Improved Image-based Pose Regressor Models for Underwater Environments
Luyuan Peng Hari Vishnu Mandar Chitre Yuen Min Too Bharath Kalyan Rajat Mishra
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.