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A proposta deste trabalho é contribuir com a construção de uma plataforma de veículo autônomo para viabilizar as pesquisas na área pelo Departamento de Engenharia de Transportes da USP. Até o momento o departamento dispõe de uma plataforma que, a partir de sua posição conhecida, consegue navegar autonomamente até um ponto de destino utilizando apenas uma solução GNSS, no caso, GPS. Para ampliar a mobilidade da plataforma, está sendo sugerida aqui, a adição de sensores inerciais ao veículo, para que ele consiga obter uma solução de posição mesmo em áreas sem cobertura GNSS. Um Sistema de Navegação Inercial não depende de infraestrutura externa, exceto para inicializar suas variáveis, o que neste caso pode ser feito com auxílio de um receptor GPS. Sensores inerciais de alto desempenho são caros, tem alta complexidade mecânica e em geral são de grande porte. A alternativa é o uso de sensores do tipo MEMS que são pequenos, fáceis de serem manipulados e apresentam baixo consumo de energia. A contrapartida é que a solução é mais susceptível a ruído do que seus pares que custam na faixa de centena de milhões de dólares. / The proposal of this paper is to build an autonomous vehicle platform to enable the researches in this area by the Transport Engineering Department of the USP. Until now the Department has a platform that, once its initial position is known, it can navigate autonomously to a destination point using only the GNSS, in this case, GPS. To expand the mobility resources of the platform, it is being suggested here the addition of inertial sensors to the vehicle, enabling it to acquire a position solution even in areas where there is no coverage of the GNSS. An Inertial Navigation System does not depend on an external infra-structure, with the exception on the initial setup, where the GPS can be used to provide this kind of initialization. High performance inertial sensors are expensive, have high mechanical complexity and in general are big. The alternative is the usage of MEMS sensors, which are small, easy to handle and has low power consumption. In the opposite side this solution is more susceptible to noises in comparison to those High performance sensors that cost hundreds of thousands of dollars.

A proposta deste trabalho é contribuir com a construção de uma plataforma de veículo autônomo para viabilizar as pesquisas na área pelo Departamento de Engenharia de Transportes da USP. Até o momento o departamento dispõe de uma plataforma que, a partir de sua posição conhecida, consegue navegar autonomamente até um ponto de destino utilizando apenas uma solução GNSS, no caso, GPS. Para ampliar a mobilidade da plataforma, está sendo sugerida aqui, a adição de sensores inerciais ao veículo, para que ele consiga obter uma solução de posição mesmo em áreas sem cobertura GNSS. Um Sistema de Navegação Inercial não depende de infraestrutura externa, exceto para inicializar suas variáveis, o que neste caso pode ser feito com auxílio de um receptor GPS. Sensores inerciais de alto desempenho são caros, tem alta complexidade mecânica e em geral são de grande porte. A alternativa é o uso de sensores do tipo MEMS que são pequenos, fáceis de serem manipulados e apresentam baixo consumo de energia. A contrapartida é que a solução é mais susceptível a ruído do que seus pares que custam na faixa de centena de milhões de dólares. / The proposal of this paper is to build an autonomous vehicle platform to enable the researches in this area by the Transport Engineering Department of the USP. Until now the Department has a platform that, once its initial position is known, it can navigate autonomously to a destination point using only the GNSS, in this case, GPS. To expand the mobility resources of the platform, it is being suggested here the addition of inertial sensors to the vehicle, enabling it to acquire a position solution even in areas where there is no coverage of the GNSS. An Inertial Navigation System does not depend on an external infra-structure, with the exception on the initial setup, where the GPS can be used to provide this kind of initialization. High performance inertial sensors are expensive, have high mechanical complexity and in general are big. The alternative is the usage of MEMS sensors, which are small, easy to handle and has low power consumption. In the opposite side this solution is more susceptible to noises in comparison to those High performance sensors that cost hundreds of thousands of dollars.

