A Multibeam Underwater Terrain Modeling Algorithm Based on Echo Energy Distribution Gradient In the field of underwater robot, abundant researches have been carried out in development of multibeam echosounding bottom detection. However, most works focused on the analysis of the amplitude of the reverberated echo which had its own limitation. In this paper, a new algorithm based on the multibeam echo energy gradient is developed to map the shallow water bottom topography. On this basis, all singleframe bottom depth curves are joined together to form a seabed surface. Kalman filter is further used to remove outliers and improve the reliability of surface mapping. The effectiveness of our algorithm is validated by both simulation and practical experiment.

Underwater Sonar and Aerial Images Data Fusion for Robot Localization Autonomous underwater navigation is a challenging problem because of the limitations imposed by aquatic environments. Among them, the use of Global Positioning System (GPS) is severely limited. Thus, we propose the use of sensor fusion to improve underwater localization in partially structured environments. We sustain our proposal explores the benefits of aerial images, such as georeferencing, to improve underwater navigation with a multibeam forward looking sonar. Our methodology combines stateoftheart approaches such as Deep Neural Networks and Adaptive Monte Carlo Localization to fuse data from different image domains. The obtained results show a significant improvement over traditional odometry for underwater localization.