This thesis project considers the potential use of inertial navigation on a consumer grade tablet mounted in a vehicle in an underground mine. The goal is to identify which sensors and techniques are useful and to design a navigation algorithm based on those results. The navigation algorithm is intended to work alongside the current received signal strength indication (RSSI) positioning system. Testing of the gyroscope, accelerometer and magnetometer sensors suggest that, while dead reckoning is likely not precise enough, an orientation filter can be designed that can be used for navigation. A complementary orientation filter using the gyroscope and accelerometer is then designed that shows better results than the default sensor fusion solutions available in Android. The filter is expandable and can come to include magnetometer data in the future. Based on the outputs of this filter, a navigation algorithm based onangle matching with map information is proposed. Precise positioning in an underground mine can be crucial to employee safety, and may also bring production benefits.

Yield monitors have become an indispensable part of precision agriculture systemsbecause of their ability to measure the yield variability. Accurate yield monitor data availabilityis essential for the assessment of farm practices. The current technology of measuring grainyields is prone to errors that can be attributed to mass flow variations caused by the mechanismswithin a grain combine. Because of throughput variations, there are doubts regarding thecorrelation between the mass flow measurement and the actual grain volume produced at aspecific location. Another inaccuracy observed in yield monitor data can be attributed to inexactcut-widths values entered by the machine operator.To effectively address these yield monitor errors, two crop mass flow sensing deviceswere developed and used to correct yield monitor data. The two quantities associated with cropmaterial mass flow that were sensed were tension on the feeder housing drive chain and thehydraulic pressure on the threshing cylinder's variable speed drive. Both sensing approacheswere capable of detecting zero mass flow conditions better than the traditional grain mass flowsensor. The alternative sensors also operate without being adversely affected by materialtransport delays. The feeder housing-based sensor was more sensitive to variations in cropmaterial throughput than the hydraulic pressure sensor. Crop mass flow is not a surrogate forgrain mass flow because of a weak relationship (R2 andlt; 0.60) between the two quantities. The cropmass flow signal does denote the location and magnitude of material throughput variations intothe combine. This delineation was used to redistribute grain mass flow by aligning grain andcrop mass flow transitions using sensor fusion techniques. Significant improvements (?? = 0.05)in yield distribution profile were found after the correction was applied.To address the cut-width entry error, a GIS-based post-processing algorithm wasdeveloped to calculate the true harvest area for each yield monitor data point. Based on theresults of this method, a combine operator can introduce yield calculation errors of 15%. Whenthese two correction methods applied to yield monitor data, the result is yield maps withdramatically improved yield estimates and enhanced spatial accuracy.

Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased. Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets. Recent advances in the field of multiple target tracking lead to the use of finite set statistics (FISST) in a set theoretic approach, where the targets and the measurements are treated as random finite sets (RFS). The first order moment of a RFS is called probability hypothesis density (PHD), and it is propagated in time with a PHD filter. In this thesis, the PHD filter is applied to radar data for constructing a parsimonious representation of the map of the stationary objects around the vehicle. Two original contributions, which exploit the inherent structure in the map, are proposed. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the PHD filter. Improvements in the merging step further simplify the map representation. When it comes to tracking moving targets, the focus of this thesis is on extended targets, i.e., targets which potentially may give rise to more than one measurement per time step. An implementation of the PHD filter, which was proposed to handle data obtained from extended targets, is presented. An approximation is proposed in order to limit the number of hypotheses. Further, a framework to track the size and shape of a target is introduced. The method is based on measurement generating points on the surface of the target, which are modeled by an RFS. Finally, an efficient and novel Bayesian method is proposed for approximating the tire radii of a vehicle based on particle filters and the marginalization concept. This is done under the assumption that a change in the tire radius is caused by a change in tire pressure, thus obtaining an indirect tire pressure monitoring system. The approaches presented in this thesis have all been evaluated on real data from both freeways and rural roads in Sweden. / SEFS -- IVSS / VR - ETT

<p>Crowdsourced live video streaming from users is on the rise. Several factors such as social networks, streaming applications, smartphones with high-quality cameras, and ubiquitous wireless connectivity are contributing to this phenomenon. Unlike isolated professional videos, live streams emerge at an unprecedented scale, poorly captured, unorganized, and lack user context. To utilize the full potential of this medium and enable new services on top, immediate addressing of open challenges is required. Smartphones are resource constrained -- battery power is limited, bandwidth is scarce, on-board computing power and storage is insufficient to meet real-time demand. Therefore, mobile cloud computing is cited as an obvious alternative where cloud does the heavy-lifting for the smartphone. But, cloud resources are not cheap and real-time processing demands more than what the cloud can deliver.</p><p>This dissertation argues that throwing cloud resources at these problems and blindly offloading computation, while seemingly necessary, may not be sufficient. Opportunities need to be identified to streamline big-scale problems by leveraging in device capabilities, thereby making them amenable to a given cloud infrastructure. One of the key opportunities, we find, is the cross-correlation between different streams of information available in the cloud. We observe that inferences on a single information stream may often be difficult, but when viewed in conjunction with other information dimensions, the same problem often becomes tractable.</p> / Dissertation

