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, however, the data is not clear and the sensors expensive. For this reason, the community has focused on extracting pose estimates from RGB input. In this work, we propose an approach that leverages 2D object detection to reliably compute 6D pose estimates in different underwater scenarios. We test our proposal with 4 objects with symmetrical shapes and poor texture spanning across 33,920 synthetic and 10 real scenes. All objects and scenes are made available in an open-source dataset that includes annotations for object detection and pose estimation. When benchmarking against similar end-to-end methodologies for 6D object pose estimation, our pipeline provides estimates that are 8% more accurate. We also demonstrate the real world usability of our pose estimation pipeline on an underwater robotic manipulator in a reaching task.

翻译：水下目标姿态估计使自主系统能够执行跟踪和干预任务。然而，由于能见度有限、光散射、环境杂乱以及水体条件不断变化等诸多因素，水下目标姿态估计极具挑战性。一种方法是利用声纳或激光传感获取三维数据，但数据不够清晰且传感器成本高昂。因此，学术界专注于从RGB输入中提取姿态估计。本文提出一种利用二维目标检测在不同水下场景中可靠计算六维姿态估计的方法。我们在4个具有对称形状且纹理匮乏的物体上进行了测试，涵盖33,920个合成场景和10个真实场景。所有物体和场景均以开源数据集形式公开，包含目标检测和姿态估计的标注。在与类似的端到端六维目标姿态估计方法进行基准测试时，我们的流程提供的估计准确率提高了8%。我们还在水下机器人机械臂的抓取任务中展示了姿态估计流程的实际可用性。