Au cours des dernières années, les drones civils sont devenus un outil intéressant dans la photographie aérienne et dans les travaux de photogrammétrie. Cela a poussé le LOEMI (Laboratoire d’Opto-Electronique, Métrologie et Instrumentation) de l'IGN (Institut National de l'Information Géographique et Forestière) de mettre au point une nouvelle caméra aérienne mieux adaptée pour l'exploitation photogrammétrique et métrologiques des images que les caméras grand public. Cette caméra est composée essentiellement d'un capteur CMOS ``global-shutter'', d'une centrale inertielle IMU, et d'un système sur puce (FPGA + 2 CPUs) pour la gestion de l'acquisition des images. Ce SoC/FPGA ouvre la porte à l'implémentation temps-réel des algorithmes de traitement d'image. Parmi les travaux futurs de l'IGN, on peut distinguer certaines applications qui nécessitent l'acquisition des images aériennes avec un temps d'exposition long, comme par exemple les prises de vue aériennes en bande spectrale étroite et les prises de vue aériennes de nuit. Ce type de prises de vue manifeste un flou de bougé dans les images dû aux mouvements erratiques du drone. Cette thèse consiste en l'implémentation dans la caméra légère de l'IGN d'un algorithme qui permet de remédier ce problème de flou de bougé. La première partie de ce travail a été consacrée au développement de la méthode qui consiste à acquérir plusieurs images avec un temps de pose court, puis utiliser un algorithme de traitement d'image afin de générer une image empilée finale avec l'équivalent d'un temps de pose long. Les paramètres des orientations correctes pour le ré-échantillonnage des images sont obtenus par l'estimation de la transformation géométrique entre la première image et la nième image à partir des points d'intérêts détectés par FAST dans la première image et les points homologues obtenus par corrélation dans les autres images accélérées par les capteurs inertiels intégrés à la caméra. Afin d'accélérer le traitement de calcul de notre algorithme, certaines phases sont accélérées en les implémentant dans le matériel (SoC/FPGA).Les résultats obtenus sur des jeux de tests acquis avec un drone type Copter 1B UAV et la caméra ultra-légère de l'IGN montrent que l'image finale empilée ne présente pas un flou de bougé. Les résultats de temps des différentes phases de l'algorithme montrent aussi que l'exécution de notre algorithme a un temps quasi-nulle. Un des sous-produits intéressants de ces travaux est la ré-étalonnage des capteurs IMU / In the recent years, the civilian UAVs (Unmanned Aerial Vehicles) have become an interesting tool in aerial photography and in photogrammetry. This led the LOEMI (Laboratoire d'Opto-électronique, de Métrologie et d'Instrumentation) team of IGN (Institut National de l'Information Géographique) to design an light-weight digital camera better adapted for exploiting photogrammetry and metrology applications than consumer cameras. This camera consists essentially of a CMOS ``global shutter'' sensor, an inertial measurement unit IMU, and a system on chip (FPGA + 2 CPUs) used originally to acquire image data from the sensor. This SoC/FPGA-based camera opens the door to implement in hardware some real-time image processing algorithms. Night-time surveys and narrow spectral band with imagery are one of the next applications targeted by IGN, this type of applications needs a long-exposure time imagery that usually exhibits a motion blur due to erratic movements of the UAV. This thesis consists in the implementation on the light-weight IGN camera of an alogirithm which makes it possible to remedy this problem of motion blur. The first part of this work was devoted to the development of the method which consists in acquiring several images with a short exposure time and then using an image processing algorithm in order to generate a stacked image with the equivalent of a long-exposure time. To obtain the correct parameters for the resampling of images, the presented method accurately estimates the geometrical relation between the first and the nth image, taking into account the internal parameters and thedistortion of the camera. Features are detected in the first image by the FAST detector, than homologous points on other images are obtained by template matching aided by the IMU sensors. In order to speed up the processing of our algorithm, some phases are accelerated by implementing them in the hardware (SoC / FPGA).The results obtained on real surveys show that the final stacked image does not present a motion blur. The time results of the different phases of the algorithm also show that the execution of the algorithm has a quasi-zero time. One of the interesting byproducts of this work is the recalibration of the IMU sensors

Detta examensarbete utförs tillsammans med Ålö AB som tillverkar frontlastare under varumärket Quike och ämnar utvärdera olika möjliga lösningar för ett system som beräknar inklinationen på fordon. Systemet ska utformas med låg kostnad som prioritet och skall klara hårdvaruklassificeringar för elektronik tillämpat på fordon (AEC-Q100). Arbetet behandlar i huvudsak bestämmande- och motivation av komponentval, teori kring den signalbehandling som behövs i miljöer där en stor mängd brus förekommer samt implementationen av detta.   I arbetet valdes två sensorer som ansågs lämpliga för utvärdering. Den första var en 3-axlad accelerometer till vilken en proprietär 2D-positioneringsalgoritm används för att bestämma inklination. Den andra sensorn som valdes är en 6-axlad sensor som kombinerar accelerometer och gyroskop vilken implementeras med sensorfusion och Kalman-filter genom användning av ett mjukvarubibliotek. Systemet implementerades hårdvarumässigt i form av ett kretskort som huserar båda sensorerna.   