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Probabilistic 3D surface matching for bathymetry based Simultaneous Localization and Mapping of underwater vehicles

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Sonar localisation and mapping for underwater vehicles

Autonomous underwater vehicles (AUVs) are useful but difficult to localise underwater. New EU-developed methods help to estimate AUVs trajectories, using a special multi-beam sonar and image-compositing algorithms to monitor the vehicles' 3D surroundings.

Digital Economy

AUVs are small robotic submarines not connected by cable to other vehicles or ships. They have many scientific applications, especially as stable sensor platforms. However, their use can be challenging because of the difficulty in determining their position. The EU funded SURF3DSLAM, a two-year project aimed at developing a technique for locating AUVs in three dimensions using their own sensors. The concept, called Simultaneous Localisation and Mapping (SLAM), uses precise multi-beam echosounders to sense the vehicle's depth and surroundings. The system is unaffected by visibility or lighting conditions. The objective was to adapt to three dimensions a 2D technique for probabilistic, sonar-based, range-image registration. SURF3DSLAM concluded in March 2013. Project members described a method for pose-based SLAM, using probabilistic surface matching and a multi-beam sonar profiler. The algorithm combines scanned strips of the seafloor, and uses position calculations. The method was successfully demonstrated using real data from a submarine survey. The project also investigated a technique for surface adaptation. Published results total 12 peer-reviewed publications, 1 book chapter and 2 posters presented at international conferences. The multidisciplinary work of SURF3DSLAM significantly advanced both robotics and oceanographic research.

Keywords

Sonar, robot, submarines, autonomous underwater vehicles, multi-beam sonar, robotic submarines, sensor platforms, Simultaneous Localisation and Mapping, echosounders, probabilistic, sonar profiler, robotics, oceanographic research

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