Pour assister la navigation piétonne dans les espaces urbains et intérieurs, une estimation précise de la pose (i.e. la position 3D et l'orientation3D) d'un équipement tenu en main constitue un point essentiel dans le développement d'outils d'aide à la mobilité (e.g. applications de réalité augmentée). Dans l'hypothèse où le piéton n'est équipé que d'appareils grand public, l'estimation de la pose est limitée à l'utilisation de capteurs à faible coût intégrés dans ces derniers (i.e. un récepteur GNSS, une unité de mesure inertielle et magnétique et une caméra monoculaire). De plus, les espaces urbains et intérieurs, comprenant des bâtiments proches et des éléments ferromagnétiques, constituent des zones difficiles pour la localisation et l'estimation de la pose lors de grands déplacements piétons.Cependant, le développement récent et la mise à disposition d'informations contenues dans des Systèmes d'Information Géographiques 3D constituent une nouvelle source de données exploitable pour la localisation et l'estimation de la pose. Pour relever ces défis, cette thèse propose différentes solutions pour améliorer la localisation et l'estimation de la pose des équipements tenus en main par le piéton lors de ses déplacements en espaces urbains et intérieurs. Les solutions proposées intègrent l'estimation de l'attitude basée inertielle et magnétique, l'odométrie visuelle monoculaire mise à l'échelle grâce à l'estimation des déplacements du piéton, l'estimation absolue de la pose basée sur la reconnaissance d'objets SIG 3D parfaitement connus et la mise à jour en position de la navigation à l'estime du piéton.Toutes ces solutions s'intègrent dans un processus de fusion permettant d'améliorer la précision de la localisation et d'estimer en continu une pose qualifiée de l'appareil tenu en main.Cette qualification est nécessaire à la mise en place d'un affichage en réalité augmentée sur site. Pour évaluer les solutions proposées, des données expérimentales ont été recueillies au cours de déplacements piétons dans un espace urbain avec des objets de référence et des passages intérieurs. / To support pedestrian navigation in urban and indoor spaces, an accurate pose estimate (i.e. 3Dposition and 3D orientation) of an equipment held inhand constitutes an essential point in the development of mobility assistance tools (e.g.Augmented Reality applications). On the assumption that the pedestrian is only equipped with general public devices, the pose estimation is restricted to the use of low-cost sensors embedded in the latter (i.e. an Inertial and Magnetic Measurement Unit and a monocular camera). In addition, urban and indoor spaces, comprising closely-spaced buildings and ferromagnetic elements,constitute challenging areas for localization and sensor pose estimation during large pedestrian displacements.However, the recent development and provision of data contained in 3D Geographical Information System constitutes a new wealth of data usable for localization and pose estimation.To address these challenges, this thesis proposes solutions to improve pedestrian localization and hand-held device pose estimation in urban and indoor spaces. The proposed solutions integrate inertial and magnetic-based attitude estimation, monocular Visual Odometry with pedestrian motion estimation for scale estimation, 3D GIS known object recognition-based absolute pose estimation and Pedestrian Dead-Reckoning updates. All these solutions are fused to improve accuracy and to continuously estimate a qualified pose of the handheld device. This qualification is required tovalidate an on-site augmented reality display. To assess the proposed solutions, experimental data has been collected during pedestrian walks in an urban space with sparse known objects and indoors passages.

