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 visioninertial 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.