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Autonomous vehicles, from self driving cars to small sized unmanned aircraft, is a hotly contested market experiencing significant growth. As a result, fundamental concepts of autonomous vehicle navigation, such as simultaneous localisation and mapping (SLAM) are very active fields of research garnering significant interest in the drive to improve effectiveness. Traditionally, SLAM has been performed by filtering methods but several improvements have brought smoothing and mapping (SAM) based methods to the forefront of SLAM research. Although recent works have made such methods incremental, they retain some batch functionalities from their bundle-adjustment origins. More specifically, re-linearisation and column reordering still require the full re-computation of the solution. In this thesis, the problem of re-computation after column reordering is addressed. A novel method to reflect changes in ordering directly on the Cholesky factor, called Factor Recovery, is proposed. Under the assumption that changes to the ordering are small and localised, the proposed method can be executed faster than the re-computation of the Cholesky factor. To define each method’s optimal region of operation, a function estimating the computational cost of Factor Recovery is derived and compared with the known cost of Cholesky factorisation obtained using experimental data. Combining Factor Recovery and traditional Cholesky decomposition, the Hybrid Cholesky decomposition algorithm is proposed. This novel algorithm attempts to select the most efficient algorithm to compute the Cholesky factor based on an estimation of the work required. To obtain experimental results, the Hybrid Cholesky decomposition algorithm was integrated in the SLAM++ software and executed on popular datasets from the literature. The proposed method yields an average reduction of 1.9 % on the total execution time with reductions of up to 31 % obtained in certain situations. When considering only the time spend performing reordering and factorisation for batch steps, reductions of 18 % on average and up to 78 % in certain situations are observed.

La présente thèse en création littéraire comporte deux parties. La première est un recueil de poésie divisé entre les deux types de poésies orales que nous avons étudiés : le spoken word poetry et le slam de poésie. À travers cette expérience de création, notre objectif était de montrer les différences entre les deux styles ainsi que leur courbe évolutive. Pour y arriver, nous avons pris comme point de départ un poème dans le style plus traditionnel de la poésie. À partir de ce texte « sacrifié » (selon la méthode mise en place durant les soirées de Slam), nous avons donc organisé les poèmes en suivant notre adaptation aux exigences du slam de poésie et du spoken word poetry. À travers ce travail, nous avons aussi dressé un plan thématique pour compléter l’organisation du recueil. La seconde partie est composée de deux chapitres. Le premier est une étude théorique sur les différences entre les deux styles de poésie : il serait alors question de l’évolution progressive d’une poésie orale vers une autre en plus des orientations prises par chacune au fil des années. Comme genre littéraire, le spoken word, qui reste plus près des sources, est beaucoup plus libre que le Slam, qui s’implante dans le milieu actuel avec ses thèmes et doit suivre les règles rigides de la compétition. Le deuxième chapitre est un retour réflexif sur ma propre création à partir des notions vues au premier chapitre. Il explique en profondeur l’organisation de mon recueil de même que le plan thématique de ses deux parties.

Mobile Road Mapping Systems (MRMS) are the current solution to the growing demand for high definition road surface maps in wide ranging applications from pavement management to autonomous vehicle testing. The focus of this research work is to improve the accuracy of MRMS by using the principles of Simultaneous Localization and Mapping (SLAM). First a framework for describing the sensor measurement models in MRMS is developed. Next the problem of estimating the road surface from the set of sensor measurements is formulated as a SLAM problem and two approaches are proposed to solve the formulated problem. The first is an incremental solution wherein sensor measurements are processed in sequence using an Extended Kalman Filter (EKF). The second is a post-processing solution wherein the SLAM problem is formulated as an inference problem over a factor graph and existing factor graph SLAM techniques are used to solve the problem. For the mobile road mapping problem, the road surface being measured is one the primary inputs to the dynamics of the MRMS. Hence, concurrent to the main objective this work also investigates the use of the dynamics of the host vehicle of the system to improve the accuracy of the MRMS. Finally a novel method that builds off the concepts of the popular model fitting algorithm, Random Sampling and Consensus (RANSAC), is developed in order to identify outliers in road surface measurements and estimate the road elevations at grid nodes using these measurements. The developed methods are validated in a simulated environment and the results demonstrate a significant improvement in the accuracy of MRMS over current state-of-the art methods. / Doctor of Philosophy / Mobile Road Mapping Systems (MRMS) are the current solution to the growing demand for high definition road surface maps in wide ranging applications from pavement management to autonomous vehicle testing. The objective of this research work is to improve the accuracy of MRMS by investigating methods to improve the sensor data fusion process. The main focus of this work is to apply the principles from the field of Simultaneous Localization and Mapping (SLAM) in order to improve the accuracy of MRMS. The concept of SLAM has been successfully applied to the field of mobile robot navigation and thus the motivation of this work is to investigate its application to the problem of mobile road mapping. For the mobile road mapping problem, the road surface being measured is one the primary inputs to the dynamics of the MRMS. Hence this work also investigates whether knowledge regarding the dynamics of the system can be used to improve the accuracy. Also developed as part of this work is a novel method for identifying outliers in road surface datasets and estimating elevations at road surface grid nodes. The developed methods are validated in a simulated environment and the results demonstrate a significant improvement in the accuracy of MRMS over current state-of-the-art methods.