O número de aplicações industriais para ambientes de “Realidade Aumentada” (AR) e “Realidade Virtual” (VR) tem crescido de forma significativa nos últimos anos. Sistemas óticos de rastreamento (optical tracking systems) constituem um importante componente dos ambientes de AR/VR. Este trabalho propõe um sistema ótico de rastreamento de baixo custo e com características adequadas para uso profissional. O sistema opera na região espectral do infravermelho para trabalhar com ruído ótico reduzido. Uma câmera de alta velocidade, equipada com filtro para bloqueio da luz visível e com flash infravermelho, transfere imagens de escala de cinza não comprimidas para um PC usual, onde um software de pré-processamento de imagens e o algoritmo PTrack de rastreamento reconhecem um conjunto de marcadores retrorefletivos e extraem a sua posição e orientação em 3D. É feita neste trabalho uma pesquisa abrangente sobre algoritmos de pré-processamento de imagens e de rastreamento. Uma bancada de testes foi construída para a realização de testes de acurácia e precisão. Os resultados mostram que o sistema atinge níveis de exatidão levemente piores, mas ainda comparáveis aos de sistemas profissionais. Devido à sua modularidade, o sistema pode ser expandido através do uso de vários módulos monoculares de rastreamento interligados por um algoritmo de fusão de sensores, de modo a atingir um maior alcance operacional. Uma configuração com dois módulos foi montada e testada, tendo alcançado um desempenho semelhante à configuração de um só módulo. / In the last years the number of industrial applications for Augmented Reality (AR) and Virtual Reality (VR) environments has significantly increased. Optical tracking systems are an important component of AR/VR environments. In this work, a low cost optical tracking system with adequate attributes for professional use is proposed. The system works in infrared spectral region to reduce optical noise. A highspeed camera, equipped with daylight blocking filter and infrared flash strobes, transfers uncompressed grayscale images to a regular PC, where image pre-processing software and the PTrack tracking algorithm recognize a set of retro-reflective markers and extract its 3D position and orientation. Included in this work is a comprehensive research on image pre-processing and tracking algorithms. A testbed was built to perform accuracy and precision tests. Results show that the system reaches accuracy and precision levels slightly worse than but still comparable to professional systems. Due to its modularity, the system can be expanded by using several one-camera tracking modules linked by a sensor fusion algorithm, in order to obtain a larger working range. A setup with two modules was built and tested, resulting in performance similar to the stand-alone configuration.

O número de aplicações industriais para ambientes de “Realidade Aumentada” (AR) e “Realidade Virtual” (VR) tem crescido de forma significativa nos últimos anos. Sistemas óticos de rastreamento (optical tracking systems) constituem um importante componente dos ambientes de AR/VR. Este trabalho propõe um sistema ótico de rastreamento de baixo custo e com características adequadas para uso profissional. O sistema opera na região espectral do infravermelho para trabalhar com ruído ótico reduzido. Uma câmera de alta velocidade, equipada com filtro para bloqueio da luz visível e com flash infravermelho, transfere imagens de escala de cinza não comprimidas para um PC usual, onde um software de pré-processamento de imagens e o algoritmo PTrack de rastreamento reconhecem um conjunto de marcadores retrorefletivos e extraem a sua posição e orientação em 3D. É feita neste trabalho uma pesquisa abrangente sobre algoritmos de pré-processamento de imagens e de rastreamento. Uma bancada de testes foi construída para a realização de testes de acurácia e precisão. Os resultados mostram que o sistema atinge níveis de exatidão levemente piores, mas ainda comparáveis aos de sistemas profissionais. Devido à sua modularidade, o sistema pode ser expandido através do uso de vários módulos monoculares de rastreamento interligados por um algoritmo de fusão de sensores, de modo a atingir um maior alcance operacional. Uma configuração com dois módulos foi montada e testada, tendo alcançado um desempenho semelhante à configuração de um só módulo. / In the last years the number of industrial applications for Augmented Reality (AR) and Virtual Reality (VR) environments has significantly increased. Optical tracking systems are an important component of AR/VR environments. In this work, a low cost optical tracking system with adequate attributes for professional use is proposed. The system works in infrared spectral region to reduce optical noise. A highspeed camera, equipped with daylight blocking filter and infrared flash strobes, transfers uncompressed grayscale images to a regular PC, where image pre-processing software and the PTrack tracking algorithm recognize a set of retro-reflective markers and extract its 3D position and orientation. Included in this work is a comprehensive research on image pre-processing and tracking algorithms. A testbed was built to perform accuracy and precision tests. Results show that the system reaches accuracy and precision levels slightly worse than but still comparable to professional systems. Due to its modularity, the system can be expanded by using several one-camera tracking modules linked by a sensor fusion algorithm, in order to obtain a larger working range. A setup with two modules was built and tested, resulting in performance similar to the stand-alone configuration.

