  In the last decade, numerous supervised deep learning approaches requiring
large amounts of labeled data have been proposed for visual-inertial odometry
(VIO) and depth map estimation. To overcome the data limitation,
self-supervised learning has emerged as a promising alternative, exploiting
constraints such as geometric and photometric consistency in the scene. In this
study, we introduce a novel self-supervised deep learning-based VIO and depth
map recovery approach (SelfVIO) using adversarial training and self-adaptive
visual-inertial sensor fusion. SelfVIO learns to jointly estimate 6
degrees-of-freedom (6-DoF) ego-motion and a depth map of the scene from
unlabeled monocular RGB image sequences and inertial measurement unit (IMU)
readings. The proposed approach is able to perform VIO without the need for IMU
intrinsic parameters and/or the extrinsic calibration between the IMU and the
camera. estimation and single-view depth recovery network. We provide
comprehensive quantitative and qualitative evaluations of the proposed
framework comparing its performance with state-of-the-art VIO, VO, and visual
simultaneous localization and mapping (VSLAM) approaches on the KITTI, EuRoC
and Cityscapes datasets. Detailed comparisons prove that SelfVIO outperforms
state-of-the-art VIO approaches in terms of pose estimation and depth recovery,
making it a promising approach among existing methods in the literature.
