2D前视声纳俯仰角估计自监督学习中的运动退化
Motion Degeneracy in Self-supervised Learning of Elevation Angle Estimation for 2D Forward-Looking Sonar
AI总结：
针对2D前视声纳俯仰角估计自监督学习中依赖合成数据预训练的问题，通过分析运动退化现象并利用有效运动构建数据集，实现了无需预训练的稳定自监督学习，经仿真与真实实验验证有效。
摘要：
2D前视声纳是水下机器人感知的关键传感器。该领域一个众所周知的问题是估计声纳成像中俯仰方向缺失的信息。在穿越任务期间，为进行3D建图和机器人导航，需要逐图像估计3D信息。近期基于学习的方法已展现出优势，但仍存在缺陷。监督学习方法取得了高质量结果，但可能需要进一步努力获取3D真值标签。现有的自监督方法需要使用带有3D监督的合成图像进行预训练。本研究旨在实现无需使用合成图像预训练的俯仰角估计的稳定自监督学习。自监督学习中的失败可能由运动退化问题引起。我们首先分析了2D前视声纳的运动场，这与主要监督信号相关。我们利用现代学习框架并证明，如果训练数据集使用有效运动构建，网络可以在没有合成数据知识的情况下以自监督方式进行训练。仿真和真实实验均验证了所提出的方法。
英文摘要：
2D forward-looking sonar is a crucial sensor for underwater robotic perception. A well-known problem in this field is estimating missing information in the elevation direction during sonar imaging. There are demands to estimate 3D information per image for 3D mapping and robot navigation during fly-through missions. Recent learning-based methods have demonstrated their strengths, but there are still drawbacks. Supervised learning methods have achieved high-quality results but may require further efforts to acquire 3D ground-truth labels. The existing self-supervised method requires pretraining using synthetic images with 3D supervision. This study aims to realize stable self-supervised learning of elevation angle estimation without pretraining using synthetic images. Failures during self-supervised learning may be caused by motion degeneracy problems. We first analyze the motion field of 2D forward-looking sonar, which is related to the main supervision signal. We utilize a modern learning framework and prove that if the training dataset is built with effective motions, the network can be trained in a self-supervised manner without the knowledge of synthetic data. Both simulation and real experiments validate the proposed method.
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