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In this paper, a hovering-type autonomous underwater vehicle called Cyclops is introduced. Because of the symmetric body structure and thruster configuration of Cyclops, it is specially designed to utilize a lawnmower trajectory without changing its heading direction. This movement is effective at reducing the dead reckoning error and obtaining source images with homogeneous optical characteristics for underwater image mosaicing.

This paper proposes a sonar-based underwater object classification method for autonomous underwater vehicles (AUVs) by reconstructing an object’s three-dimensional (3D) geometry. The point cloud of underwater objects can be generated from sonar images captured while the AUV passes over the object. Then, a neural network can predict the class given the generated point cloud. By reconstructing the 3D shape of the object, the proposed method can classify the object accurately through a straightforward training process. We verified the proposed method by performing simulations and field experiments.