Object pose estimation underwater allows an autonomous system to perform tracking and intervention tasks. Nonetheless, underwater target pose estimation is remarkably challenging due to, among many factors, limited visibility, light scattering, cluttered environments, and constantly varying water conditions. An approach is to employ sonar or laser sensing to acquire 3D data, but besides being costly, the resulting data is normally noisy. For this reason, the community has focused on extracting pose estimates from RGB input. However, the literature is scarce and exhibits low detection accuracy. In this work, we propose an approach consisting of a 2D object detection and a 6D pose estimation that reliably obtains object poses in different underwater scenarios. To test our pipeline, we collect and make available a dataset of 4 objects in 10 different real scenes with annotations for object detection and pose estimation. We test our proposal in real and synthetic settings and compare its performance with similar end-to-end methodologies for 6D object pose estimation. Our dataset contains some challenging objects with symmetrical shapes and poor texture. Regardless of such object characteristics, our proposed method outperforms stat-of-the-art pose accuracy by ~8%. We finally demonstrate the reliability of our pose estimation pipeline by doing experiments with an underwater manipulation in a reaching task.

翻译：然而,水下目标的估算具有极大的挑战性,原因包括:可见度有限、光散散、环境杂乱、水况不断变化等许多因素。一种方法是利用声纳或激光遥感获得3D数据,但结果数据通常很吵。为此,社区侧重于提取RGB输入的估算值。然而,文献稀缺,检测准确性低。在这项工作中,我们提出一种由2D天体探测和6D天体估计组成的方法,以可靠的方式获取不同水下情景中的物体构成的物体。为了测试我们的管道,我们收集并提供10个不同真实场的4个物体数据集,附有物体探测和估计的说明。我们在真实和合成环境中测试我们的提案,并将其性能与6D天体的类似端对端方法进行比较。我们的数据集包含一些具有挑战性的物体,形状对称和质性差。我们提出的方法优于这些物体的特点,我们提出的方法优于能可靠地获得不同水下情景中的物体。我们最后要用~8号来测试我们的空间操纵性能。我们用一个任务来展示我们的可靠性。