Abstract
This research addresses the challenge of estimating bathymetry from imaging sonars where the state-of-the-art works have primarily relied on either supervised learning with ground-truth labels or surface rendering based on the Lambertian assumption. In this letter, we propose a novel, self-supervised framework based on volume rendering for reconstructing bathymetry using forward-looking sonar (FLS) data collected during standard surveys. We represent the seafloor as a neural heightmap encapsulated with a parametric multi-resolution hash encoding scheme and model the sonar measurements with a differentiable renderer using sonar volumetric rendering employed with hierarchical sampling techniques. Additionally, we model the horizontal and vertical beam patterns and estimate them jointly with the bathymetry. We evaluate the proposed method quantitatively on simulation and field data collected by remotely operated vehicles (ROVs) during low-altitude surveys. Results show that the proposed method outperforms the current state-of-the-art approaches that use imaging sonars for seabed mapping. We also demonstrate that the proposed approach can potentially be used to increase the resolution of a low-resolution prior map with FLS data from low-altitude surveys.
Abstract (translated)
这项研究解决了从成像声纳中估计海底地形这一挑战，因为最先进的工作主要依赖于监督学习或基于Lambertian假设的表面渲染。在本文中，我们提出了一个新颖的、自监督的框架，基于体积渲染，用于通过标准调查期间收集的前向声纳数据（FLS）重构海底地形。我们将海底被视为一个参数多分辨率哈希编码方案捕获的神经高度图，并使用采用分层采样技术展开的声纳体积渲染模型来建模声纳测量。此外，我们还建模水平和垂直束模式，并与其共同估计海底地形。我们对使用遥控操作车辆（ROVs）在低空调查期间收集的模拟和现场数据进行定量评估。结果表明，与使用成像声纳进行海底映射的现有最佳方法相比，所提出的方法表现优异。我们还证明了这种方法有可能用于从低空调查中增加低分辨率先验图的分辨率。
URL
https://arxiv.org/abs/2404.14819
