This thesis report presents a study on the virtualization of an Electric Cabin Scooter used to validate the feasibility of converting it into an autonomous vehicle. The project aimed to design, develop, and test a virtual model of the car that can navigate from points A to B while avoiding obstacles. The report describes the methodology used in the project, which includes setting up the workspace, construction of the virtual model, implementation of ROS2 controllers, and integration of SLAM and Navigation2. The thesis report also describes and discusses related work, as well as the theoretical background of the project. Results show a successfully developed working virtual vehicle model, which provides a solid starting point for future work. / Detta examensarbete presenterar en studie om virtualiseringen av en elektrisk kabinscooter. Den virtuella modellen används för att validera genomförbarheten av att omvandla den till ett autonomt fordon. Projektet syftade till att designa, utveckla och testa en virtuell modell av bilen som kan navigera från punkt A till B medan den undviker hinder. Rapporten beskriver metodiken som används i projektet, vilket inkluderar att sätta upp arbetsytan, konstruktion av den virtuella modellen, implementering av ROS2-kontroller och integration av SLAM och Navigation2. Rapporten diskuterar även relaterat arbete, samt teoretisk bakgrund till arbetet. Resultaten visar en framgångsrikt utvecklad fungerande virtuell fordonsmodell, som ger en solid utgångspunkt för framtida arbete.

As the capabilities of autonomous systems have increased so has interest in utilizing teams of autonomous systems to accomplish tasks more efficiently. This dissertation takes steps toward enabling the cooperation of unmanned systems in scenarios that are challenging, such as GPS-denied or perceptually aliased environments. This work begins by developing a cooperative navigation framework that is scalable in the number of agents, robust against communication latency or dropout, and requires little a priori information. Additionally, this framework is designed to be easily adopted by existing single-agent systems with minimal changes to existing software and software architectures. All systems in the framework are validated through Monte Carlo simulations. The second part of this dissertation focuses on making cooperative navigation robust in challenging environments. This work first focuses on enabling a more robust version of pose graph SLAM, called cycle-based pose graph optimization, to be run in real-time by implementing and validating an algorithm to incrementally approximate a minimum cycle basis. A new algorithm is proposed that is tailored to multi-agent systems by approximating the cycle basis of two graphs that have been joined. These algorithms are validated through extensive simulation and hardware experiments. The last part of this dissertation focuses on scenarios where perceptual aliasing and incorrect or unknown data association are present. This work presents a unification of the framework of consistency maximization, and extends the concept of pairwise consistency to group consistency. This work shows that by using group consistency, low-degree-of-freedom measurements can be rejected in high-outlier regimes if the measurements do not fit the distribution of other measurements. The efficacy of this method is verified extensively using both simulation and hardware experiments.

No description available.

Most of the navigation systems used in today’s aircraft rely on Global Navigation Satellite Systems (GNSS). However, GNSS is not fully reliable. For example, it can be jammed by attacks on the space or ground segments of the system or denied at inaccessible areas. Hence to ensure successful navigation it is of great importance to continuously be able to establish the aircraft’s location without having to rely on external reference systems. Localization is one of many sub-problems in navigation and will be the focus of this thesis. This brings us to the field of visual odometry (VO), which involves determining position and orientation with the help of images from one or more camera sensors. But to date, most VO systems have primarily been established on ground vehicles and low flying multi-rotor systems. This thesis seeks to extend VO to new applications by exploring it in a fairly new context; a fixed-wing piloted combat aircraft, for vision-only pose estimation in applications of extremely large scene depth. A major part of this research work is the data gathering, where the data is collected using the flight simulator X-Plane 11. Three different flight routes are flown; a straight line, a curve and a loop, for two types of visual conditions; in clear weather with daylight and during sunset. The method used in this work is ORB-SLAM3, an open-source library for visual simultaneous localization and mapping (SLAM). It has shown excellent results in previous works and has become a benchmark method often used in the field of visual pose estimation. ORB-SLAM3 tracks the straight line of 78 km very well at an altitude over 2700 m. The absolute trajectory error (ATE) is 0.072% of the total distance traveled in daylight and 0.11% during sunset. These results are of the same magnitude as ORB-SLAM3 on the EuRoC MAV dataset. For the curved trajectory of 79 km ATE is 2.0% and 1.2% of total distance traveled in daylight and sunset respectively.  The longest flight route of 258 km shows the challenges of visual pose estimation. Although it is managing to close loops in daylight, it has an ATE of 3.6% during daylight. During sunset the features do not possess enough invariant characteristics to close loops, resulting in an even larger ATE of 14% of total distance traveled. Hence to be able to use and properly rely on vision in localization, more sensor information is needed. But since all aircraft already possess an inertial measurement unit (IMU), the future work naturally includes IMU data in the system. Nevertheless, the results from this research show that vision is useful, even at the high altitudes and speeds used by a combat aircraft.

