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In this paper, we propose a real-time deep learning approach for determiningthe 6D relative pose of Autonomous Underwater Vehicles (AUV) from a singleimage. A team of autonomous robots localizing themselves in acommunication-constrained underwater environment is essential for manyapplications such as underwater exploration, mapping, multi-robot convoying,and other multi-robot tasks. Due to the profound difficulty of collectingground truth images with accurate 6D poses underwater, this work utilizesrendered images from the Unreal Game Engine simulation for training. Animage-to-image translation network is employed to bridge the gap between therendered and the real images producing synthetic images for training. Theproposed method predicts the 6D pose of an AUV from a single image as 2D imagekeypoints representing 8 corners of the 3D model of the AUV, and then the 6Dpose in the camera coordinates is determined using RANSAC-based PnP.Experimental results in real-world underwater environments (swimming pool andocean) with different cameras demonstrate the robustness and accuracy of theproposed technique in terms of translation error and orientation error over thestate-of-the-art methods. The code is publicly available.

DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization