This thesis examines the design and implementation of the navigation solution for an autonomous ground vehicle suited with global position system (GPS) receivers, an inertial measurement unit (IMU), and wheel speed sensors (WSS) using the framework of Kalman filtering (KF). To demonstrate the flexibility of the KF several methods are explored and implemented such as constraints, multi-rate data, and cascading filters to augment the measurement matrix of a main filter. GPS and IMU navigation are discussed, along with common errors and disadvantages of each type of navigation system. It is shown that the coupling of sensors, constraints, and self-alignment techniques provide an accurate solution to the navigation problem for an autonomous vehicle. Filter divergence is discussed during times when the states are unobservable. Post processed data is analyzed to demonstrate performance under several test cases, such as GPS outage, and the effect that the initial calibration and alignment has on the accuracy of the solution. / Master of Science

The study of human locomotion, known as gait analysis, has for a long time been performed withexpensive equipment in laboratory settings. However, the emergence of machine learning sparkedinterest in integrating this technology in gait analysis, thus simplifying the process. This study’saim is to substitute the pressure insoles used during gait cycle analysis of a walking subject, with amachine learning model.To achieve this, a model based on Long-Short Term Memory networks that predicts vertical groundreaction force based on data from inertial measurement unit sensors was used. This serves as asubstitution for pressure insoles or pressure plates. The model was trained with time series datasetscontaining inertial measurement unit data and corresponding pressure insole data. Subsequently, itwas tested for intersubjective, out-of-sample data.The model was able to capture the periodicity of the gait cycle as well as predict the general shapeof the vertical ground reaction force curves, where the accuracy was quantified using normalisedroot mean squared error. The error was in a range between 17.8% and 13.4% and had an average of15.2%, when tested intersubjectively and out-of-sample. The most significant factor contributing tothe error was the model’s amplitude inaccuracies which was, most likely, due to information beinglost during the processing of the data, as well as simply having an insufficient amount of data.

Les systèmes électroniques actuels intègrent de plus en plus de fonctionnalités nécessitant l'intégration de capteurs très variés. Ces systèmes hétérogènes sont complexes à intégrer notamment lorsque différentes technologies de fabrication sont nécessaires pour les capteurs.Les technologies de fabrication de MEMS avec un procédé CMOS-FSBM offrent un coût de production réduit et permettent d'intégrer sur un même substrat différents types de capteurs (magnétomètres et accéléromètres notamment). Ce procédé de fabrication implique cependant une détection résistive des capteurs avec tous les problèmes qui lui sont associés (faible sensibilité, offset important, bruit de l'électronique). A travers la réalisation de la première centrale inertielle sur une puce, cette thèse renforce l'intérêt d'une approche « CMOS-MEMS » pour la conception de systèmes multi-capteurs. Le système est basé sur une mesure incomplète du champ magnétique terrestre (axes X et Y) et sur la mesure complète du champ gravitationnel. Une électronique de conditionnement des capteurs performante a été développée adressant les principaux problèmes relatifs à une détection résistive permettant ainsi une optimisation de la résolution de chaque capteur. Enfin, deux algorithmes ont été développés pour la détermination de l'attitude à partir de la mesure des cinq capteurs montrant la faisabilité et l'intérêt d'un tel système. / Current electronic systems integrate more and more applications that require the integration of various kinds of sensors. The integration of such heterogeneous systems is complex especially when sensor fabrication processes differ from one to another. MEMS manufacturing processes based on CMOS-FSBM process promote a low-cost production and allow the integration of various types of sensors on the same die (e.g., magnetometers and accelerometers). However, this manufacturing process requires that sensors make use of resistive transduction with its associated drawbacks (low sensitivity, offset, electronic noise). Through the design and the implementation of the first inertial measurement unit (IMU) on a chip, this thesis demonstrates the interest of a “CMOS-MEMS” approach for the design of multi-sensor systems. The IMU is based on the incomplete measurement of the Earth magnetic field (X and Y axis) and the complete measurement of the gravity. An efficient front-end electronic has been developed addressing the most important issues of resistive transduction and thus allowing an optimization of sensor resolution. Finally, two attitude determination algorithms have been developed from the five sensor measurements showing the feasibility and the interest of such a system.

The focus of the present thesis is on the joint use of cameras and inertial sensors, a recent area of active research. Within our scope, the performance of body state estimation is investigated with isolated inertial sensors, isolated cameras and finally with a fusion of two types of sensors within a Kalman Filtering framework. The study consists of both simulation and real hardware experiments. The body state estimation problem is restricted to a single axis rotation where we estimate turn angle and turn rate. This experimental setup provides a simple but effective means of assessing the benefits of the fusion process. Additionally, a sensitivity analysis is carried out in our simulation experiments to explore the sensitivity of the estimation performance to varying levels of calibration errors. It is shown by experiments that state estimation is more robust to calibration errors when the sensors are used jointly. For the fusion of sensors, the Indirect Kalman Filter is considered as well as the Direct Form Kalman Filter. This comparative study allows us to assess the contribution of an accurate system dynamical model to the final state estimates. Our simulation and real hardware experiments effectively show that the fusion of the sensors eliminate the unbounded error growth characteristic of inertial sensors while final state estimation outperforms the use of cameras alone. Overall we can v demonstrate that the Kalman based fusion result in bounded error, high performance estimation of body state. The results are promising and suggest that these benefits can be extended to body state estimation for multiple degrees of freedom.

In the production industry, quality control is of importance. Even though today's tools provide a lot of functionality and safety to help the operators in their job, the operators still is responsible for the final quality of the parts. Today the nutrunners manufactured by Atlas Copco use their driver to detect the tightening angle. There- fore the operator can influence the tightening by turning the tool clockwise or counterclockwise during a tightening and quality cannot be assured that the bolt is tightened with a certain torque angle. The function of orientation tracking was de- sired to be evaluated for the Tensor STB angle and STB pistol tools manufactured by Atlas Copco. To be able to study the orientation of a nutrunner, practical exper- iments were introduced where an IMU sensor was fixed on a battery powered nutrunner. Sensor fusion in the form of a complementary filter was evaluated. The result states that the accelerometer could not be used to estimate the angular dis- placement of tightening due to vibration and gimbal lock and therefore a sensor fusion is not possible. The gyroscope by itself can be used to provide the angular displacement around every axis with high accuracy without taking into account the gimbal lock phenomena or external forces in the form of vibration of the tool. The gyroscope provided data with a probability to measure ±1° in future tightenings by 69,76%. The gyroscope provided data with high accuracy and stability and can be used in real world application and production for true angle functionality of the tools. / I produktionsindustrin är kvalitetskontroll av stor betydelse. Även om dagens verk- tyg innehåller mycket funktionalitet och säkerhet för att hjälpa operatörer i jobbet, är operatören fortfarande ansvarig för den slutliga kvaliteten. Idag använder Atlas Copcos skruvdragare motorns vridmoment för att göra den slutliga åtdragningen. Därav kan operatören påverka åtdragningen genom att vrida verktyget medurs el- ler moturs under en åtdragning och kvaliteten kan inte säkerställas att bulten dras med ett visst vridmoment. Funktion för rörelseavläsning var önskvärd att utvärde- ras för Tensor STB-vinkel- och STB-pistolverktygen tillverkade av Atlas Copco. För att kunna studera orienteringen hos en skruvdragare introducerades praktiska ex- periment där en IMU-sensor fixerades på en batteridriven skruvdragare. En Sen- sorfusion i form av ett komplementärt filter utvärderades. Resultaten visar att acce- lerometern inte kunde användas för att uppskatta vinkelförskjutningen av en åt- dragning på grund av vibration och gimballås och därav kan inte fusionen heller nyttjas. Gyroskopet i sig kan användas för att ge vinkelförskjutningen runt varje axel med hög noggrannhet utan att ta hänsyn till gimballåsfenomen eller yttre kraf- ter i form av vibration från verktyget. Gyroskopet gav data med en sannolikhet att mäta ± 1° i avvikelse från ett förbestämt värde i framtida åtdragningar med 69,76%. Vidare utvärdering av gyroskop och implementation av detta borde göras innan detta skulle anses kunna användas i en riktig applikation för rörelseavläsning un- der en åtdragning.

Inom kanotsporten är synkronisering av paddeltag i en kanot med fler än en kanotist väldigt viktig för att uppnå en så hög hastighet som möjligt. Hans Rosdahl från Gymnastik- och Idrottshögskolan har därför gett i uppdrag att utveckla en mobilapplikation som ger respons till kanotisten som använder applikationen om dennes paddeltag är synkroniserad med frontkanotisten. En förstudie har därför utförts för att bland annat undersöka de olika sensoralternativen som fanns tillgängliga och en applikation inom operativsystemet Android har utvecklats. Applikationen ansluter sig till IMU-sensorer, Intertial Measurement Unit, som sitter på varje paddel och som mäter tröghetskrafterna för att bestämma orientering av sensorn. Applikationen tar emot IMU-data från sensorerna som sitter på användarkanotistens och frontkanotistens paddlar och varje sensor avgör när ett paddeltag har utförts genom orienteringen på sensorn. Denna sensordata som visar ett utfört paddeltag används för att beräkna tidsskillnaden är på paddeltagen från båda kanotister. När tidsskillnaden har räknats ut får användaren visuell respons på mobilskärmen om denne är synkroniserad med frontkanotisten eller inte och på vilken nivå synkroniseringen ligger. / Synchronisation of paddle stroke is an important aspect within the sports of paddling with multiple paddles in one boat to maximise the velocity. Hans Rosdahl from The Swedish School of Sport and Health Sciences, GIH, therefore provided a task to develop a mobile phone application that offers feedback to the paddlers using the application if their paddle stroke is synchronised with the paddler in front. A pre-study has been carried out to investigate possible sensor alternatives that are available, and an application has been developed for the mobile operative system Android. The application connects to an IMU, Inertial Measurement Unit, sensor that measures inertia to determine the orientation of the sensor node. The application receives sensor data from the IMU from the user paddler’s and the front paddler’s sensor nodes and each sensor determines when a paddle stroke has occurred using its orientation. The data showing a stroke is used to calculate the time difference between these strokes to evaluate if the user is synchronised with the front paddler or not. After this evaluation, the user receives a visual response of their synchronisation level on their screen.

