Towards Accurate 3D Positioning in Large-Scale Underwater Environments

An effective and reliable AUV (autonomous underwater vehicle) should be capable to carry out complex underwater maintenance and exploration tasks in complete autonomy. Unfortunately, the hostility of the underwater environment, combined with the lack of any communication infrastructure and global positioning, introduce important scientific challenges. In this paper, we introduce an underwater global positioning system for AUVs suitable for large-scale environments sparsely populated by man-made infrastructure. Our system relies on sonar and stereo camera data, by means of a novel deep descriptor for sonar images and a two-view pixel-wise voting network for 6DoF (Six degrees of freedom) object pose estimation, both trained on synthetically generated datasets. The global AUV position is then estimated by exploiting a probabilistic framework that fuses on-line the available sensor readings.