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Main Authors: Peng, Luyuan, Vishnu, Hari, Chitre, Mandar, Too, Yuen Min, Kalyan, Bharath, Mishra, Rajat
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2403.08360
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author Peng, Luyuan
Vishnu, Hari
Chitre, Mandar
Too, Yuen Min
Kalyan, Bharath
Mishra, Rajat
author_facet Peng, Luyuan
Vishnu, Hari
Chitre, Mandar
Too, Yuen Min
Kalyan, Bharath
Mishra, Rajat
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2403_08360
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Improved Image-based Pose Regressor Models for Underwater Environments
Peng, Luyuan
Vishnu, Hari
Chitre, Mandar
Too, Yuen Min
Kalyan, Bharath
Mishra, Rajat
Computer Vision and Pattern Recognition
Robotics
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.
title Improved Image-based Pose Regressor Models for Underwater Environments
topic Computer Vision and Pattern Recognition
Robotics
url https://arxiv.org/abs/2403.08360