inertial orientation tracker having automatic drift compensation using an at rest sensor for tracking parts of a human body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive sate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion

Inertial orientation tracker having gradual automatic drift compensation for tracking human head and other similarly sized body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive rate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion

Inertial orientation tracker apparatus method having automatic drift compensation for tracking human head and other similarly sized body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive rate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion

Application of inertial instruments for DSN antenna pointing and tracking

The feasibility of using inertial instruments to determine the pointing attitude of the NASA Deep Space Network antennas is examined. The objective is to obtain 1 mdeg pointing knowledge in both blind pointing and tracking modes to facilitate operation of the Deep Space Network 70 m antennas at 32 GHz. A measurement system employing accelerometers, an inclinometer, and optical gyroscopes is proposed. The initial pointing attitude is established by determining the direction of the local gravity vector using the accelerometers and the inclinometer, and the Earth's spin axis using the gyroscopes. Pointing during long-term tracking is maintained by integrating the gyroscope rates and augmenting these measurements with knowledge of the local gravity vector. A minimum-variance estimator is used to combine measurements to obtain the antenna pointing attitude. A key feature of the algorithm is its ability to recalibrate accelerometer parameters during operation. A survey of available inertial instrument technologies is also given

Stabilization of a thermal camera at sea

During, for example, search-and-rescue operations at sea, technical equipment like spotlights and thermal cameras are important aids. However, heave and sway affect the ship and make it harder for finding a person in distress. This thesis presents a way of stabilizing a thermal camera by controlling a stepper motor connected to it. Moreover, the thermal camera can only be turned upwards and downwards. The whole process was investigated to see what can and needs to be measured for stabilization at sea to work. With this knowledge, sensors had to be chosen to collect the necessary measurements. The measurements from the different sensors were merged together using a Kalman filter to give an estimation for the tilt and elevation of a ship, which was used for controlling the stepper motor. Moreover, the control of the stepper motor was done using PID control. The process was simulated in Simulink, making it possible to tune different parameters so that good performance was ensured based on assumed models. This was then implemented on the real system and tests were carried out to verify what was found during simulations. The result from this thesis is a stabilized thermal camera which helps an operator enormously in searching and finding objects at sea

Space shuttle navigation analysis. Volume 2: Baseline system navigation

Studies related to the baseline navigation system for the orbiter are presented. The baseline navigation system studies include a covariance analysis of the Inertial Measurement Unit calibration and alignment procedures, postflight IMU error recovery for the approach and landing phases, on-orbit calibration of IMU instrument biases, and a covariance analysis of entry and prelaunch navigation system performance

Estimating the orientation of a game controller from inertial and magnetic measurements

L’estimation de l’orientation d’un corps rigide en mouvement dans l’espace joue un rôle indispensable dans les technologies de navigation, par exemple, les systèmes militaires de missiles, les avions civils, les systèmes de navigation chirurgicale, la cartographie faite par des robots, les véhicules autonomes et les contrôleurs de jeux. Cette technique est maintenant utilisée dans certaines applications qui nous touchent directement, notamment dans les contrôleurs de jeux tels que la Wii-mote. Dans cette veine, la recherche présentée ici porte sur l’estimation de l’orientation d’un corps rigide à partir des mesures de capteurs inertiels et magnétiques peu coûteux. Comme les capteurs inertiels permettent de mesurer les dérivées temporelles de l’orientation, il est naturel de commencer par l’estimation de la vitesse angulaire. Par conséquent, nous présentons d’abord une nouvelle façon de déterminer la vitesse angulaire d’un corps rigide à partir d’accéléromètres. Ensuite, afin d’estimer l’orientation, nous proposons une nouvelle méthode d’estimation de l’orientation d’un corps rigide dans le plan vertical à partir des mesures d’accéléromètres, en discernant ses composantes inertielle et gravitationnelle. Mais, ce n’est sûrement pas suffisant d’estimer l’orientation dans le plan vertical, parce que la plupart des applications se produisent dans l’espace tridimensionnel. Pour estimer les rotations dans l’espace, nous présentons d’abord la conception d’un contrôleur de jeu, dans lequel tous les capteurs nécessaires sont installés. Ensuite, ces capteurs sont étalonnés pour déterminer leurs facteurs d’échelle et leurs zéros, de manière à améliorer leurs exactitudes. Ensuite, nous développons une nouvelle méthode d’estimation de l’orientation d’un corps rigide se déplaçant dans l’espace, encore en discernant les composantes gravitationnelle et inertielle des accélérations. Finalement, pour imiter le contrôleur de jeu Wii, nous créons une interface usager simple de sorte qu’une représentation virtuelle du contrôleur de jeu puisse suivre chaque mouvement du contrôleur de jeu conçu (réalité virtuelle). L’interface usager conçue montre que l’algorithme proposé est suffisamment précis pour donner à l’usager un contrôle fidèle de l’orientation du contrôleur de jeu virtuel.Estimating the orientation of a rigid-body moving in space is an indispensable component of navigation technology, e.g., military missile systems, civil aircrafts, surgical navigation systems, robot mapping, autonomous vehicles and game controllers. It has now come directly into some aspects of our lives, notoriously in game controllers, such as the Wiimote. In this vein, this research focuses on the development of new algorithms to estimate the rigid-body orientation from common inexpensive inertial and magnetic sensors. As inertial sensors measure the time derivatives of the orientation, it is natural to start with the estimation of the angular velocity. More precisely, we present a novel way of determining the angular velocity of a rigid body from accelerometer measurements. This method finds application in crashworthiness and motion analysis in sports, for example, where impacts forbid the use of mechanical gyroscopes. Secondly, in an attempt to estimate the orientation in a simplified setting, we propose a novel method of estimating the orientation of a rigid body in the vertical plane from point-acceleration measurements, by discerning its gravitational and inertial components. Thirdly, it is surely not enough to estimate the orientation in the vertical plane, because most applications take place in three dimensions. For estimating rotations in space, we first present the game controller design, in which all necessary sensors are installed. Then, these sensors are calibrated to determine their scale factors and offsets so as to improve their performances. Thence, we develop a novel method of estimating the orientation of a rigid body moving in space from inertial sensors, also by discerning the gravitational and inertial components of the acceleration. Finally, in order to imitate the game controller Wii, we create a simple user interface in which a virtual representative of the game controller follows every orientation of the true game controller (virtual reality). The user interface shows that the proposed algorithm is sufficiently accurate to give the user a transparent control of the orientation of the virtual game controller

