Automatic Determination of Validity of Input Data Used in Ellipsoid Fitting MARG Calibration Algorithms

Ellipsoid fitting algorithms are widely used to calibrate Magnetic Angular Rate and Gravity (MARG) sensors. These algorithms are based on the minimization of an error function that optimizes the parameters of a mathematical sensor model that is subsequently applied to calibrate the raw data. The convergence of this kind of algorithms to a correct solution is very sensitive to input data. Input calibration datasets must be properly distributed in space so data can be accurately fitted to the theoretical ellipsoid model. Gathering a well distributed set is not an easy task as it is difficult for the operator carrying out the maneuvers to keep a visual record of all the positions that have already been covered, as well as the remaining ones. It would be then desirable to have a system that gives feedback to the operator when the dataset is ready, or to enable the calibration process in auto-calibrated systems. In this work, we propose two different algorithms that analyze the goodness of the distributions by computing four different indicators. The first approach is based on a thresholding algorithm that uses only one indicator as its input and the second one is based on a Fuzzy Logic System (FLS) that estimates the calibration error for a given calibration set using a weighted combination of two indicators. Very accurate classification between valid and invalid datasets is achieved with average Area Under Curve (AUC) of up to 0.98.This work was partly supported by the MICINN under the TEC2012-34306 project, the MEEC under the TIN2012-32039 (hpMooN) and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103

Calibration of miniature inertial and magnetic sensor units for robust attitude estimation

Attitude estimation from miniature inertial and magnetic sensors has been used in a wide variety of applications, ranging from virtual reality, underwater vehicles, handheld navigation devices, to biomotion analysis. However, appropriate sensor calibrations for accurate sensor measurements are essential to the performance of attitude estimation algorithms. In this paper, we present a robust sensor calibration method for accurate attitude estimation from three-axis accelerometers, gyroscopes, and magnetometer measurements. The proposed calibration method only requires a simple pan-tilt unit. A unified sensor model for inertial and magnetic sensors is used to convert the sensor readings to physical quantities in metric units. Based on the sensor model, a cost function is constructed, and a two-step iterative algorithm is then proposed to calibrate the inertial sensors. Due to the difficulties of acquiring the ground-truth of the Earth magnetic field, a simplified pseudomagnetometer calibration method is also presented based on an ellipsoid fitting algorithm. The calibration method is then applied to our sensor nodes, and the good performance of the orientation estimation has illustrated the effectiveness of the proposed sensor calibration method

Two-step calibration methods for miniature inertial and magnetic sensor units

Low-cost inertial/magnetic sensor units have been extensively used to determine sensor attitude information for a wide variety of applications, ranging from virtual reality, underwater vehicles, handheld navigation systems, to biomotion analysis and biomedical applications. In order to achieve precise attitude reconstruction, appropriate sensor calibration procedures must be performed in advance to process sensor readings properly. In this paper, we are aiming to calibrate different error parameters, such as sensor sensitivity/scale factor error, offset/bias error, nonorthogonality error, mounting error, and also soft iron and hard iron errors for magnetometers. Instead of estimating all of these parameters individually, these errors are combined together as the combined bias and transformation matrix. Two-step approaches are proposed to determine the combined bias and transformation matrix separately. For the accelerometer and magnetometer, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings, and the transformation matrix is then derived through a two-step iterative algorithm by exploring the intrinsic relationship among sensor readings. For the gyroscope, the combined bias can be easily determined by placing the sensor node stationary. For the transformation matrix estimation, the intrinsic relationship among gyroscope readings is explored again, and an unscented Kalman filter is employed to determine such matrix. The calibration methods are then applied to our sensor nodes, and the good performance of the orientation estimation has illustrated the effectiveness of the proposed sensor calibration methods

