Visual detection of micro aerial vehicles (MAVs) is an important problem in many tasks such as vision-based swarming of MAVs. This article studies vision-based 6-D pose estimation to detect a 3-D bounding box of a target MAV, and then, estimate its 3-D position and 3-D attitude. The 3-D attitude information is critical to better estimate the target's velocity since the attitude and motion are dynamically coupled. In this article, we propose a novel 6-D pose estimation method, whose novelties are threefold. First, we propose a novel centroid point-guided keypoint localization network that outperforms the state-of-the-art methods in terms of both accuracy and efficiency. Second, while there are no publicly available real-world datasets for 6-D pose estimation for MAVs up to now, we propose a high-quality dataset based on an automatic dataset collection method. Third, since the dataset is collected in an indoor environment but detection tasks are usually in outdoor environments, we propose a self-training-based unsupervised domain adaption method to transfer the method from indoor to outdoor. Finally, we show that the estimated 6-D pose especially the 3-D attitude can significantly help improve the target's velocity estimation.

中文翻译：

---

微型飞行器关键点引导的高效位姿估计和域适应


微型飞行器 (MAV) 的视觉检测是许多任务中的一个重要问题，例如基于视觉的 MAV 集群。本文研究基于视觉的 6-D 位姿估计，以检测目标 MAV 的 3-D 边界框，然后估计其 3-D 位置和 3-D 姿态。由于姿态和运动是动态耦合的，因此 3D 姿态信息对于更好地估计目标速度至关重要。在本文中，我们提出了一种新颖的 6 维姿态估计方法，其新颖之处有三个。首先，我们提出了一种新颖的质心点引导关键点定位网络，该网络在准确性和效率方面均优于最先进的方法。其次，虽然到目前为止还没有公开的 MAV 6 维姿态估计真实世界数据集，但我们提出了一个基于自动数据集收集方法的高质量数据集。第三，由于数据集是在室内环境中收集的，但检测任务通常在室外环境中，因此我们提出了一种基于自训练的无监督域适应方法，将该方法从室内转移到室外。最后，我们表明估计的 6 维姿态尤其是 3 维姿态可以显着帮助改善目标的速度估计。