ElevateNet: A Convolutional Neural Network for Estimating the Missing Dimension in 2D Underwater Sonar Images In this work we address the challenge of predicting the missing dimension (elevation angle) from 2D underwater sonar images. The high noise levels in these images, from phenomena such as nondiffuse reflections, frequently limits the usefulness of physical models. We thus propose the utilization of Convolutional Neural Networks (CNNs) as a powerful method to extract meaningful information without being misled by noisy data. We also introduce a selfsupervised method that uses the physics of the sonar sensor to train the network on real data without groundtruth elevation maps. Our method can produce accurate elevation angle estimates given only a single image. Finally, we demonstrate that our method produces more accurate 3D reconstructions than competing methods, both in simulation and on real data.

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.