Development of MEMS - based IMU for position estimation: comparison of sensor fusion solutions

With the surge of inexpensive, widely accessible, and precise Micro-Electro Mechanical Systems (MEMS) in recent years, inertial systems tracking move ment have become ubiquitous nowadays. Contrary to Global Positioning Sys tem (GPS)-based positioning, Inertial Navigation System (INS) are intrinsically unaffected by signal jamming, blockage susceptibilities, and spoofing. Measure ments from inertial sensors are also acquired at elevated sampling rates and may be numerically integrated to estimate position and orientation knowledge. These measurements are precise on a small-time scale but gradually accumulate errors over extended periods. Combining multiple inertial sensors in a method known as sensor fusion makes it possible to produce a more consistent and dependable un derstanding of the system, decreasing accumulative errors. Several sensor fusion algorithms occur in literature aimed at estimating the Attitude and Heading Reference System (AHRS) of a rigid body with respect to a reference frame. This work describes the development and implementation of a low-cost, multi purpose INS for position and orientation estimation. Additionally, it presents an experimental comparison of a series of sensor fusion solutions and benchmarking their performance on estimating the position of a moving object. Results show a correlation between what sensors are trusted by the algorithm and how well it performed at estimating position. Mahony, SAAM and Tilt algorithms had best general position estimate performance.Com o recente surgimento de sistemas micro-eletromecânico amplamente acessíveis e precisos nos últimos anos, o rastreio de movimento através de sistemas de in erciais tornou-se omnipresente nos dias de hoje. Contrariamente à localização baseada no Sistema de Posicionamento Global (GPS), os Sistemas de Naveg ação Inercial (SNI) não são afetados intrinsecamente pela interferência de sinal, suscetibilidades de bloqueio e falsificação. As medições dos sensores inerciais também são adquiridas a elevadas taxas de amostragem e podem ser integradas numericamente para estimar os conhecimentos de posição e orientação. Estas medições são precisas numa escala de pequena dimensão, mas acumulam grad ualmente erros durante longos períodos. Combinar múltiplos sensores inerci ais num método conhecido como fusão de sensores permite produzir uma mais consistente e confiável compreensão do sistema, diminuindo erros acumulativos. Vários algoritmos de fusão de sensores ocorrem na literatura com o objetivo de estimar os Sistemas de Referência de Atitude e Rumo (SRAR) de um corpo rígido no que diz respeito a uma estrutura de referência. Este trabalho descreve o desenvolvimento e implementação de um sistema multiusos de baixo custo para estimativa de posição e orientação. Além disso, apresenta uma comparação experimental de uma série de soluções de fusão de sensores e compara o seu de sempenho na estimativa da posição de um objeto em movimento. Os resultados mostram uma correlação entre os sensores que são confiados pelo algoritmo e o quão bem ele desempenhou na posição estimada. Os algoritmos Mahony, SAAM e Tilt tiveram o melhor desempenho da estimativa da posição geral