Dagens intensiva användning av plast genererar mikroplaster som på olika sätt sprids ut i vår omgivning och medför risker för naturliga system världen över. Dessa mikroplaster härrör från bland annat hushåll, industrier och deponier, och färdas ofta med avloppsvattten och i viss utsträckning dagvatten till reningsverk. Där renas vattnet och en stor del av mikroplasterna ansamlas i det slam som bildas i samband med vattenreningen. Slammet består till stora delar av organiskt material och näring, och betraktas ofta som en resurssnarare än avfall. Cirka 50 % av det slam som bildas i europeiska reningsverk används som gödningsmedel inom jordbruk. Det innebär att slamgivor potentiellt är källor för spridning av stora mängder mikroplast. Idag är kunskapen om mikroplaster i slam relativt liten och en standardiserad metod för extrahering av mikroplaster ur slam saknas, vilket begränsar möjligheten att se problemets magnitud. Detta examensarbete strävar efter att bidra till utformandet av en sådan metod, och undersöker förekomsten av mikroplaster i slam från Ekeby reningsverk i Eskilstuna och Käppalaverket på Lidingö utanför Stockholm med hjälp av två olika metoder. De båda metoderna kombinerar oxidativ nedbrytning med hjälp av fenton-reagens och densitetseparering för att skilja mikroplaster från organiskt och inorganiskt material. Skillnaden mellan de olika metoderna är att den ena innehåller ett oxiderande moment och den andra två. Rapporten behandlar huruvida det extra oxiderande momentet har någon betydande skillnad för utvinningen av mikroplaster ur slam, samt om förekomsten av mikroplasterskiljer sig åt mellan slam från de två olika reningsverken. Resultaten visar att det finns en skillnad mellan de två extraheringsmetoderna samt viss skillnad mellan förekomsten av mikroplaster i de olika slammen. I rapporten diskuteras huruvida skillnaden mellan resultaten av de två olika metoderna beror på det extra oxiderande momentet, eller andra tillkommande effekter. Vad gäller mikroplasters förekomst i slam från de olika reningsverken fastslås att de skiljer sig åt kvantitativt, och i viss mån även kvalitativt.

Vid vattenrening produceras slam som restprodukt. Slam innehåller en stor mängd ämnen, såsom metaller, medicinrester och olika näringsämnen. I Sverige produceras flera hundra tusen ton årligen. Innehållet måste hanteras, men hur görs det idag och hur kommer det att hanteras i framtiden. Vanligast idag är att återvinna fosfor och kväve genom att sprida slam som gödning inom jord och skogsbruk. Däremot metaller som finns i slam utvinns inte idag, även om det vore värdefullt ur miljösynpunkt.  Denna litteraturstudie beskriver hur slamhantering ser ut idag, med fokus på Sverige. Därefter beskrivs forskningsläge och möjlig framtida utveckling i hantering. Slamhantering är problematisk ur flera aspekter. Slamhantering behöver hantera följande svårigheter: En logistisk aspekt är de stora volymer som behöver hanteras och att de produceras på många olika platser.Forskning på konsekvenser av slamspridning är relativt liten. Det gäller den påverkan som medicinrester, tungmetaller och andra ämnen utgör.Vilka utvinningsbara ämnen ska prioriteras, eftersom metoder för utvinning ställer maximerad utvinning av gödning i motsats till maximerad utvinning av metaller. / When purifying water, sludge is produced as a residual product. Sludge contains lots of different substances, such as metals, medical waste and different nutrients. In Sweden there is many hundred thousands of tons of sludge produced annually. The content must be managed, but how is it handled today and how will it be managed in the future. Commonly today phosphorus and nitrogen recycles by spreading sludge as fertiliser in agriculture and forestry. Metals in sludge is not recycled, even if it would be positive from an environmental point of view. This literature study describes how sludge treatment is today with a focus in Sweden. Then it describes the current state of research and possible future development in management. Sludge handling is problematic from many different aspects. Sludge treatment must have to address these challanges: One logistic aspect is that there are great volumes that needs to be handled and that it is produced in many different places. The research in consequences of sludge spreading is a relatively small base. That goes for the influence of medical waste, heavy metals, and other substances in sludge. What recyclable substances needs to be prioritised, since the methods of extraction puts the maximal extraction of fertiliser against the maximised extractions of metals

