  Visual Odometry (VO) is vital for the navigation of autonomous systems,
providing accurate position and orientation estimates at reasonable costs.
While traditional VO methods excel in some conditions, they struggle with
challenges like variable lighting and motion blur. Deep learning-based VO,
though more adaptable, can face generalization problems in new environments.
Addressing these drawbacks, this paper presents a novel hybrid visual odometry
(VO) framework that leverages pose-only supervision, offering a balanced
solution between robustness and the need for extensive labeling. We propose two
cost-effective and innovative designs: a self-supervised homographic
pre-training for enhancing optical flow learning from pose-only labels and a
random patch-based salient point detection strategy for more accurate optical
flow patch extraction. These designs eliminate the need for dense optical flow
labels for training and significantly improve the generalization capability of
the system in diverse and challenging environments. Our pose-only supervised
method achieves competitive performance on standard datasets and greater
robustness and generalization ability in extreme and unseen scenarios, even
compared to dense optical flow-supervised state-of-the-art methods.
