The use of commercially available autonomous underwater vehicles (AUVs) has increased during the last fifteen years. While they are mainly used for routine survey missions, there is a set of applications that nowadays can be only addressed by manned submersibles or work-class remotely operated vehicles (ROVs) equipped with teleoperated arms: the intervention applications. To allow these heavy vehicles controlled by human operators to perform intervention tasks, underwater structures like observatory facilities, subsea panels or oil-well Christmas trees have been adapted, making them more robust and easier to operate. The TRITON Spanish founded project proposes the use of a light-weight intervention AUV (I-AUV) to carry out intervention applications simplifying the adaptation of these underwater structures and drastically reducing the operational cost. To prove this concept, the Girona 500 I-AUV is used to autonomously dock into an adapted subsea panel and once docked perform an intervention composed of turning a valve and plugging in/unplugging a connector. The techniques used for the autonomous docking and manipulation as well as the design of an adapted subsea panel with a funnel-based docking system are presented in this article together with the results achieved in a water tank and at sea.
This paper reports on a novel technique to visually detect loop closings in feature-poor underwater environments in order to increase the accuracy of vision-based localization systems. The main problem of the classical visual Simultaneous Localization and Mapping (SLAM) for underwater vehicles is the lack of robust, stable and matchable features in certain aquatic environments. The presence of sandbanks, seagrass or other underwater phenomena cause the visual features to concentrate in regions heavily textured, leaving great image areas completely free of visual information. In this situation, the classical loop closing detection algorithms fail, resulting in no corrections for the SLAM system. Our novel method proposes to reinforce the loop closing detection by clustering visual keypoints present in multiple keyframes and to match features of clusters instead of features of keyframes.This new technique is assessed on the particular application of navigating an Autonomous Underwater Vehicle (AUV) in marine environments colonized with seagrass or with the presence of sandbanks. Experiments conducted in several coastal zones on the Balearic Islands show a high degree of success in the visual registration of overlapping areas.
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