  This paper presents an self-supervised deep learning network for monocular
visual inertial odometry (named DeepVIO). DeepVIO provides absolute trajectory
estimation by directly merging 2D optical flow feature (OFF) and Inertial
Measurement Unit (IMU) data. Specifically, it firstly estimates the depth and
dense 3D point cloud of each scene by using stereo sequences, and then obtains
3D geometric constraints including 3D optical flow and 6-DoF pose as
supervisory signals. Note that such 3D optical flow shows robustness and
accuracy to dynamic objects and textureless environments. In DeepVIO training,
2D optical flow network is constrained by the projection of its corresponding
3D optical flow, and LSTM-style IMU preintegration network and the fusion
network are learned by minimizing the loss functions from ego-motion
constraints. Furthermore, we employ an IMU status update scheme to improve IMU
pose estimation through updating the additional gyroscope and accelerometer
bias. The experimental results on KITTI and EuRoC datasets show that DeepVIO
outperforms state-of-the-art learning based methods in terms of accuracy and
data adaptability. Compared to the traditional methods, DeepVIO reduces the
impacts of inaccurate Camera-IMU calibrations, unsynchronized and missing data.
