Lowcost Sonar Navigation System In this paper, we present a sonarbased navigation system, designed to deploy a fleet of autonomous mobile platforms at a reasonable cost. In educational and hobbyist contexts, a large number of robots is required. By means of classical navigation approaches, every robot should be provided with accurate vision or range sensors. This limits the maximum number of robots in the fleet, due to the unaffordable cost of these sensors. In contrast to that, our system requires a single platform equipped with a higher quality sensor, used to perform calibration and mapping tasks. The rest of the fleet, able to localize and navigate, is equipped solely with lowcost sonars, providing a notable reduction in the overall cost. We achieve this task by presenting a novel calibration procedure to estimate the sonars extrinsic and adapting a classical montecarlo localization algorithm to the sonar model, focusing on efficiency. We release an open source implementation of the system to the community.

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.