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La navigation autonome est l'un des plus grands challenges pour un robot autonome. Elle nécessite la capacité à localiser sa position ou celle de l'objectif et à trouver le meilleur chemin connectant les deux en évitant les obstacles. Pour cela, les robots utilisent une carte de l'environnement modélisant sa géométrie ou sa topologie. Cependant la construction d'une telle carte dans des environnements de grande dimension est ardue du fait de la quantité de données à traiter et le problème de la localisation peut devenir insoluble. De plus, un environnement changeant peut conduire à l'obsolescence rapide du modèle. Comme démontré dans cette thèse, l'ajout d'information de nature sémantique dans ces cartes améliore significativement les performances de navigation des robots dans des environnements réels. La labélisation d'image permet de construire des modèles extrêmement compacts qui sont utilisés pour la localisation rapide en utilisant une approche basée comparaison de graphes. Ils sont des outils puissants pour comprendre l'environnement et permettent d'étendre la carte au-delà des limites perceptuelles du robot. L'analyse statistique de ces modèles est utilisée pour construire un embryon de sens commun qui est ensuite utilisé pour détecter des erreurs de labélisation et pour mettre à jour la carte en utilisant des algorithmes conçus pour maintenir une représentation stable en dépits des occlusions créées par les objets dynamiques. Finalement, la sémantique est utilisées pour sélectionner le meilleur chemin vers une position cible en fonction de critères de haut niveau plutôt que métriques, autorisant une navigation intelligente. / Utonomous navigation is one of the most challenging tasks for mobile robots. It requires the ability to localize itself or a target and to find the best path linking both positions avoiding obstacles. Towards this goal, robots build a map of the environment that models its geometry or topology. However building such a map in large scale environments is challenging due to the large amount of data to manage and localization could become intractable. Additionally, an ever changing environment leads to fast obsolescence of the map that becomes useless. As shown in this thesis, introducing semantics in those maps dramatically improves navigation performances of robots in realistic environments. Scene parsing allows to build extremely compact semantic models of the scene that are used for fast relocalization using a graph-matching approach. They are powerful tools to understand scene and they are used to extend the map beyond perceptual limits of the robot through reasoning. Statistical analysis of those models is used to build an embryo of common sens which allows to detect labeling errors and to update the map using algorithms designed to maintain a stable model of the world despite occlusions due to dynamic objects. Finally semantics is used to select the best route to a target position according to high level criteria instead of metrical constraints, allowing intelligent navigation.

<div>The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.</div>

Indiana University-Purdue University Indianapolis (IUPUI) / The current technologies employed for autonomous driving provide tremendous performance and results, but the technology itself is far from mature and relatively expensive. Some of the most commonly used components for autonomous driving include LiDAR, cameras, radar, and ultrasonic sensors. Sensors like such are usually high-priced and often require a tremendous amount of computational power in order to process the gathered data. Many car manufacturers consider cameras to be a low-cost alternative to some other costly sensors, but camera based sensors alone are prone to fatal perception errors. In many cases, adverse weather and night-time conditions hinder the performance of some vision based sensors. In order for a sensor to be a reliable source of data, the difference between actual data values and measured or perceived values should be as low as possible. Lowering the number of sensors used provides more economic freedom to invest in the reliability of the components used. This thesis provides an alternative approach to the current autonomous driving methodologies by utilizing acoustic signatures of moving objects. This approach makes use of a microphone array to collect and process acoustic signatures captured for simultaneous localization and mapping (SLAM). Rather than using numerous sensors to gather information about the surroundings that are beyond the reach of the user, this method investigates the benefits of considering the sound waves of different objects around the host vehicle for SLAM. The components used in this model are cost-efficient and generate data that is easy to process without requiring high processing power. The results prove that there are benefits in pursuing this approach in terms of cost efficiency and low computational power. The functionality of the model is demonstrated using MATLAB for data collection and testing.