Systemet testades och utvärderades stationärt och på fordon tillsammans med en referenssensor för att ge en bild av hur de respektive sensorerna och algoritmerna presterade i både ideala och verkliga förhållanden. Vid de stationära testerna uppvisar båda lösningarna goda egenskaper och ger inklination nära verklig vinkel. Traktortesternas tillförlitlighet kan höjas och håller inte nog hög kvalitet för att kunna leda till slutsats, vilket leder till en rekommendation om fortsatt utvecklingsarbete. / This student thesis is carried out togheter with Ålö AB which manufactures front loaders under the Quicke brand and intents to evaluate a system that calculates vehicle incline. The system should prioritize low cost and must use electronics that is classified for vehicle use (AEC-Q100). This thesis deals with selecting components, sensors and motivations of the selected components, theory of sensor signal conditioning in noisy environments and its implementation.   Two sensors where selected that was deemed suitable for evaluation. The first was a 3-axis accelerometer for which a proprietary 2D-positioning algorithm was used to calculate incline. The second sensor that was chosen was a 6-axis IMU that was implemented using sensor fusion with a Kalman filter which was realized with an available software library. The system hardware was implemented with a custom PCB.   The system was tested and evaluated stationary and, on a vehicle, along with a reference sensor to give an indication of how the sensors and their respective algorithms performed in both ideal and a real conditions. The stationary tests showed that both solutions perform well regarding outputting incline close to the actual angle. The reliability of the vehicle tests can be improved and as the results they yielded are not of high enough quality to draw conclusions from them. This leads to a recommendation of further testing and evaluation of both solutions.

Simning är en sport där små marginaler har stor inverkan på resultatet. I OS-finalen 2008 vann Michael Phelps över Milorad Čavić med en hundradels sekund. För att få marginalen på sin sida behöver simmaren träna otaliga timmar. I dagsläget har Kungliga Tekniska Högskolan inte en bra prototyp för att testa, utvärdera och mäta en simmares träning. Att kontinuerligt under ett helt träningspass få mätvärden som kan användas för att förbättra simtekniken skulle kunna ge en stor fördel. Målet med det här examensarbetet är att utvärdera möjligheten att utveckla en metod för att mäta dessa värden. Metoden som valdes var att med en IMU och mikrokontroller mäta rörelser. En jämförelseanalys gjordes mellan mikrokontrollers för att ge plattformen bästa utvecklingsmöjligheter. Efter resultatet från jämförelseanalysen införskaffades de två olika mikrokontrollers som hade störst potential och dessa två jämfördes vidare. Därefter utvecklades flera möjliga designer för plattformen med störst potential som kan användas för att kvantifiera simträning. Resultatet blev två funktionella protyper som uppfyllde de krav som ställdes. Framtida utveckling skulle vara att testa och utvärdera plattformarna vidare och hitta andra sporter och användningsområden. / Swimming is a sport where small margins can have a big impact on the result. In the 2008 Olympic finals Michael Phelps won over Milorad Čavić by one hundredth of a second. To get the margin on their side, the swimmers need to practice countless hours. Currently, the Royal Institute of Technology does not have a good prototype for testing, evaluating, and measuring a swimmer's training. Continuously getting measured values during an entire training session that can be used to improve a swimming technique could provide a great advantage. The aim of this thesis is to evaluate the possibility of developing a method for measuring these metrics. The method chosen was to measure movements using an IMU and microcontroller. A comparison analysis was made between microcontrollers to provide the platform with the best development opportunities. Following the results of the comparison analysis, two different microcontrollers with the greatest potential were acquired and these two were further compared. Subsequently, several possible designs were developed for the platform with the greatest potential that can be used to quantify swim training. The result was two functional prototypes that met the requirements. Future development would be to test and evaluate the platforms further and find other sports and areas of use.

Longline fishing is a method of fishing that utilizes baited hooks to catch fish in an environmentally friendly way. In order to reduce the number of catch lost while longline fishing, it is of great interest to be able to keep an even tension on the fishing line. This can be done by estimating the speed at the point of interest (POI) at which the fishing line is attached to the boat. Due to the harsh conditionson the seas, it is not recommended to put any sensors directly at that point. The aim of this thesis was to explore whether or not it is possible to estimate the vertical speed at the POI by having sensors measuring linear acceleration and angular velocity at various unknown places in the boat. The sensors were placed at various places in a simulated boat, after which the sensor orientations and positions were calculated using a nonlinear Least Squares method. After the sensors were positioned, an Extended Kalman Filter (EKF) was implemented on each sensor, after which the speed of the POI was calculated as the fused estimate of all EKFs. By changing the number of sensors and their sampling times, the best compromise between accuracy, computational load and number of sensors was found. The results prove that it is fully possible to estimate the vertical speed of the POI using only four 6DOF IMU:s using a sampling time of 50 or 100 ms, depending on how accurate the user wants the estimated positions of the sensors to be. However, there are still many ways in which the method used can be improved to geta better estimate.