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Accurate and efficient localization is a fundamental requirement for autonomous operation of robots, especially in areas that deny global navigation services. Localization is even more challenging in environments that present visual and geographic difficulties. This not only includes environmental aspects like darkness, fog and dust but also geometrically monotone areas. The solution that the Center for Applied Autonomous Sensor Systems at the Örebro university decided to develop is therefore a prototype of radar-only localization and mapping (SLAM) system. The radar modality is less susceptible to the environmental factors when compared to, for example, a lidar. Our goal is to support this effort by creating an odometry module that uses radar and inertial data to provide the localization for this SLAM prototype. This radar-inertial-odometry (RIO) takes radar point clouds and inertial gyroscopic data to output an odometry message usable by other components in the robot operating system (ROS). The module has been tested on two datasets representing areas typical for deployment, one consisting of underground tunnels and the other one being an outside forest environment. The dataset has been processed by two different mappers where the lidar has been used as the basic modality. This choice allows us to evaluate the odometry module in a more practical way. The final results are promising, the underground localization closely adheres to reality. The forest dataset is more challenging although it still resembles the ground-truth position in the horizontal dimension. The module's biggest shortcoming is a noticeable drift problem in the vertical z-dimension , for which we propose a constraint that limits this drift.

The need for accurate 3D reconstructions of complex and large environments or structures has risen dramatically in recent years. In this context, devices known as portable mobile mapping systems have lately emerged as fast and accurate reconstruction solutions. While most of the research and commercial works have relied so far on laser scanners, solutions solely based on cameras and photogrammetry are attracting an increasing interest for the minor costs, size and power consumption of cameras. This thesis presents a novel handheld mobile mapping system based on stereo vision and image-based 3D reconstruction techniques. The main novelty of the system is that it leverages Visual Simultaneous Localization And Mapping (V-SLAM) technology to support and control the acquisition of the images. The real-time estimates of the system trajectory and 3D structure of the scene are used not only to enable a live feedback of the mapped area, but also to optimize the saving of the images, provide geometric and radiometric quality measures of the imagery, and robustly control the acquisition parameters of the cameras. To the best of authors’ knowledge, the proposed system is the first handheld mobile mapping system to offer these features during the acquisition of the images, and the results support its advantages in enabling accurate and controlled visual mapping experiences even in complex and challenging scenarios.

Background: In recent years, computer vision has undergone significant advancements, revolutionizing fields such as robotics, augmented reality, and autonomoussystems. Key to this transformation is Simultaneous Localization and Mapping(SLAM), a fundamental technology that allows machines to navigate and interactintelligently with their surroundings. Challenges persist in harmonizing spatial andsemantic understanding, as conventional methods often treat these tasks separately,limiting comprehensive evaluations with shared datasets. As applications continueto evolve, the demand for accurate and efficient image segmentation ground truthbecomes paramount. Manual annotation, a traditional approach, proves to be bothcostly and resource-intensive, hindering the scalability of computer vision systems.This thesis addresses the urgent need for a cost-effective and scalable solution byfocusing on the creation of accurate and efficient image segmentation ground truth,bridging the gap between spatial and semantic tasks. Objective: This thesis addresses the challenge of creating an efficient image segmentation ground truth to complement datasets with spatial ground truth. Theprimary objective is to reduce the time and effort taken for annotation of datasets. Method: Our methodology adopts a systematic approach to evaluate and combineexisting annotation techniques, focusing on precise object detection and robust segmentation. By merging these approaches, we aim to enhance annotation accuracywhile streamlining the annotation process. This approach is systematically appliedand evaluated across multiple datasets, including the NYU V2 dataset(consists ofover 1449 images), ARID(real-world sequential dataset), and Italian flats(sequentialdataset created in blender). Results: The developed pipeline demonstrates promising outcomes, showcasing asubstantial reduction in annotation time compared to manual annotation, thereby addressing the challenges posed by the cost and resource intensiveness of the traditionalapproach. We observe that although not initially optimized for SLAM datasets, thepipeline performs exceptionally well on both ARID and Italian flats datasets, highlighting its adaptability to real-world scenarios. Conclusion: In conclusion, this research introduces an innovative annotation pipeline,offering a systematic and efficient approach to annotation. It tries to bridge the gapbetween spatial and semantic tasks, addressing the pressing need for comprehensiveannotation tools in this domain.