Aktivt slam är en biologisk process som används i stor utsträckning för rening av avloppsvatten, där bakterier som odlas under luftiga förhållanden avlägsnar organiska ämnen och näringsämnen från förorenat vatten. För industriell verksamhet modelleras denna process för att representera och förutsäga biologiska fenomen. De modeller som används för närvarande (främst modellen för aktiverat slam) ger acceptabla resultat men har vissa begränsningar, t.ex. lång kalibreringstid och beroende av många modellparametrar som är svåra att förutsäga. Under detta projekt som genomfördes på företaget SUEZ bedömdes det om sådana modeller kunde förbättras genom att lägga till ett termokemiskt perspektiv i deras arkitektur, antingen genom att minska antalet parametrar eller genom att teoretiskt förutsäga deras värden. Det termokemiska bidraget bedömdes på två olika nivåer: de stökiometriska förhållanden som beskriver systemet och de biologiska processernas kinetik. På grundval av en omfattande litteraturstudie valdes två metoder ut för att föra in termodynamiska överväganden i dessa delar av modellen. Termodynamiska beräkningar som krävs för att förbättra specifikationen av de förorenande molekyler som finns i vattenmatrisen. Arkitekturen i den ursprungliga modellen för aktiverat slam ändrades för att möjliggöra en differentiering mellan substrat av olika slag. När det gäller den stökiometriska studien visade det sig att den dynamiska förutsättningen av termodynamiska begränsningar för reaktionerna inte var av något större intresse med tanke på de stora mängder energi som finns tillgängliga. Användningen av olika organiska substrat gav dock intressanta resultat, eftersom det gjorde det möjligt att modellera de biologiska beteendena på ett mer detaljerat sätt och att förutsäga tillväxten av specifika typer av mikroorganismer. Den kinetiska studien gav måttliga resultat, med adekvata simuleringar men utan någon ny större förbättring av modellen. Den mest lovande utvecklingen av denna studie tycks vara en modell som omfattar en mängd olika organiska substrat med motsvarande biomassapopulationer, för att kunna rikta in sig på den specifika förekomsten av intressanta bakterier. / Activated sludge is a biological process that is widely used for wastewater treatment, where bacteria grown in aerated conditions remove organic substances and nutrients from polluted waters. For industrial operations, this process is modeled to represent and predict biological phenomena. Currently used models (mainly, the Activated Sludge Model) give acceptable results but suffer from some limitations such as long calibration time and dependency on an many model parameters that are difficult to predict. During this project conducted at the company SUEZ, it was assessed whether such models could be improved by adding a thermochemical perspective to their architecture, either by reducing the number of parameters involved or by predicting theoretically their values. The contribution of thermochemistry was assessed on two different levels: the stoichiometric relations that describe the system, and the kinetics of the biological processes. Based on an extensive literature study, two methods were selected to inject thermodynamical considerations to these parts of the model. Thermodynamic calculations required to improve the specification of the pollutant molecules present in the water matrix. The architecture of the initial Activated Sludge Model was modified to allow for a differentiation between substrates of different nature. Regarding the stoichiometric study, it appeared that the dynamic prevision of thermodynamical constraints over the reactions did not show a major interest considering the high amounts of energy available. However, the use of different organic substrates gave interesting results because it allowed to model more finely the biological behaviors and to predict the growth of specific types of microorganisms. The kinetic study gave mitigated results, with adequate simulations but no new major improvement to the model. The most promising development of this study appears to be a model involving a multiplicity of organic substrates, with corresponding biomass populations, to target the specific occurrence of bacteria of interest.

