Robotics, Perception and Learning Laboratory, Royal Institute of Technology, SE-100 44 Stockholm, Sweden.
Sensors (Basel). 2022 Jul 6;22(14):5092. doi: 10.3390/s22145092.
Sidescan sonar is a small and low-cost sensor that can be mounted on most unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs). It has the advantages of high resolution and wide coverage, which could be valuable in providing an efficient and cost-effective solution for obtaining the bathymetry when bathymetric data are unavailable. This work proposes a method of reconstructing bathymetry using only sidescan data from large-scale surveys by formulating the problem as a global optimization, where a Sinusoidal Representation Network (SIREN) is used to represent the bathymetry and the albedo and the beam profile are jointly estimated based on a Lambertian scattering model. The assessment of the proposed method is conducted by comparing the reconstructed bathymetry with the bathymetric data collected with a high-resolution multi-beam echo sounder (MBES). An error of 20 cm on the bathymetry is achieved from a large-scale survey. The proposed method proved to be an effective way to reconstruct bathymetry from sidescan sonar data when high-accuracy positioning is available. This could be of great use for applications such as surface vehicles with Global Navigation Satellite System (GNSS) to obtain high-quality bathymetry in shallow water or small autonomous underwater vehicles (AUVs) if simultaneous localization and mapping (SLAM) can be applied to correct the navigation estimate.
侧扫声纳是一种小型、低成本的传感器，可安装在大多数无人水下航行器（UUV）和无人水面航行器（USV）上。它具有高分辨率和广覆盖的优点，在提供高效、经济的解决方案以获取测深数据方面具有重要价值。这项工作提出了一种仅使用大规模调查的侧扫数据来重建测深的方法，将该问题表述为全局优化问题，其中使用正弦表示网络（SIREN）来表示测深，根据朗伯散射模型联合估计反照率和波束轮廓。通过将重建的测深与使用高分辨率多波束回声测深仪（MBES）收集的测深数据进行比较，对所提出的方法进行了评估。从大规模调查中获得了 20 厘米的测深误差。当可以获得高精度定位时，该方法被证明是一种从侧扫声纳数据中重建测深的有效方法。这对于表面航行器使用全球导航卫星系统（GNSS）在浅水区获得高质量测深，或者对于可以应用同时定位和制图（SLAM）来纠正导航估计的小型自主水下航行器（AUV）来说非常有用。