O número de aplicações industriais para ambientes de “Realidade Aumentada” (AR) e “Realidade Virtual” (VR) tem crescido de forma significativa nos últimos anos. Sistemas óticos de rastreamento (optical tracking systems) constituem um importante componente dos ambientes de AR/VR. Este trabalho propõe um sistema ótico de rastreamento de baixo custo e com características adequadas para uso profissional. O sistema opera na região espectral do infravermelho para trabalhar com ruído ótico reduzido. Uma câmera de alta velocidade, equipada com filtro para bloqueio da luz visível e com flash infravermelho, transfere imagens de escala de cinza não comprimidas para um PC usual, onde um software de pré-processamento de imagens e o algoritmo PTrack de rastreamento reconhecem um conjunto de marcadores retrorefletivos e extraem a sua posição e orientação em 3D. É feita neste trabalho uma pesquisa abrangente sobre algoritmos de pré-processamento de imagens e de rastreamento. Uma bancada de testes foi construída para a realização de testes de acurácia e precisão. Os resultados mostram que o sistema atinge níveis de exatidão levemente piores, mas ainda comparáveis aos de sistemas profissionais. Devido à sua modularidade, o sistema pode ser expandido através do uso de vários módulos monoculares de rastreamento interligados por um algoritmo de fusão de sensores, de modo a atingir um maior alcance operacional. Uma configuração com dois módulos foi montada e testada, tendo alcançado um desempenho semelhante à configuração de um só módulo. / In the last years the number of industrial applications for Augmented Reality (AR) and Virtual Reality (VR) environments has significantly increased. Optical tracking systems are an important component of AR/VR environments. In this work, a low cost optical tracking system with adequate attributes for professional use is proposed. The system works in infrared spectral region to reduce optical noise. A highspeed camera, equipped with daylight blocking filter and infrared flash strobes, transfers uncompressed grayscale images to a regular PC, where image pre-processing software and the PTrack tracking algorithm recognize a set of retro-reflective markers and extract its 3D position and orientation. Included in this work is a comprehensive research on image pre-processing and tracking algorithms. A testbed was built to perform accuracy and precision tests. Results show that the system reaches accuracy and precision levels slightly worse than but still comparable to professional systems. Due to its modularity, the system can be expanded by using several one-camera tracking modules linked by a sensor fusion algorithm, in order to obtain a larger working range. A setup with two modules was built and tested, resulting in performance similar to the stand-alone configuration.

Orientador: Carlos Alberto dos Reis Filho / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-05T17:36:56Z (GMT). No. of bitstreams: 1 Salustiano_RogerioEsteves_M.pdf: 3698724 bytes, checksum: a5c6d59ec19db38f5a0324243ddb1eb5 (MD5) Previous issue date: 2006 / Resumo: Este trabalho propõe um sistema computacional no qual são aplicadas técnicas de Fusão de Sensores no monitoramento de ambientes. O sistema proposto permite a utilização e incorporação de diversos tipos de dados, incluindo imagens, sons e números em diferentes bases. Dentre os diversos algoritmos pertinentes a um sistema como este, foram implementados os de Sensores em Consenso que visam a combinação de dados de uma mesma natureza. O sistema proposto é suficientemente flexível, permitindo a inclusão de novos tipos de dados e os correspondentes algoritmos que os processem. Todo o processo de recebimento dos dados produzidos pelos sensores, configuração e visualização dos resultados é realizado através da Internet / Abstract: This work proposes a computer system in which Sensor Fusion techniques are applied to monitoring the environment. The proposed system allows the use and incorporation of different data types, including images, sounds and numbers in different bases. Among the existing algorithms that pertain to a system like this, those, which aim to combine data of the same nature, called Consensus Sensors, have been particularly implemented. The proposed system is flexible enough and allows the inclusion of new data types and their corresponding algorithms. The whole process of receiving the data produced by the sensors, configuration of produced results as well as their visualization is performed through the Internet / Mestrado / Eletrônica, Microeletrônica e Optoeletrônica / Mestre em Engenharia Elétrica

In recent years researchers has focused on the development of techniques for multi-sensor data fusion systems. Data fusion systems process data from multiple sensors to develop improved estimate of the position, velocity, attributes and identity of entities such as the targets or entities of interest. Visualizing sensor data from fused data to raw data from each sensor help analysts to interpret the data and assess sensor data fusion platform, an evolving situation or threats. Immersive visualization has emerged as an ideal solution for exploration of sensor data and provides opportunities for improvement in multi sensor data fusion. The thesis aims to investigate possibilities of applying information visualization to sensor data fusion platform in Volvo. A visualization prototype is also developed to enables multiple users to interactively visualize Sensor Data Fusion platform in real-time, mainly in order to demonstrates, evaluate and analyze the platform functionality. In this industrial study two research methodologies were used; a case study and an experiment for evaluating the results. First a case study was conducted in order to find the best visualization technique for visualizing sensor data fusion platform. Second an experiment was conducted to evaluate the usability of the prototype that has been developed and make sure the user requirement were met. The visualization tool enabled us to study the effectiveness and efficiency of the visualization techniques used. The results confirm that the visualization method used is effective, efficient for visualizing sensor data fusion platform.