사용자 상황인지 딥러닝을 사용한 GPS 반송파 / 관성 센서 결합 스마트폰 보행자 항법

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 여재익.본 논문에서는 스마트폰 Galaxy S8 환경에서 GPS / INS 결합 보행자 항법을 수행하였으며, 스마트폰 센서의 특성을 자세히 분석하였다. 이에 최근 공개된 Android GNSS API 를 사용하여 GPS 원시데이터를 항법에 이용하면서, Cycle slip 을 보정한 Carrier phase 를 이용한 속도 결정법을 사용하였다. 이로 인해 기존의 NMEA GPS 를 사용한 방식의 스마트폰 보행자 항법보다 정밀한 위치, 속도 항법이 가능하였고, 성능을 향상 시켰다. 또한 사용자 상황 분석이 가능한 분류 딥러닝 기법을 사용하여 각 보행 상황에 따른 분류감지가 가능하였음 보였으며, LSTM 의 입력부분을 변화한 몇가지 딥러닝 모델의 성능을 비교하였다. 이를 통해서 사용자의 보행 상황에 따른 적응적 보행자 항법 파라메터 결정이 가능함의 가능성을 보였다.In this research, the overall construction of the smartphone GPS / INS pedestrian dead reckoning system is detaily described with considering the smartphone sensor measurement properties. Also, the recent android GNSS API which can provide the raw GPS measurement is used. With carrier phase, the cycleslip compensated velocity determination is considered. As a result, the carrier phase /INS integrated pedestrian dead reckoning shows the more precise navigation accuracy than NMEA. Moreover, The deep learning approach is applied in the user context classification to change the parameters in the pedestrian dead reckoning system. The author compares the effect of several transformed inputs for the LSTM model and validate each classification performances.Abstract i Contents ii List of Figures iv List of Tables vii Chapter 1. Introduction 1 1.1 Motivation and Backgrounds 1 1.2 Research Purpose and Contribution 3 1.3 Contents and Methods of Research 3 Chapter 2. Smartphone GPS / INS measurements analysis 4 2.1 Smartphone GNSS measurements 4 2.1.1 Android Raw GNSS Measurements API 4 2.1.2 Raw GPS Measurements Properties 7 2.1.3 Smartphone NMEA Location Provider 8 2.1.4 Pseudorange Based Position Estimation 10 2.1.5 Position Determination Experiment 11 2.2 Smartphone INS Measurements 12 2.2.1 Android Sensor Manager API 12 2.2.2 INS Measurements Properties 13 2.2.3 Noise level, Constant bias, Scale factor, Calibration 14 2.2.4. Accelerometer, Gyroscope Calibration Experiment 17 2.2.5 Magnetometer Ellipse Fitting Calibration 22 2.2.6 Random Bias, Allan Variance Exiperiment 25 2.3 Developed Android Smartphone App 30 Chapter 3. Pedestrian Dead Reckoning 31 3.1 Pedestrian Dead-Reckoning System 31 3.1.1 Attitude Determination Quaternion Kalman Filter 32 3.1.2 Attitude Determination Simulation , Experiment 35 3.1.3 Walking Detection 39 3.1.4 Step Counting, Stride Length 41 3.1.5 Pedestrian Dead Reckoning Experiment 45 Chapter 4. Carrier phase / INS integrated Pedestrian Dead Reckoning 50 4.1 Carrier phase Cycleslip Compensation & Velocity Determination 50 4.1.1 Carrier phase Cycleslip Compensation 50 4.1.2 Android Environment Cycle slip Detection 51 4.1.3 False Alarm & Miss Detection Analysis 55 4.1.4 Doppler, Carrier Based Velocity Estimation 56 4.1.5 Cycle slip Compensation & Velocity Determination Experiment 58 4.2 Raw GPS / INS Integrated Pedestrian Dead Reckoning 63 4.2.1 GPS / INS Integration 63 4.2.2 Position Determination Extended Kalman Filter 65 4.2.3 Raw GPS / INS Integrated Pedestrian Dead Reckoning Experiment 66 Chapter 5. User Context Classification Deep learning for Adaptive PDR 69 5.1 Smartphone Location / Walking Context Classification 69 5.1.1 Smartphone Location / Walking Context Dataset 69 5.1.2 Deep Learning Models 71 5.1.3 Comparison of Input Transformations 72 Chapter 6. Conclusion & Future work 76 Chapter 7. Bibliography 77Maste

