DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization
DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization
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DOI：
10.1109/iros45743.2020.9341201
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Bharat Joshi;M. Modasshir;Travis Manderson;Hunter Damron;M. Xanthidis;Alberto Quattrini Li;Ioannis M. Rekleitis;G. Dudek
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文献类型：
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作者：
Bharat Joshi;M. Modasshir;Travis Manderson;Hunter Damron;M. Xanthidis;Alberto Quattrini Li;Ioannis M. Rekleitis;G. Dudek
在本文中，我们提出了一种实时深度学习方法，用于从单个图像中确定自主水下航行器（AUV）的6D相对位姿。在通信受限的水下环境中定位自己的自主机器人团队对于水下勘探、测绘、多机器人护航和其他多机器人任务等许多应用至关重要。由于在水下收集具有准确6D姿势的地面实况图像非常困难，因此这项工作利用虚幻游戏引擎模拟的渲染图像进行训练。采用图像到图像转换网络来桥接渲染图像和真实的图像之间的差距，从而产生用于训练的合成图像。该方法从一幅图像中预测AUV的6D位姿，作为AUV 3D模型的8个角点的2D图像关键点，然后使用基于RANSAC的Pestrian确定相机坐标系中的6D位姿。在现实世界的水下环境（游泳池和海洋）与不同的相机的实验结果表明，所提出的技术的鲁棒性和准确性的平移误差和方向误差的国家的最先进的方法。代码是公开的。
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.