of　CNN.　The　error　covariance　of　EKF　is　optimized　by　the　CNN.　The　monocular　camera　pose　data　and　the　inertial　measurement　unit　(IMU)　data　are　fused　in　EKF　to　estimate　the　motion　pose.　The　monocular　scale　informations　and　the　cumulative　errors　of　the　IMU　are　compensated.　Experimental　results　show　that　the　proposed　algorithm　performs　more　precise　pose　estimation.　The　accuracy　and　feasibility　of　the　algorithm　are　verified.　Compared　with　the　Depth-VO-Feat　algorithm　that　relies　on　monocular　images,　the　proposed　algorithm　combines　monocular　image　and　IMU　data　for　pose　estimation.　The　translation　and　rotation　errors　of　the　09　sequence　in　KITTI　dataset　are　reduced　by　45.4%　and　47.8%,　respectively.　The　translation　and　rotation　errors　of　10　sequences　are　reduced　by　68.1%　and　43.4%,　respectively.　©　2021,　Science　Press.　All　right　reserved.