Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World. (arXiv:2203.05712v1 [cs.CV])

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 …

arxiv cv learning scale virtual virtual world