Underwater Sonar and Aerial Images Data Fusion for Robot Localization

Abstract

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 state-of-the-art 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.