  Autonomous underwater vehicles (AUVs) have been deployed for underwater
exploration. However, its potential is confined by its limited on-board battery
energy and data storage capacity. This problem has been addressed using docking
systems by underwater recharging and data transfer for AUVs. In this work, we
propose a vision based framework for underwater docking following these
systems. The proposed framework comprises two modules; (i) a detection module
which provides location information on underwater docking stations in 2D images
captured by an on-board camera, and (ii) a pose estimation module which
recovers the relative 3D position and orientation between docking stations and
AUVs from the 2D images. For robust and credible detection of docking stations,
we propose a convolutional neural network called Docking Neural Network (DoNN).
For accurate pose estimation, a perspective-n-point algorithm is integrated
into our framework. In order to examine our framework in underwater docking
tasks, we collected a dataset of 2D images, named Underwater Docking Images
Dataset (UDID), in an experimental water pool. To the best of our knowledge,
UDID is the first publicly available underwater docking dataset. In the
experiments, we first evaluate performance of the proposed detection module on
UDID and its deformed variations. Next, we assess the accuracy of the pose
estimation module by ground experiments, since it is not feasible to obtain
true relative position and orientation between docking stations and AUVs under
water. Then, we examine the pose estimation module by underwater experiments in
our experimental water pool. Experimental results show that the proposed
framework can be used to detect docking stations and estimate their relative
pose efficiently and successfully, compared to the state-of-the-art baseline
systems.