Ce mémoire s’intéresse aux apports possibles de la biomécanique meyerholdienne à l’expression théâtrale et plastique du slam de poésie, l’objectif initial de la recherche consistant à bonifier et à développer l’expression tant gestuelle que vocale du poète performant son texte à slamer. Il s’agissait donc, à travers le processus de recherche-création proposé, de conduire une série de laboratoires et d’expérimentations théâtrales afin de développer Panpan!, un spectacle expérimental hybridant à la fois biomécanique et slam de poésie. Des multiples expériences nécessaires à la réalisation de ce court spectacle, j’ai dégagé une série d’outils théoriques et pratiques, que j’ai ensuite exposés en profondeur dans cet essai. En annexe, le lecteur trouvera également l’ensemble de mes outils de travail, développés au fil de ma pratique expérimentale et ayant permis tant la réalisation de Panpan! que la découverte et l’étude des principes théoriques et pratiques dont fait état ma recherche. Mots clés: poésie slamée, slam de poésie, biomécanique, Vsevolod E. Meyerhold, slam-théâtre, expression corporelle, expression vocale, partition dramaturgique, mécanique dramaturgique, musicalité, virtuosité, rythme, constructivisme russe, grotesque, cabotinage. / This master thesis studies the contribution of Meyerhold’s biomechanics to the theatrical and plastic expression of the slam poetry, the initial aim of the research consisting of improving and developing the poet’s gestural and vocal expression during his slam performance. Through the process of this research-creation, I had to conduct a series of theatrical laboratories and experimentations in order to produce Panpan!, an experimental show crossing the slam poetry with Meyerhold’s biomechanics. Through the multiple experiments necessary to the realization of this short show, I discovered a series of theoretical and practical tools, which I then exposed in this essay. In the appendix, the reader will also find all of my working tools, developed during my experimental practice. These tools allowed the making of Panpan!, as well as the discovery and the studying of the theoretical and practical principles put forward by my research. Keywords: slam poetry, poetry slam, corporal expression, vocal expression, biomechanics, Vsevolod E. Meyerhold, dramaturgic score, dramaturgic mechanics, musicality, virtuosity, rhythm, Russian constructivism, grotesque, cabotinage (a French word for "ham acting", used here with a positive connotation).

SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers. In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem\'s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.

Die Sensordatenfusion ist eine allgegenwärtige Aufgabe im Bereich der mobilen Robotik und darüber hinaus. In der vorliegenden Arbeit wird das typischerweise verwendete Verfahren zur Sensordatenfusion in der Robotik in Frage gestellt und anhand von neuartigen Algorithmen, basierend auf einem Faktorgraphen, gelöst sowie mit einer korrespondierenden Extended-Kalman-Filter-Implementierung verglichen. Im Mittelpunkt steht dabei das technische sowie algorithmische Sensorkonzept für die Navigation eines Flugroboters im Innenbereich. Ausführliche Experimente zeigen die Qualitätssteigerung unter Verwendung der neuen Variante der Sensordatenfusion, aber auch Einschränkungen und Beispiele mit nahezu identischen Ergebnissen beider Varianten der Sensordatenfusion. Neben Experimenten anhand einer hardwarenahen Simulation wird die Funktionsweise auch anhand von realen Hardwaredaten evaluiert.

The Master thesis is focused on analyzing stereo camera ZED in the outdoor environment. There is compared ZEDfu visual odometry with commonly used methods like GPS or wheel odometry. Moreover, the thesis includes analyses of SLAM in the changeable outdoor environment, too. The simultaneous mapping and localization in RTAB-Map were processed separately with SIFT and BRISK descriptors. The aim of this master thesis is to analyze the behaviour ZED camera in the outdoor environment for future implementation in mobile robotics.

SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers. In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem\'s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.