Evaluation of data fusion algorithms for attitude estimation of unmanned aerial vehicles
The aim of this study was to evaluate and compare the three most commonly used data processing algorithms for Attitude and Heading Reference Systems (AHRS) for unmanned aerial vehicles (UAVs), which implement filtering processes and data fusion. These algorithms are the Kalman filter, Madgwick algor...
| Autores Principales: | Chérigo, Cristóbal, Rodríguez, Humberto |
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| Formato: | Artículo |
| Idioma: | Español |
| Publicado: |
Universidad Tecnológica de Panamá
2017
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| Materias: | |
| Acceso en línea: |
http://revistas.utp.ac.pa/index.php/id-tecnologico/article/view/1719 http://ridda2.utp.ac.pa/handle/123456789/3297 |
| Sumario: |
The aim of this study was to evaluate and compare the three most commonly used data processing algorithms for Attitude and Heading Reference Systems (AHRS) for unmanned aerial vehicles (UAVs), which implement filtering processes and data fusion. These algorithms are the Kalman filter, Madgwick algorithm and Mahony algorithm. Commercially, there are several types of IMU / Magnetometer sensors, which provide a very good feedback of an aircraft states. However, they tend to be very expensive, so in this paper we focus on those who have a medium cost and a good cost / performance ratio for use with UAVs. A methodology was developed so we could compare what algorithm adapts better to systems with different characteristics. The results showed that the Mahony algorithm worked better due to its faster convergence. Of the three angles of rotation around the main axes xyz, the angle around z (ψ) showed the largest error, which indicates that there is still some deficiency from those estimates which depend on the magnetometer. |
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