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.
@article{arxiv.2403.08360,
title = {Improved Image-based Pose Regressor Models for Underwater Environments},
author = {Luyuan Peng and Hari Vishnu and Mandar Chitre and Yuen Min Too and Bharath Kalyan and Rajat Mishra},
journal= {arXiv preprint arXiv:2403.08360},
year = {2024}
}