| Summary: | The potential of using autonomous underwater vehicles (AUVs) for underwater exploration 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 paper, we propose a vision-based framework by addressing the detection and pose estimation problems for short-range underwater docking using these systems. 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), which is the first publicly available underwater docking dataset to the best of our knowledge. In the field experiments, we first evaluate the performance of DoNN on the UDID and its deformed variations. Next, we examine the pose estimation module by ground and underwater experiments. At last, we integrate our proposed vision-based framework with an ultra-short baseline acoustic sensor, to demonstrate the efficiency and accuracy of our framework by performing experiments in a lake. The experimental results show that the proposed framework is able to detect docking stations and estimate their relative pose more efficiently and successfully, compared with the state-of-the-art baseline systems.
|
|---|
