A Lightweight Learning-based Visual-Inertial Odometry

In this paper, we propose a learning-based lightweight visual-inertial odometry (VIO) based on an uncertainty-aware pose network and an extended Kalman filter (EKF). The pose network serving as the VIO vision front-end predicts the relative motion of the camera between consecutive image frames and estimates the prediction uncertainty. The training of the pose network can be conducted without requiring ground-truth labels. The distributions of visual measurements are fused with inertial measurements by an EKF that is the VIO back-end. Evaluations show that the proposed VIO fails to outperform a state-of-the-art feature-point-based VIO solution in accuracy. But it has high time efficiency, translational motion estimation with metric scale, estimation of gravity direction, and generalization to new environments. So, unlike most works on learning-based visual ego-motion estimation in the literature, the proposed VIO can be directly deployed on an MAV. The comparative studies of supervision signals and forms of translational motion prediction provide insights that can contribute to future research.