Detta examensarbete har genomförts med syftet att utveckla Inno-x företagets system, som är avsett för vardagsidrottare för att mäta neuromuskulära aktiviteter i underkroppen med hjälp av modern teknologi. Systemet omfattar en tröghetsmätningsenhet (IMU) med accelerometer, gyroskop och en EMG-sensor (elektromyografi). Denna konfiguration möjliggör noggrann övervakning av neuromuskulära aktiviteter genom analys av svar på träning. Studiens mål var att identifiera en effektiv sensor för mätning av horisontella hoppavstånd och att utveckla en algoritm som sedan ska integreras i företagets produkt. Produkten kommer att använda magnetometer och IMU för att tolka mänskliga rörelser och för att förbättra noggrannheten i företagets mätningssystem. Processen inkluderar förbättring av mätningarnas noggrannhet, integration av teknik med biomekaniska principer, utvärdering av kalibreringstekniker för magnetometeravläsningar, kombination av sensorer för rörelseanalys och genomförande av utvärdering med olika åldersgrupper som består av 10 deltagare för att bedöma systemets effektivitet. Även om ingen av metoderna helt uppnådde den önskade noggrannheten inom ±5 cm, visade alla metoder god prestanda för olika tillämpningar. Detta antyder att implementeringen av en kalibrerad magnetometer potentiellt kan förbättra systemets noggrannhet vid bestämning av horisontella hoppavstånd, dock endast med en liten marginal, eftersom studien visade att med kalibrerade magnetometer RMSE (Root Mean Square Error) ökat med 0.99 cm. Ytterligare forskning rekommenderas för att undersöka nya sätt att kalibrera sensorer och integrera dem för mer precisa avläsningar. Dock bör det beaktas att magnetometeravläsningar påverkas av miljöfaktorer. Dessutom är det viktigt att skapa ett användarvänligt gränssnitt som gör det möjligt för idrottare att enkelt spåra och analysera sina prestandadata. / This thesis has been conducted with the objective of developing the Inno-X company's system, which is intended for everyday athletes to measure neuromuscular activities in the lower body using modern technology. The system includes an Inertial Measurement Unit (IMU) with an accelerometer, gyroscope, and an Electromyography (EMG) sensor. This configuration enables accurate monitoring of neuromuscular activities through the analysis of responses to training. The study's goal was to identify an effective sensor for measuring horizontal jump distances and to develop an algorithm that would then be integrated into the company's product. The product will use a magnetometer and IMU to interpret human movements and to improve the accuracy of the company's measurement system. The process includes improving the accuracy of measurements, integrating technology with biomechanical principles, evaluating calibration techniques for magnetometer readings, combining sensors for motion analysis, and conducting evaluations with different age groups consisting of 10 participants to assess the system's effectiveness. Although none of the methods fully achieved the desired accuracy within ±5 cm, all methods showed good performance for various applications. This suggests that the implementation of a calibrated magnetometer could potentially improve the system's accuracy in determining horizontal jump distances, albeit only by a small margin, as the study showed that with calibrated magnetometers, the Root Mean Square Error (RMSE) increased by 0.99 cm. Further research is recommended to explore new ways to calibrate sensors and integrate them for more precise readings. However, it should be considered that magnetometer readings are affected by environmental factors. Additionally, it is important to create a user-friendly interface that enables athletes to easily track and analyze their performance data.