Ett restaureringsarbete pågår i Vallentunasjön under ledning av Täby och Vallentuna kommuner. Syftet är att minska näringsämnena i Vallentunasjön, då sjön är övergödd. Tidigare har man provat olika metoder för att minska näringsämnena, men resultatet har varit dåligt. Den nya metoden går ut på att pumpa ut slammet från sjön kontinuerligt året om. Kemiska analyser har gjorts på sjövattnet och slammet före och efter isläggningen, för att se om metoden är hållbar.  Mätningar från Vattenkemikursen KH1400 åren 2013 till 2015 har använts som kompletterande mätningar i detta examensarbete.[10]    Sjövattnet och slammet testades med analyser för att kunna dra slutsatser gällande hur miljöfarligt det pumpade vattnet är. Torrsubstansen bestämdes i vattnet och slammet för att undersöka om det pumpade slammet kan avskiljas genom sedimentering. Fosforanalyser användes som måttstock för att uppskatta mängden pumpat slam. Genom analys av fosfor, vars koncentration kan mätas med hög precision, uppskattades slammängden med hjälp av Redfields formel. Två pumpar beskrivs i denna rapport och det är Fiskevårdsföreningens pump och EON’s pump där EON’s pump används för värmeproduktion. Denna metod fungerar då det pumpades ut ungefär 1.5 ton fosfor per år med EON’s pump, vilket är tillräckligt. Nästa frågeställning är att se om slammet kan koncentreras genom sedimentering. Det visade sig att slammet kan koncentreras, så att torrsubstansen ökade från 3 % till 30 %. Under ett år, transporteras ca 5 800 ton slam från Vallentunasjön genom EON’s pumpsystem. Det behövs ungefär 580 lastbilar för att transportera iväg slammet. Om slammet koncentreras genom sedimentation behövs 58 lastbilar. Huruvida slammet, som pumpas och transporteras, har en hög metallhalt är en fråga, som behöver analyseras och diskuteras i ett nytt projekt. Det krävs fler analyser för att se huruvida det pumpade sjövattnet innehållande slammet är miljöfarligt. Det skulle ta ca 3-5 år att pumpa bort slammet i sjön med EON´s pump. / A restoration work is being carried out in the Lake Vallentuna under the leadership of Täby and Vallentuna municipalities. The aim of this project is to reduce the nutrients in Lake Vallentuna, since the lake is eutrophicated. Previously, various methods have been tested to reduce the amount of nutrients, but the result has not been good enough. The new method is to pump out the sludge continuously throughout the year. Chemical analysis has been made on the water and the sludge from Lake Vallentuna, before and after the formation an ice cover on the lake, in order to investigate whether the method could be successful. Measurements in the water chemistry course KH1400, made in the years 2013, 2014 and 2015 have been used as a supplement in this thesis.[10] Lake water and the sludge have been analyzed. The dry matter of the lake bottom water and the sludge were determined to investigate whether the sludge can be separated by sedimentation or not. The concentration of phosphorous has been used as a yard stick to estimate the amount of pumped sludge.  The Redfield formula gives the relationship between phosphorous and total mass of sludge, assuming that the sludge consists of dead algea . Two pumps are described in this report. One is managed by Fiskevårdsföreningens (VFOF) and one is managed by EON. The latter pump is used for heat production. This method could be successful, because approximately 1.5 tons phosphorous per year of were transported, which is sufficient. The next issue was to investigate whether the sludge could be concentrated by sedimentation. It was found that the sludge could be concentrated by sedimentation and that the dry substance increased from 3 % to 30 %. It was found, that in one year, approximately 5 800 tons of sludge can be separated by pumping and sedimentation. This corresponds to 580 trucks (each loading 10 tons).  If the sludge is concentrated by sedimentation 58 trucks are required.  Whether the pumped and transported sludge has a high metal content is an issue, that needs to be investigated and considered in a special project, which requires further analyses. It would take 3-5 years to remove the excess phosphorus from Lake Vallentuna by continuous pumping.

<p><em>The first objective of this research has always been carry out a study of visual techniques SLAM (Simultaneous localization and mapping), specifically the type monovisual, less studied than the stereo. These techniques have been well studied in the world of robotics. These techniques are focused on reconstruct a map of the robot enviroment while maintaining its position information in that map. We chose to investigate a method to encode the points by the inverse of its depth, from the first time that the feature was observed. This method permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF).At first, the study mentioned it should be consolidated developing an application that implements this method. After suffering various difficulties, it was decided to make use of a platform developed by the same author of Slam method mentioned in MATLAB. Until then it had developed the tasks of calibration, feature extraction and matching. From that point, that application was adapted to the characteristics of our camera and our video to work. We recorded a video with our camera following a known trajectory to check the calculated path shown in the application. Corroborating works and studying the limitations and advantages of this method.</em></p>