  Unsupervised learning for monocular camera motion and 3D scene understanding
has gained popularity over traditional methods, relying on epipolar geometry or
non-linear optimization. Notably, deep learning can overcome many issues of
monocular vision, such as perceptual aliasing, low-textured areas, scale-drift,
and degenerate motions. Also, concerning supervised learning, we can fully
leverage video streams data without the need for depth or motion labels.
However, in this work, we note that rotational motion can limit the accuracy of
the unsupervised pose networks more than the translational component.
Therefore, we present RAUM-VO, an approach based on a model-free epipolar
constraint for frame-to-frame motion estimation (F2F) to adjust the rotation
during training and online inference. To this end, we match 2D keypoints
between consecutive frames using pre-trained deep networks, Superpoint and
Superglue, while training a network for depth and pose estimation using an
unsupervised training protocol. Then, we adjust the predicted rotation with the
motion estimated by F2F using the 2D matches and initializing the solver with
the pose network prediction. Ultimately, RAUM-VO shows a considerable accuracy
improvement compared to other unsupervised pose networks on the KITTI dataset
while reducing the complexity of other hybrid or traditional approaches and
achieving comparable state-of-the-art results.