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic

Auto-correction of 3D-orientation of IMUs on electric bicycles

The application of inertial measurement units (IMU) in electronically power-assisted cycles (EPACs) has become increasingly important for improving their functionalities. One central issue of such an application is to calibrate the orientation of the IMU on the EPAC. The approach presented in this paper utilizes common bicycling motions to calibrate the 2D- and 3D-mounting orientation of a micro-electro-mechanical system (MEMS) IMU on an electric bicycle. The method is independent of sensor biases and requires only a very low computation expense and, thus, the estimation can be realized in real-time. In addition, the acceleration biases are estimated using a barometric pressure sensor. The experimental results show high accuracy of the calibrated orientation and estimated sensor biases

UAV perception for safe flight under physical interaction

Aplicat embargament des de la data de defensa fins al maig 2020The control of autonomous flying vehicles with navigation purposes is a challenging task. Complexity arises mainly due to the non-linearity and uncertainty inherently present in the flight mechanics and aircraft-air interactions. Recently, interest has grown for equipping unmanned vehicles with the capacity to interact with their environment, other vehicles or humans. This will enable interesting applications such as autonomous load carrying, aerial refueling or parcel delivering. Having measured the interaction wrenches ease the control problem which can be configured to reject disturbances or to take profit of them to fulfill mission objectives. This thesis will contribute to this area by providing perception solutions which use limited and low cost sensors that enable state and disturbance estimation for possible, but not restricted to, interaction scenarios. This thesis contain three parts. The first part, introduces basic concepts related to the navigation state, aircraft dynamics, and sensor models. In addition, the platform under study is presented and mathematical models associated to it are calibrated. The second part is devoted to the observability analysis and the design of state observers. Linear and non-linear observability analysis techniques are used to unveil that the state of quadrotors equipped with GPS, magnetometers an IMU sensors cannot be uniquely identified in some specific flight configurations. Results of this section are relevant because the conflicting flight configurations contain hover, a flight maneuverer central in many unmanned aerial missions of VtoL vehicles. For many possible singular configurations, insightful descriptions and interpretations of the solution space known as indistinguishable region is provided. Findings are verified in simulation scenarios where it can be seen how a filter fails to recover the true state of an aircraft when imposing the hover flight condition. We discuss then the design of Extended Kalman Filters for state estimation that considers the available sensors. Issues that are typically not reported in the literature, such as when to update or propagate in the estimator algorithm or which coordinate frame should be used to represent each state variable are discussed. This leads to the formulation of four potentially equivalent but different discrete event-based filters for which precise algorithmic expressions are given. We compare the results of the four filters in simulation under known favorable conditions for observability. In order to diminish the effect of flying in the conflicting observability configurations, we provide an alternative filter based on the Schmidt Kalman Filter (SKF). The proposed filter shares the structure of the EKF, behaves better in the instants that the EKF fails and provides similar results in the remaining conditions. The last part of the thesis deals with the estimation of external disturbances. Disturbance estimation results are based on the derivation of a linear model for the aircraft dynamics which then extended with a high order disturbance model to enable the estimation of fast varying disturbances. Two external disturbance