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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.08360 |
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| _version_ | 1866913263990603776 |
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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 |