Micromagnetometer calibration for accurate orientation estimation

Micromagnetometers, together with inertial sensors, are widely used for attitude estimation for a wide variety of applications. However, appropriate sensor calibration, which is essential to the accuracy of attitude reconstruction, must be performed in advance. Thus far, many different magnetometer calibration methods have been proposed to compensate for errors such as scale, offset, and nonorthogonality. They have also been used for obviate magnetic errors due to soft and hard iron. However, in order to combine the magnetometer with inertial sensor for attitude reconstruction, alignment difference between the magnetometer and the axes of the inertial sensor must be determined as well. This paper proposes a practical means of sensor error correction by simultaneous consideration of sensor errors, magnetic errors, and alignment difference. We take the summation of the offset and hard iron error as the combined bias and then amalgamate the alignment difference and all the other errors as a transformation matrix. A two-step approach is presented to determine the combined bias and transformation matrix separately. In the first step, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings. In the second step, the intrinsic relationships of the raw sensor readings are explored to estimate the transformation matrix as a homogeneous linear least-squares problem. Singular value decomposition is then applied to estimate both the transformation matrix and magnetic vector. The proposed method is then applied to calibrate our sensor node. Although there is no ground truth for the combined bias and transformation matrix for our node, the consistency of calibration results among different trials and less than 3° root mean square error for orientation estimation have been achieved, which illustrates the effectiveness of the proposed sensor calibration method for practical applications

Cameras and Inertial/Magnetic Sensor Units Alignment Calibration

Due to the external acceleration interference/ magnetic disturbance, the inertial/magnetic measurements are usually fused with visual data for drift-free orientation estimation, which plays an important role in a wide variety of applications, ranging from virtual reality, robot, and computer vision to biomotion analysis and navigation. However, in order to perform data fusion, alignment calibration must be performed in advance to determine the difference between the sensor coordinate system and the camera coordinate system. Since orientation estimation performance of the inertial/magnetic sensor unit is immune to the selection of the inertial/magnetic sensor frame original point, we therefore ignore the translational difference by assuming the sensor and camera coordinate systems sharing the same original point and focus on the rotational alignment difference only in this paper. By exploiting the intrinsic restrictions among the coordinate transformations, the rotational alignment calibration problem is formulated by a simplified hand–eye equation AX = XB (A, X, and B are all rotation matrices). A two-step iterative algorithm is then proposed to solve such simplified handeye calibration task. Detailed laboratory validation has been performed and the good experimental results have illustrated the effectiveness of the proposed alignment calibration method

Robust computational intelligence techniques for visual information processing

The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

Algoritmo de Calibração de Magnetômetros Triaxiais Utilizando Ajuste de Quádrica por Distância Algébrica

As medidas obtidas de sensores de campo magnético são sensíveis a perturbações e erros, sendo necessário um método de calibração que pode melhorar consideravelmente sua precisão. O ajuste de elipsoides é um dos métodos mais utilizados para calibração de magnetômetros, porém a maioria dos algoritmos utiliza métodos iterativos, causando problemas de tempo de execução e convergência. Como alternativa, propõe-se um algoritmo direto de cálculo dos parâmetros de calibração utilizando o método dos mínimos quadrados com a métrica de distância algébrica. O presente trabalho apresenta um algoritmo de calibração de magnetômetros e a sua utilização em um sistema de calibração e fusão de dados de magnetômetros, acelerômetros e giroscópios baseado em um filtro de Kalman formando um sensor inercial capaz de obter sua orientação no espaço. As simulações computacionais e testes com dados reais mostram que o algoritmo de calibração elimina quase a totalidade dos erros lineares, enquanto executa muito mais rápido que os algoritmos tradicionais encontrados na literatura. As medições de um magnetômetro calibrado com o algoritmo proposto são utilizadas em conjunto com medições provenientes de acelerômetros e giroscópios para formar uma unidade de medição inercial (IMU) usando um filtro de Kalman simples. O sistema completo funcionou como esperado e os resultados dos testes indicam que o algoritmo de calibração de magnetômetros é adequado para utilização em uma IMU, sendo mais de dez vezes mais rápido que os algoritmos tradicionais e apresentando precisão similar