estimators from the literature are reviewed and adapted to the new model. Also, two Kalman observers that exploit the linearity of the derived model are presented. A simulation comparison is provided demonstrating that the KF disturbance estimators outperform the other. In addition, this part presents a design methodology of generic quadratic bounded observers for linear systems with ellipsoidal bounded uncertainty. The derived observers maximize a user tunable compromise between the estimation convergence speed and the final volume containing the estimation error. An observer for disturbances acting on a flying platform is derived considering the high order disturbance model above mentioned. Finally, an analysis of the estimation performance with respect to the design parameters is presented.Esta tesis, contribuye en este área formulando soluciones de percepción que permiten la estimación del estado y perturbaciones externas en condiciones normales de vuelos así como casos de interacción para UAVs equipados con sensores limitados y de bajo coste. La tesis se estructura en tres partes. La primera de ellas introduce los conceptos básicos relacionados con el estado de navegación, la dinámica de la aeronave y modelos de sensores. Además, se presenta la plataforma de estudio así como los modelos matemáticos asociados a ella y su calibración. La segunda parte está destinada al análisis de observabilidad y el diseño de observadores de estado. Los resultados de esta sección son importantes porque dentro de las condiciones de vuelo conflictivas se encuentra el vuelo a punto fijo, una maniobra de vuelo central en muchas misiones de vehículos VToL. Se analizan estas condiciones críticas de vuelo y para ellas se deriva y describe el espacio de soluciones posible conocido como región indistinguible. Los resultados son verificados en simulación dónde se puede apreciar como un estimador de estado falla al intentar realizar su tarea cuando la aeronave está en vuelo a punto fijo. Seguidamente se presenta el diseño de filtros extendidos de Kalman (EKF) que proveen estimaciones del estado con la información limitada de los sensores disponibles. Se discuten conceptos que habitualmente no se presentan en la literatura como cuando actualizar o propagar en el algoritmo de estimación o que sistema de referencia se debe utilizar para representar adecuadamente las variables de estado. Esto lleva a la formulación algorítmica de cuatro filtros discretos basados en eventos, diferentes, pero en esencia equivalentes. Se derivan rutinas de inicialización para los filtros y se comparan los resultados en simulación bajo condiciones favorables de estimación. Con la idea de disminuir el efecto de volar en configuraciones de observabilidad conflictivas, se deriva un filtro alternativo basado en el filtro de Schmidt Kalman (SKF). El filtro propuesto comparte estructura con el EKF, tiene un mejor comportamiento allí dónde le EKF falla y una respuesta similar en el resto de condiciones de vuelo. La última parte de la tesis trata con la estimación de perturbaciones externas. Para ello se deriva un modelo lineal que relaciona fuerzas y momentos con velocidades junto a un modelo de alto orden para las perturbaciones. Se estudia su aplicación a dos modelos para la estimación de perturbaciones ya presentes en la literatura. Además, se proponen dos nuevos filtros de Kalman que se aprovechan de la linealidad del modelo. Se presenta una comparativa basada en la simulación de escenarios ideales así como realistas que demuestra que los filtros KF superan al resto. Esta misma parte de la tesis presenta el diseño genérico de estimadores "quadratic bounded" para sistemas dinámicos lineales cuya incertidumbre se encuentra acotada dentro de elipsoides. Estos estimadores maximizan un compromiso, ajustable por el usuario que contempla la velocidad de convergencia así como el volumen de la solución final que contiene el error de estimación. Se deriva un observador de perturbaciones para plataformas aéreas basado en el modelo de alto orden arriba mencionado. Finalmente, se presenta un análisis del desempeño de estimación en función de los parámetros de diseño del filtro.Postprint (published version

