  Monocular visual odometry (VO) has attracted extensive research attention by
providing real-time vehicle motion from cost-effective camera images. However,
state-of-the-art optimization-based monocular VO methods suffer from the scale
inconsistency problem for long-term predictions. Deep learning has recently
been introduced to address this issue by leveraging stereo sequences or
ground-truth motions in the training dataset. However, it comes at an
additional cost for data collection, and such training data may not be
available in all datasets. In this work, we propose VRVO, a novel framework for
retrieving the absolute scale from virtual data that can be easily obtained
from modern simulation environments, whereas in the real domain no stereo or
ground-truth data are required in either the training or inference phases.
Specifically, we first train a scale-aware disparity network using both
monocular real images and stereo virtual data. The virtual-to-real domain gap
is bridged by using an adversarial training strategy to map images from both
domains into a shared feature space. The resulting scale-consistent disparities
are then integrated with a direct VO system by constructing a virtual stereo
objective that ensures the scale consistency over long trajectories.
Additionally, to address the suboptimality issue caused by the separate
optimization backend and the learning process, we further propose a mutual
reinforcement pipeline that allows bidirectional information flow between
learning and optimization, which boosts the robustness and accuracy of each
other. We demonstrate the effectiveness of our framework on the KITTI and
vKITTI2 datasets.
