  This paper addresses the challenge of improving learning-based monocular
visual odometry (VO) in underwater environments by integrating principles of
underwater optical imaging to manipulate optical flow estimation. Leveraging
the inherent properties of underwater imaging, the novel wflow-TartanVO is
introduced, enhancing the accuracy of VO systems for autonomous underwater
vehicles (AUVs). The proposed method utilizes a normalized medium transmission
map as a weight map to adjust the estimated optical flow for emphasizing
regions with lower degradation and suppressing uncertain regions affected by
underwater light scattering and absorption. wflow-TartanVO does not require
fine-tuning of pre-trained VO models, thus promoting its adaptability to
different environments and camera models. Evaluation of different real-world
underwater datasets demonstrates the outperformance of wflow-TartanVO over
baseline VO methods, as evidenced by the considerably reduced Absolute
Trajectory Error (ATE). The implementation code is available at:
https://github.com/bachzz/wflow-TartanVO