The most commonly used localisation methods, such as GPS, rely on external signals to generate an estimate of the location. There is a need of systems which are independent of external signals in order to increase the robustness of the localisation capabilities. In this thesis a visual-inertial SLAM-based localisation system which utilises detailed map, image, IMU, and odometry data, is presented and evaluated. The system utilises factor graphs through Georgia Tech Smoothing and Mapping (GTSAM) library, developed at the Georgia Institute of Technology. The thesis contributes with performance evaluations for different camera and landmark settings in a localisation system based on GTSAM. Within the visual SLAM field, the thesis also contributes with a sparse landmark selection and a low image frequency approach to the localisation problem. A variety of camera-related settings, such as image frequency and amount of visible landmarks per image, are used to evaluate the system. The findings show that the estimate improve with a higher image frequency, and does also improve if the image frequency was held constant along the tracks. Having more than one landmark per image result in a significantly better estimate. The estimate is not accurate when only using one distant landmark throughout the track, but it is significantly better if two complementary landmarks are identified briefly along the tracks. The estimate can also handle time periods where no landmarks can be identified while maintaining a good estimate.

One of the main goals of augmented reality is placing virtual content in the real world at a precise location. To achieve this goal, the Head Mounted Display (HMD) should be able to place virtual content at a precise location, and the users should be able to perceive at the exact location. However, achieving this task is very challenging. Since the birth of augmented reality, researchers have been trying to design AR glasses which can do this. Recently AR researchers by taking advantage of SLAM algorithms are able to come closer to the first phase of this goal. Microsoft designed and manufactured a pair of smart glasses called the HoloLens. It is well known for its advanced SLAM algorithm to place the content in a precise location as close as possible. However, there is no significant research on the perceptual location of the virtual content which are placed through Hololens. Therefore this thesis presents a method for measuring the perceived location of virutal objects, and presents an experiment, where these measurements are made with the Hololens. Through this experiment, interesting information about HoloLens was found, such as the capability of regaining tracking immediately after occlusion, rightward error about the horizontal plane, and bias of floating the virtual content above the surface, and objects that appear to close to the observer. Therfore Hololens is an advanced AR display, it still suffers from these problems.

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La motivación de esta tesis consistió en investigar el empleo de sistemas visuales monoculares en ambientes subacuáticos e implementar soluciones novedosas a los problemas complejos que en tales ambientes se presentan. Algunos de los principales aportes de este trabajo son: el desarrollo de algoritmos para la navegación en ambientes altamente auto-símiles, la implementación de un sistema monocular SLAM (Simultaneous localization and Mapping) denso orientado a su uso en ambientes subacuáticos, el desarrollo de una metodología robusta para la exploración retorno y revisita basada en SLAM, la implementación de un filtro de onda cáustica para ambientes tridimensionales. La validez de los resultados obtenidos teóricamente se verificó experimentalmente en ambientes subacuáticos controlados. / The motivation of this thesis consisted in researching the use of visual-monocular systems in underwater environments and to implement novel solutions to the complex problems that arise in such scenes. Some of the main contributions of this work are: the development of algorithms for navigation in highly self-similar scenes, the implementation of a dense monocular SLAM system (Simultaneous localization and Mapping) developed to be used in underwater scenes, the development of a robust methodology for exploration, return and revisiting based on SLAM, the implementation of a caustic wave filter for tridimentional environments. The validity of the obtained results were verified experimentally in controlled underwater environments.