Machine Learning for Inferring Sidescan Images from Bathymetry and AUV Pose

Sammanfattning: Underwater navigation has been a big challenge for autonomous underwater vehicles (AUVs) for a long time. It is highly dependent on acoustic methods called SONAR. There are two kinds of sonar sensors which are commonly used, the multibeam sonar and the sidescan sonar. Both of them have some advantages and limitations. Substantial improvements can be made if a machine interpretation method can be developed for the translation between these two sonar data.The objective of this thesis project is to find an effective way to do translation from seabed bathymetry (underwater depth) data (from multibeam sonar) to sidescan sonar images. In the project, we explored the feasibility of machine learning based translation methods. Some different generative models based on the idea of generative adversarial nets were tried. This project is an experimental trial, and it still needs more improvement before production. But the result shows a strong potential for the ability of machine learning based methods to handle this kind of translation tasks.