We propose a novel data-driven approach for high-resolution bathymetric reconstruction from sidescan. Sidescan sonar (SSS) intensities as a function of range do contain some information about the slope of the seabed. However, that information must be inferred. Additionally, the navigation system provides the estimated trajectory, and normally the altitude along this trajectory is also available. From these we obtain a very coarse seabed bathymetry as an input. This is then combined with the indirect but high-resolution seabed slope information from the sidescan to estimate the full bathymetry. This sparse depth could be acquired by single-beam echo sounder, Doppler Velocity Log (DVL), other bottom tracking sensors or bottom tracking algorithm from sidescan itself. In our work, a fully convolutional network is used to estimate the depth contour and its aleatoric uncertainty from the sidescan images and sparse depth in an end-to-end fashion. The estimated depth is then used together with the range to calculate the point's 3D location on the seafloor. A high-quality bathymetric map can be reconstructed after fusing the depth predictions and the corresponding confidence measures from the neural networks. We show the improvement of the bathymetric map gained by using sparse depths with sidescan over estimates with sidescan alone. We also show the benefit of confidence weighting when fusing multiple bathymetric estimates into a single map.

翻译：我们建议从侧方进行高分辨率测深重建时采用新的数据驱动方法。 侧扫描声纳强度作为测距函数, 包含一些关于海底坡度的信息。 但是, 必须推断出这些信息 。 此外, 导航系统提供估计轨道, 通常还有沿此轨迹的高度。 我们从中获取了一个非常粗糙的海底测深仪作为输入。 然后, 与侧端间接但高分辨率的海底坡度信息相结合, 以估计整个测深。 这种稀疏的深度可以通过单波音回声仪( Doppler Velcity Log ) 获得, 其它底跟踪传感器或底底跟踪算法本身也包含一些信息 。 在我们的工作中, 一个完全革命性网络用来估计深处的轮廓及其测高度, 以端到端的深度为稀疏远。 然后, 估计深度与测深处的测距一起用来计算海底三维测深点的位置。 高品质的测深图可以从深度预测和深度测深度测深的深度测算中, 利用我们测深的深度测测测深的深度测深的测测测算器,, 显示深度测深的深度测深线的深度测深的深度测深的深度测算结果, 之后, 还可以测得的深度测得的深度测得的深度测得的深度测得的深度测深面的深度测得的深度测算结果, 。