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.

Experimental Comparison of Open Source VisualInertialBased State Estimation Algorithms in the Underwater Domain A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent opensource packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct and indirect methods that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.