Combining inertial measurements with LiDAR measurements for odometry and mapping is ubiquitous, but low-cost inertial measurement units are often noisy sensors, and it is suspected that naive integration of the measurements will result in deteriorated performance. The project explores under which circumstances integration of inertial measurements is beneficial for the robustness and accuracy of LiDAR odometry and mapping. It is already known that inertial measurements have the potential to improve performance, but when and how are rarely the main topics of study in existing literature. This project analyzes one way inertial measurements are used to compensate for motion while registering points from a spinning LiDAR, and compares this to a similar method that does not use inertial measurements. It is found that integration of inertial measurements is beneficial in cases of fast rotations of the sensor and that in other cases, it does not make a significant difference. The results can be explained with existing theory, and hence provide confidence in the theory for predicting behavior in similar systems. / Att kombinera tröghetsmätningar med LiDAR-mätningar för odometri och kartläggning är vanligt förekommande, men lågkostnadströghetsmätare är ofta brusiga sensorer och därmed misstänks att en naiv integrering av mätningarna skulle resultera i försämrad prestanda. Projektet utforskar i vilka fall det är gynnsamt att integrera tröghetsmätningar för LiDAR-odometri och kartläggning. Det är redan känt att tröghetsmätningar har potential att förbättra prestandan, men när och hur är sällan ett huvudområde i den befintliga litteraturen. Detta projekt undersöker ett sätt som tröghetsmätningar används på för att kompensera för rörelse i samband med registrering av punkter från en roterande LiDAR, och jämför detta med en liknande metod som inte använder tröghetsmätningar. Resultaten pekar på att integrering av tröghetsmätningar är gynnsamt i fall med hastiga rotationer av sensorn, i övriga fall observeras ingen avsevärd skillnad. Resultaten kan förklaras med befintlig teori, vilket styrker teorin och gör det möjligt att med högre tillit använda den för att förutse beteendet hos liknande system.

The goal of this project was to make a shirt with three embedded IMU sensors (Inertial Measurement Unit) that can measure a person’s movements throughout an entire workday. This can provide information about a person’s daily routine movements and aid in finding activities which can lead to work-related injuries in order to prevent them. The objective was hence to construct a sensor fusion framework that could retrieve the measurements from these three sensors and to create an estimate of the human body orientation and to estimate the angular movements of the arms. This was done using an extended Kalman filter which uses the accelerometer and magnetometer values to retrieve the direction of gravity and north respectively, thus providing a coordinate system that can be trusted in the long term. Since this method is sensitive to quick movements and magnetic disturbance, gyroscope measurements were used to help pick up quick movements. The gyroscope measurements need to be integrated in order to get the angle, which means that we get accumulated errors. This problem is reduced by the fact that we retrieve a correct long-term reference without accumulated errors from the accelerometer and magnetometer measurements. The Kalman filter estimates three quaternions describing the orientation of the upper body and the two arms. These quaternions were then translated into Euler angles in order to get a meaningful description of the orientations. The measurements were stored on a memory card or broadcast on both the local net and the Internet. These data were either used offline in Matlab or shown in real-time in the program Unity 3D. In the latter case the user could see that a movement gives rise to a corresponding movement on a skeleton model on the screen.

L'estimation de l'attitude (ou orientation) est un problème récurrent de nombreuses applications allant de la robotique aérienne ou sous-marine en passant par des applications médicales (surveillance de patients, réhabilitation), mais aussi jeux vidéo, etc. L'objectif de cette thèse est d'évaluer l'apport des approches ensemblistes dans le cadre de l'estimation de l'attitude à partir de données issues de triaxes accéléromètres (A), magnétomètres (M) et gyromètres (G). Dans un premier temps, on s'intéresse aux mouvements "quasi-statiques" et l'estimation de l'attitude est réalisée à partir de mesures AM. On aborde ensuite le cas des mouvements dynamiques, en considérant l'ensemble des mesures AGM. Le problème du choix de la paramétrisation de l'attitude a été abordé et on a comparé les résultats obtenus et le temps calcul pour des modélisations avec les angles de Cardan et le quaternion unitaire. Les algorithmes développés ont été validés en simulation et avec des données réelles. Les résultats ont été comparés avec ceux fournis par des algorithmes de l'état de l'art, par exemple SIVIA. La deuxième partie du manuscrit est consacrée à au diagnostic des capteurs de la centrale inertielle avec des approches ensemblistes. Les algorithmes développés dans la première partie du travail sont adaptés afin de pouvoir détecter et localiser un défaut dans l'ensemble des capteurs considérés. / Attitude estimation is one of the prominent problem encountered in various application areas such as Aerial and submarine robotics, bio-medical applications (elderly people monitoring, rehabilitation) but also, video game and augmented reality. The main objective of this PhD is to assess the capabilities of set-membership estimation in the field of attitude estimation when triaxes accelerometer (A) magnetometer (M) and rate gyros (G) are used. Quasi-static movements are first considered. In this case AM measurements are taken into account. Then the dynamic case is considered with AGM measurement taken into account in the set-membership estimation algorithm. The problem of attitude parametrisation is also studied as it will have a strong in uence on the computational time. The algorithms proposed during this work have been validated with simulated and real data. The second part of the report deals with Fault Detection and Isolation based upon set-membership approaches. The algorithms that have been developed in the first part of this work have been adapted to cope with diagnosis of a faulty sensor within the Inertial Measurement Unit.