We investigate the performance of image-based pose regressor models in underwater environments for relocalization. Leveraging PoseNet and PoseLSTM, we regress a 6-degree-of-freedom pose from single RGB images with high accuracy. Additionally, we explore data augmentation with stereo camera images to improve model accuracy. Experimental results demonstrate that the models achieve high accuracy in both simulated and clear waters, promising effective real-world underwater navigation and inspection applications.

翻译：我们研究了基于图像的位姿回归模型在水下环境中的重定位性能。利用PoseNet和PoseLSTM，我们从单张RGB图像中以高精度回归出六自由度位姿。此外，我们探索了利用立体相机图像进行数据增强以提升模型精度。实验结果表明，在模拟水域和清水环境中，这些模型均能达到较高精度，有望有效应用于实际水下导航与检测场景。