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

TL;DR

This paper introduces a highly accurate vision-based attitude estimation system for air-bearing spacecraft simulators using a monocular camera, fiducial markers, and an innovative geometry-based algorithm, validated through simulations and real-world tests.

Contribution

It presents a novel, more accurate geometry-based iterative algorithm for attitude estimation that outperforms existing methods solving the Perspective-n-Point problem, with auto-calibration and real-time deployment.

Findings

Achieves ~12 arcsec accuracy for about- and cross-boresight rotations

Latency of approximately 6 milliseconds in processing

Validated through simulations and real-world experiments

Abstract

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.