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.

通过深度 Transformer 网络，使用单目图像估计无人机相对于船舶的 6D 姿态，采用合成数据集和 2D 关键点标注，利用 Transformer 神经网络模型检测关键点并估计各个部位的 6D 姿态，并通过贝叶斯融合整合估计结果，在合成数据和实地飞行实验中测试，表明对于不同光照条件具有鲁棒性和准确性，位置估计误差分别为合成数据的船距离的约 0.8% 和实地飞行实验的船距离的约 1.0%，具有船舶自主无人机降落和导航的潜在应用。

基于深度 Transformer 网络的舰载无人机单目姿态估计

Deep Transformer Network for Monocular Pose Estimation of Ship-Based UAV

In this paper, a Bayesian fusion technique for remotely sensed multi-band
images is presented. The observed images are related to the high spectral and
high spatial resolution image to be recovered through physical degradations,
e.g., spatial and spectral blurring and/or subsampling defined by the sensor
characteristics. The fusion problem is formulated within a Bayesian estimation
framework. An appropriate prior distribution exploiting geometrical
consideration is introduced. To compute the Bayesian estimator of the scene of
interest from its posterior distribution, a Markov chain Monte Carlo algorithm
is designed to generate samples asymptotically distributed according to the
target distribution. To efficiently sample from this high-dimension
distribution, a Hamiltonian Monte Carlo step is introduced in the Gibbs
sampling strategy. The efficiency of the proposed fusion method is evaluated
with respect to several state-of-the-art fusion techniques. In particular, low
spatial resolution hyperspectral and multispectral images are fused to produce
a high spatial resolution hyperspectral image.

该论文介绍了一种 Bayesian 融合技术，旨在将多波段遥感图像融合为高光谱和高空间分辨率图像，并且通过引入适当的先验分布和 Markov 链蒙特卡罗算法设计实现，以提高融合方法的效率。