Improved Image-based Pose Regressor Models for Underwater Environments
This work addresses underwater navigation and inspection applications, but appears incremental as it builds on existing models like PoseNet and PoseLSTM.
The paper tackles the problem of underwater relocalization by improving image-based pose regressor models, achieving high accuracy in both simulated and clear waters with experimental results.
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.