用于船载无人机的深度变换器网络姿态估计
Deep Transformer Network for Monocular Pose Estimation of Shipborne Unmanned Aerial Vehicle
AI总结：
本文提出了一种基于深度变换器网络的单目图像姿态估计方法，用于船载无人机的6D姿态估计，通过贝叶斯融合提升精度，并在合成数据和实地实验中验证了其鲁棒性和准确性。
摘要：
本文介绍了一种深度变换器网络，用于通过单目图像估计无人机相对于船的相对6D姿态。创建了一个船图像的合成数据集，并用多个船部分的2D关键点进行标注。训练了一个变换器神经网络模型来检测这些关键点并估计每个部分的6D姿态。估计结果通过贝叶斯融合整合。该模型在合成数据和实地飞行实验中进行了测试，展示了在不同光照条件下鲁棒性和准确性。合成数据和飞行实验中的位置估计误差分别约为船距离的0.8%和1.0%。该方法可能应用于船基自主无人机着陆和导航。
英文摘要：
This paper introduces a deep transformer network for estimating the relative 6D pose of a Unmanned Aerial Vehicle (UAV) with respect to a ship using monocular images. A synthetic dataset of ship images is created and annotated with 2D keypoints of multiple ship parts. A Transformer Neural Network model is trained to detect these keypoints and estimate the 6D pose of each part. The estimates are integrated using Bayesian fusion. The model is tested on synthetic data and in-situ flight experiments, demonstrating robustness and accuracy in various lighting conditions. The position estimation error is approximately 0.8\% and 1.0\% of the distance to the ship for the synthetic data and the flight experiments, respectively. The method has potential applications for ship-based autonomous UAV landing and navigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
