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

本文介绍了一种改善水下环境中基于学习的单目视觉里程计的方法，通过集成水下光学成像原理来操纵光流估计，从而增强了自主水下航行器的视觉里程计系统的准确性。新的方法wflow-TartanVO利用规范化的介质透射图作为权重图，调整估计的光流以强调降解较小的区域并抑制受水下光散射和吸收影响的不确定区域。wflow-TartanVO不需要对预训练的视觉里程计模型进行微调，因此适用于不同的环境和相机模型。对不同的真实水下数据集的评估表明，wflow-TartanVO相对于基线视觉里程计方法具有更好的性能，体现在绝对轨迹误差（ATE）明显降低。可通过此链接获取实现代码：https://此处为URL。

基于介质透射图的衰减感知加权光流学习的水下地形视觉里程计