This paper delves into the potential of DU-VIO, a dehazing-aided hybrid multi-rate multi-modal Visual-Inertial Odometry (VIO) estimation framework, designed to thrive in the challenging realm of extreme underwater environments. The cutting-edge DU-VIO framework is incorporating a GAN-based pre-processing module and a hybrid CNN-LSTM module for precise pose estimation, using visibility-enhanced underwater images and raw IMU data. Accurate pose estimation is paramount for various underwater robotics and exploration applications. However, underwater visibility is often compromised by suspended particles and attenuation effects, rendering visual-inertial pose estimation a formidable challenge. DU-VIO aims to overcome these limitations by effectively removing visual disturbances from raw image data, enhancing the quality of image features used for pose estimation. We demonstrate the effectiveness of DU-VIO by calculating RMSE scores for translation and rotation vectors in comparison to their reference values. These scores are then compared to those of a base model using a modified AQUALOC Dataset. This study's significance lies in its potential to revolutionize underwater robotics and exploration. DU-VIO offers a robust solution to the persistent challenge of underwater visibility, significantly improving the accuracy of pose estimation. This research contributes valuable insights and tools for advancing underwater technology, with far-reaching implications for scientific research, environmental monitoring, and industrial applications.

翻译：本文深入探讨了DU-VIO的潜力，这是一种基于去雾辅助的混合多速率多模态视觉-惯性里程计（VIO）估计框架，专为在极端水下环境的挑战性领域中稳定运行而设计。该前沿的DU-VIO框架集成了基于GAN的预处理模块和混合CNN-LSTM模块，利用可见性增强的水下图像与原始IMU数据进行精确的姿态估计。精确的姿态估计对于各类水下机器人及勘探应用至关重要。然而，水下能见度常因悬浮颗粒与衰减效应而严重受损，使得视觉-惯性姿态估计成为一项艰巨挑战。DU-VIO旨在通过有效消除原始图像数据中的视觉干扰、提升用于姿态估计的图像特征质量来克服这些局限。我们通过计算平移与旋转向量相对于参考值的均方根误差（RMSE）分数，并基于改进的AQUALOC数据集与基准模型结果进行对比，验证了DU-VIO的有效性。本研究的核心意义在于其可能为水下机器人技术与勘探领域带来革命性突破。DU-VIO为长期存在的水下能见度难题提供了鲁棒的解决方案，显著提升了姿态估计的精度。此项研究为推进水下技术发展贡献了宝贵的见解与工具，对科学研究、环境监测及工业应用具有深远影响。