UAV perception for safe flight under physical interaction

The control of autonomous flying vehicles with navigation purposes is a challenging task. Complexity arises mainly due to the non-linearity and uncertainty inherently present in the flight mechanics and aircraft-air interactions. Recently, interest has grown for equipping unmanned vehicles with the capacity to interact with their environment, other vehicles or humans. This will enable interesting applications such as autonomous load carrying, aerial refueling or parcel delivering. Having measured the interaction wrenches ease the control problem which can be configured to reject disturbances or to take profit of them to fulfill mission objectives. This thesis will contribute to this area by providing perception solutions which use limited and low cost sensors that enable state and disturbance estimation for possible, but not restricted to, interaction scenarios. This thesis contain three parts. The first part, introduces basic concepts related to the navigation state, aircraft dynamics, and sensor models. In addition, the platform under study is presented and mathematical models associated to it are calibrated. The second part is devoted to the observability analysis and the design of state observers. Linear and non-linear observability analysis techniques are used to unveil that the state of quadrotors equipped with GPS, magnetometers an IMU sensors cannot be uniquely identified in some specific flight configurations. Results of this section are relevant because the conflicting flight configurations contain hover, a flight maneuverer central in many unmanned aerial missions of VtoL vehicles. For many possible singular configurations, insightful descriptions and interpretations of the solution space known as indistinguishable region is provided. Findings are verified in simulation scenarios where it can be seen how a filter fails to recover the true state of an aircraft when imposing the hover flight condition. We discuss then the design of Extended Kalman Filters for state estimation that considers the available sensors. Issues that are typically not reported in the literature, such as when to update or propagate in the estimator algorithm or which coordinate frame should be used to represent each state variable are discussed. This leads to the formulation of four potentially equivalent but different discrete event-based filters for which precise algorithmic expressions are given. We compare the results of the four filters in simulation under known favorable conditions for observability. In order to diminish the effect of flying in the conflicting observability configurations, we provide an alternative filter based on the Schmidt Kalman Filter (SKF). The proposed filter shares the structure of the EKF, behaves better in the instants that the EKF fails and provides similar results in the remaining conditions. The last part of the thesis deals with the estimation of external disturbances. Disturbance estimation results are based on the derivation of a linear model for the aircraft dynamics which then extended with a high order disturbance model to enable the estimation of fast varying disturbances. Two external disturbance estimators from the literature are reviewed and adapted to the new model. Also, two Kalman observers that exploit the linearity of the derived model are presented. A simulation comparison is provided demonstrating that the KF disturbance estimators outperform the other. In addition, this part presents a design methodology of generic quadratic bounded observers for linear systems with ellipsoidal bounded uncertainty. The derived observers maximize a user tunable compromise between the estimation convergence speed and the final volume containing the estimation error. An observer for disturbances acting on a flying platform is derived considering the high order disturbance model above mentioned. Finally, an analysis of the estimation performance with respect to the design parameters is presented.Esta tesis, contribuye en este área formulando soluciones de percepción que permiten la estimación del estado y perturbaciones externas en condiciones normales de vuelos así como casos de interacción para UAVs equipados con sensores limitados y de bajo coste. La tesis se estructura en tres partes. La primera de ellas introduce los conceptos básicos relacionados con el estado de navegación, la dinámica de la aeronave y modelos de sensores. Además, se presenta la plataforma de estudio así como los modelos matemáticos asociados a ella y su calibración. La segunda parte está destinada al análisis de observabilidad y el diseño de observadores de estado. Los resultados de esta sección son importantes porque dentro de las condiciones de vuelo conflictivas se encuentra el vuelo a punto fijo, una maniobra de vuelo central en muchas misiones de vehículos VToL. Se analizan estas condiciones críticas de vuelo y para ellas se deriva y describe el espacio de soluciones posible conocido como región indistinguible. Los resultados son verificados en simulación dónde se puede apreciar como un estimador de estado falla al intentar realizar su tarea cuando la aeronave está en vuelo a punto fijo. Seguidamente se presenta el diseño de filtros extendidos de Kalman (EKF) que proveen estimaciones del estado con la información limitada de los sensores disponibles. Se discuten conceptos que habitualmente no se presentan en la literatura como cuando actualizar o propagar en el algoritmo de estimación o que sistema de referencia se debe utilizar para representar adecuadamente las variables de estado. Esto lleva a la formulación algorítmica de cuatro filtros discretos basados en eventos, diferentes, pero en esencia equivalentes. Se derivan rutinas de inicialización para los filtros y se comparan los resultados en simulación bajo condiciones favorables de estimación. Con la idea de disminuir el efecto de volar en configuraciones de observabilidad conflictivas, se deriva un filtro alternativo basado en el filtro de Schmidt Kalman (SKF). El filtro propuesto comparte estructura con el EKF, tiene un mejor comportamiento allí dónde le EKF falla y una respuesta similar en el resto de condiciones de vuelo. La última parte de la tesis trata con la estimación de perturbaciones externas. Para ello se deriva un modelo lineal que relaciona fuerzas y momentos con velocidades junto a un modelo de alto orden para las perturbaciones. Se estudia su aplicación a dos modelos para la estimación de perturbaciones ya presentes en la literatura. Además, se proponen dos nuevos filtros de Kalman que se aprovechan de la linealidad del modelo. Se presenta una comparativa basada en la simulación de escenarios ideales así como realistas que demuestra que los filtros KF superan al resto. Esta misma parte de la tesis presenta el diseño genérico de estimadores "quadratic bounded" para sistemas dinámicos lineales cuya incertidumbre se encuentra acotada dentro de elipsoides. Estos estimadores maximizan un compromiso, ajustable por el usuario que contempla la velocidad de convergencia así como el volumen de la solución final que contiene el error de estimación. Se deriva un observador de perturbaciones para plataformas aéreas basado en el modelo de alto orden arriba mencionado. Finalmente, se presenta un análisis del desempeño de estimación en función de los parámetros de diseño del filtro