Visual-inertial odometry (VIO) is the process of estimating ego-motion using a camera and the inertial measurement unit (IMU). It shows outstanding performance in estimating the ego-motion of a vehicle at the absolute scale thanks to the gyroscope and the accelerometer. However, VIO has some difficulties in estimating the translation in large-scale outdoor environments because the feature points along the motion direction and distant feature points in the images can cause degenerate situations. To resolve these difficulties, the authors propose to infer the confidence measures of the feature points and appropriately incorporate them into the Kalman filter-based VIO. The confidence is computed from the motion direction and the displacements of the tracked feature points under authors' urban canyon prior. It is applied in situations where the camera is moving forward towards the measurement noise covariance of the Kalman filter for ego-motion estimation. Experimental results on the public KITTI dataset show that VIO outperforms monocular and stereo-visual odometries, and the proposed VIO with confidence analysis achieves a translation error of 1.82% and a rotation error of 0.0018 deg/m.