Air-bearing platforms for simulating the rotational dynamics of satellites
require highly precise ground truth systems. Unfortunately, commercial motion
capture systems used for this scope are complex and expensive. This paper shows
a novel and versatile method to compute the attitude of rotational air-bearing
platforms using a monocular camera and sets of fiducial markers. The work
proposes a geometry-based iterative algorithm that is significantly more
accurate than other literature methods that involve the solution of the
Perspective-n-Point problem. Additionally, auto-calibration procedures to
perform a preliminary estimation of the system parameters are shown. The
developed methodology is deployed onto a Raspberry Pi 4 micro-computer and
tested with a set of LED markers. Data obtained with this setup are compared
against computer simulations of the same system to understand and validate the
attitude estimation performances. Simulation results show expected 1-sigma
accuracies in the order of $\sim$ 12 arcsec and $\sim$ 37 arcsec for about- and
cross-boresight rotations of the platform, and average latency times of 6 ms.

一项使用单目相机和一组基准标记的方法，计算旋转空气轴承平台的姿态，该方法提出了一种基于几何的迭代算法，比解决透视 n 点问题的其他方法更准确，另外还展示了自动校准程序来执行系统参数的初步估计，并使用一组 LED 标记测试了该方法。

基于高精度视觉的空气轴承航天器模拟器姿态估计系统

High-accuracy Vision-Based Attitude Estimation System for Air-Bearing  Spacecraft Simulators

In many real-world settings, image observations of freely rotating 3D rigid
bodies, may be available when low-dimensional measurements are not. However,
the high-dimensionality of image data precludes the use of classical estimation
techniques to learn the dynamics. The usefulness of standard deep learning
methods is also limited because an image of a rigid body reveals nothing about
the distribution of mass inside the body, which, together with initial angular
velocity, is what determines how the body will rotate. We present a
physics-informed neural network model to estimate and predict 3D rotational
dynamics from image sequences. We achieve this using a multi-stage prediction
pipeline that maps individual images to a latent representation homeomorphic to
$\mathbf{SO}(3)$, computes angular velocities from latent pairs, and predicts
future latent states using the Hamiltonian equations of motion. We demonstrate
the efficacy of our approach on new rotating rigid-body datasets of sequences
of synthetic images of rotating objects, including cubes, prisms and
satellites, with unknown uniform and non-uniform mass distributions.

使用多级预测管道，通过图像序列估计和预测三维旋转动力学，采用物理信息神经网络模型，并在旋转对象的合成图像序列数据集上展示了其有效性，包括正方体、棱柱和卫星等物体的未知均匀和非均匀质量分布。