DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization
This work addresses the challenge of underwater relative localization for AUVs, which is crucial for applications like exploration and multi-robot tasks, but it is incremental as it builds on existing deep learning and simulation techniques.
The paper tackles the problem of real-time 6D relative pose estimation for Autonomous Underwater Vehicles (AUVs) from a single image, achieving robust and accurate results in real-world underwater environments with reduced translation and orientation errors compared to state-of-the-art methods.
In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. A team of autonomous robots localizing themselves in a communication-constrained underwater environment is essential for many applications such as underwater exploration, mapping, multi-robot convoying, and other multi-robot tasks. Due to the profound difficulty of collecting ground truth images with accurate 6D poses underwater, this work utilizes rendered images from the Unreal Game Engine simulation for training. An image-to-image translation network is employed to bridge the gap between the rendered and the real images producing synthetic images for training. The proposed method predicts the 6D pose of an AUV from a single image as 2D image keypoints representing 8 corners of the 3D model of the AUV, and then the 6D pose in the camera coordinates is determined using RANSAC-based PnP. Experimental results in real-world underwater environments (swimming pool and ocean) with different cameras demonstrate the robustness and accuracy of the proposed technique in terms of translation error and orientation error over the state-of-the-art methods. The code is publicly available.