  In recent years, unsupervised deep learning approaches have received
significant attention to estimate the depth and visual odometry (VO) from
unlabelled monocular image sequences. However, their performance is limited in
challenging environments due to perceptual degradation, occlusions and rapid
motions. Moreover, the existing unsupervised methods suffer from the lack of
scale-consistency constraints across frames, which causes that the VO
estimators fail to provide persistent trajectories over long sequences. In this
study, we propose an unsupervised monocular deep VO framework that predicts
six-degrees-of-freedom pose camera motion and depth map of the scene from
unlabelled RGB image sequences. We provide detailed quantitative and
qualitative evaluations of the proposed framework on a) a challenging dataset
collected during the DARPA Subterranean challenge; and b) the benchmark KITTI
and Cityscapes datasets. The proposed approach outperforms both traditional and
state-of-the-art unsupervised deep VO methods providing better results for both
pose estimation and depth recovery. The presented approach is part of the
solution used by the COSTAR team participating at the DARPA Subterranean
Challenge.
