  Multi-view geometry-based methods dominate the last few decades in monocular
Visual Odometry for their superior performance, while they have been vulnerable
to dynamic and low-texture scenes. More importantly, monocular methods suffer
from scale-drift issue, i.e., errors accumulate over time. Recent studies show
that deep neural networks can learn scene depths and relative camera in a
self-supervised manner without acquiring ground truth labels. More
surprisingly, they show that the well-trained networks enable scale-consistent
predictions over long videos, while the accuracy is still inferior to
traditional methods because of ignoring geometric information. Building on top
of recent progress in computer vision, we design a simple yet robust VO system
by integrating multi-view geometry and deep learning on Depth and optical Flow,
namely DF-VO. In this work, a) we propose a method to carefully sample
high-quality correspondences from deep flows and recover accurate camera poses
with a geometric module; b) we address the scale-drift issue by aligning
geometrically triangulated depths to the scale-consistent deep depths, where
the dynamic scenes are taken into account. Comprehensive ablation studies show
the effectiveness of the proposed method, and extensive evaluation results show
the state-of-the-art performance of our system, e.g., Ours (1.652%) v.s.
ORB-SLAM (3.247%}) in terms of translation error in KITTI Odometry benchmark.
Source code is publicly available at:
\href{https://github.com/Huangying-Zhan/DF-VO}{DF-VO}.