Försvarsmakten gav KTH:s omgivningsfysiologiavdelning i uppdrag att studera energiåtgången vid gång med night vision goggles. För att ta reda på om rörelsemönstret påverkades av night vision goggles och därmed skulle ha inverkan på energiåtgången så samlades data in med inertial measurement units (IMU), men någon fullständig analys på datan gjordes inte. För att undersöka datan från IMU skapades en mjukvara i Python. Gångcykelns karaktäristik identifierades från datan för vinkelhastighet och acceleration från en IMU på vänster ankel. Position beräknades stegvis med dubbelintegration, på så vis analyserades rörelsen för varje individuellt steg. Träffsäkerheten för gångcyklarnas medelfrekvens med nämnda metod hamnade inom 5% mot tidigare validerad data med analys av motion capture system i inomhusförsök. Andra stegparametrar som steglängd och steghöjd antar dock helt orimliga värden. Vi tror att denna orimlighet till stor del beror på tekniska fel vid insamlingen av rådatan som orsakat dataförlust då liknande metoder med framgång använts av andra inom området. Rapporten beskriver utöver metoden för själva analysen även metoden för att skapa ett användarvänligt gränssnitt för forskare att utföra den med. / The Swedish Armed Forces asked the Division of Environmental Physiology at KTH to study the energy demand whilst walking with night vision goggles. To explore whether the patterns of movement changed and hence impacted energy use, data was gathered with inertial measurement units (IMU). However, no complete analysis of the collected data was performed. To study this data, software was created in Python. The gait cycle characteristics were identified from angular velocity and acceleration data from an IMU attached to the left ankle. Position was calculated stepwise by double integration, allowing for analysis of each individual step. Mean step frequency was calculated within 5% accuracy of earlier results validated by analysis with a motion capture system indoors. Other step parameters like stride length and height on the other hand take on completely unreasonable values. We believe this to largely be due to technical errors during the collection of the data leading to data loss, since similar methods have successfully been used by others in the field. The report also discusses the production of software with a user-friendly interface to be used by the scientists performing the final analysis.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / The US ARMY, ARDEC; in cooperation with AMCOM AMRDEC, Missile Guidance and Engineering Directorates; the Office of Naval Research; Naval Surface Fire Support; and the Naval Surface Weapon Center, requires multiphase development of a common, low-cost, high G survivable, high accuracy, Micro Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU) and Common, Deeply Integrated, Guidance and Navigation Unit (DI-GNU) for DoD gun launched guided munition and missile applications. The challenge for the Precision Munition Instrumentation Division (PMID) was to develop a Telemetry System to record the interior and exterior ballistics of a M831 TP-T projectile, which will be used as a carrier for soft recovery testing of IMUs and GNUs. This valuable data that would help The Government and contractors develop and validate multiple MEMS IMU design efforts, culminating with live fire verification performance test of pre-production in the Army’s 155-mm Soft Recovery Vehicle (SRVs) and missiles airframes.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The loss of radio frequency (RF) spectrum for use in testing has steadily increased the likelihood that users of the few remaining frequencies available to test ranges will experience scheduling conflicts and interference with nontest users. A gradual increase in the base of test customers engaged in scientific, military, and commercial R&D, point toward a near term situation in which more test customers will be competing for fewer frequencies. The test ranges, often operating in close geographical proximity with other communications-intensive functions as well as with each other, will also encounter increasing out-of-band and adjacent-channel interference. This projected growth of R&Drelated testing constrained to operate in a diminished RF spectrum (and a more confined test space), will undoubtedly stimulate the development of new products that make more efficient use of the RF spectrum. This paper describes one such innovative approach to spectrum sharing. The authors assess the operational need for an affordable miniaturized avionics instrument package based on a C-band radar transponder integrated with a Global Positioning System/Inertial Measurement Unit (GPS/IMU). The proposed approach would make use of frequencies already allocated for use by existing C-band aeronautical transponders. It would augment the format of the transponder output data to include the vehicle position obtained from an onboard GPS/IMU. Existing range instrumentation radars, such as the venerable AN/FPS-16, could be modified with lowcost upgrade kits to provide uniformly higher accuracy over the entire transponder coverage range.