Keypoint-Guided Efficient Pose Estimation and Domain Adaptation for Micro Aerial Vehicles

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.