With increased mobile hardware capabilities, improved UAVs and modern algorithms, accurate maps can be created in real-time by capturing overlapping photographs of the ground. A method for mapping that can be used is to position photos by relying purely on the GPS position and altitude. However, GPS inaccuracies will be visible in the created map. In this paper, we will instead present a method for aligning the photos correctly with the help of feature matching. Feature matching is a well-known method which analyses two photos to find similar parts. If an overlap exists, feature matching can be used to find and localise those parts, which can be used for positioning one image over the other at the overlap. When repeating the process, a whole map can be created. For this purpose, we have also evaluated a selection of feature detection and matching algorithms. The algorithm found to be the best was SIFT with FLANN, which was then used in a prototype for creating a complete map of a forest. Feature matching is in many cases superior to GPS positioning, although it cannot be fully depended on as failed or incorrect matching is a common occurrence.

This project explores the design and implementation of synchronized Pan-Tilt-Zoom (PTZ) camera control for panoramic imaging, with a specific focus on enhancing surveillance systems in air traffic management. The motivation for this study comes from the need for better monitoring in air traffic control, where panoramic views can greatly improve situational awareness and safety. The main challenge is coordinating multiple PTZ cameras to capture and stitch images, creating a comprehensive panoramic view despite individual camera limitations. The study uses simulation to test synchronization and image stitching techniques, ensuring camera alignment and seamless panoramic images. Results indicate the system's robustness and potential for real-world applications, though future validation with physical hardware is necessary.

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This is a study exploring how robust one feature descriptors, scale invariant feature transform (SIFT), and one feature detector, feature accelerated segmentation test (FAST), are in terms of handling fetoscopic acquired data when mosaicing. Today’s treatment of severe Twin-to-Twin Transfusion Syndrome at Karolinska University Hospital is fetoscopic guided laser occlusion of chorioangiopagous vessels (FLOC) where intersecting blood vessels causing a transfusion (Anastomoses) in between the fetuses are occluded. These blood vessels are located somewhere on the placenta. The fetoscopy includes navigation of a relatively large area where the field of view (FOV) is limited. The limited FOV during the fetoscopy makes it cumbersome to navigate and identify intersected blood vessels. The motivation of this study is to explore ways of dealing with the complications during FLOC by mosaicing an overview of the placenta that can be used as an assisting map to make the procedure safer by improving navigation of the fetoscope and identification of blood vessels during FLOC. In this study, the steps of mosaicing are defined based on mosaicing frameworks to explore how these methods perform in terms of being able to mosaic a map of the placenta. The methods have been tested on non-fetoscopic acquired data as well as fetoscopic acquired data to create a relative measure in between the two. Three tests on non- fetoscopic data were performed to explore how well the methods handled mosaicing of data with distinctive characteristics. The same methods were then tested on unprocessed fetoscopic data before being tested on preprocessed fetoscopic data to see if the results were affected by external preprocessing. The results showed that there were differences in between the methods. SIFT and FAST showed that they have potential of mosaicing non-fetoscopic data of varying extent. SIFT gave an impression of being more robust during all of the tests. SIFT especially performed better during the tests on data with few potential keypoints which is an advantage when speaking of fetoscopic acquired data. SIFT also managed to mosaic a larger area than FAST when mosaicing preprocessed fetoscopic data. Preprocessing the data improved the mosaicing when using SIFT but further improvements are needed. / Denna studie utforskar hur robust en intressepunktsbeskrivare, scale invariant feature transform (SIFT), och enintressepunktsdetektor, feature accelerated segmentation test (FAST), hanterar digitala bilder insamlade av ett fetoskop med syfte att sy ihop dessa till en översiktskarta. Dagens behandling av tvillingtransfusionsyndrom vid Karolinska Universitetssjukhuset är fetoscopic guided laser occlusion of chorioangiopagous vessels (FLOC). Under denna fetoskopi bränner man och därmed blockerar korsande blodkärl som orsakar en transfusion (anastomoses) och obalans i blodomloppet mellan två tvillingfoster. Dessa blodkärl är lokaliserade på placentan. Fetoskopin omfattar navigering av en relativt stor area med ett begränsat synfält. Det begränsade synfältet under FLOC gör det svårt att orientera fetoskopet och identifiera korsande blodkärl som orsakar transfusionen. Syftet med studien är att utforska ett sätt att hantera komplikationerna med FLOC igenom att utforska sätt att skapa en översiktskarta av placentan under FLOC. Denna översiktskarta kan nyttjas under FLOC och därmed göra proceduren säkrare igenom att kartan underlättar orienteringen av fetoskopet och identifiering av orsakande blodkärl. I denna studie är stegen för att skapa en översiktskarta baserade på olika datorseende ramverk för att se hur dessa tillvägagångssätt presterar när det gäller att skapa en översiktskarta. Metoderna för att skapa en översiktskarta har testats på data insamlad med webkamera och data insamlad med fetoskop för att skapa en relativ uppfattning om hur de står sig beroende på indata. Tre tester på data insamlad med webkamera genomfördes för att utforska hur väl metoderna hanterade data med många potentiella intressepunkter, rörelse orsakad av handhållen enhet/kamera, repetitiva mönster, översiktskartor som resulterande i större upplösning, och liten möjlighet att hitta intressepunkter. Samma metoder testades sedan på icke behandlad data insamlad med fetoskop innan den testades på förbehandlad data insamlad med fetoskop för att se förbehandlingensnödvändighet och prestation. Resultaten visar att det är skillnader mellan de två metoderna använda i denna studie, både när det gäller data insamlad med fetoskop och webkamera. SIFT och FAST visar potential av olika grad när det gäller att skapa en översiktskarta med data insamlad av webkamera. SIFT visade sig vara mer robust under alla tester inklusive data insamlad med fetoskop. SIFT presterade speciellt bättre under testen som omfattade få antal möjliga intressepunkter vilket är en fördel när det gäller data insamlad med fetoskop. SIFT lyckades också skapa översiktskartor med större area än FAST när förbehandlad fetoskopisk data testades. När det gäller SIFT så visade resultaten en förbättring när data insamlad med fetoskop förbehandlades men att ytterligare förbättringar är nödvändiga.

Semi-autonomous uncrewed aerial vehicles (UAVs) are telerobotic operations by definition where the UAV assumes the role of a telerobot and the human assumes the role of a supervisor. All telerobotic operations are susceptible to time delays due to communication, mechanical, and other constraints. Typically, these delays are small and do not affect the telerobotic operation for most of the tasks. However, for long-distance telerobotic operations like interplanetary rovers, deep underwater vehicles, etc. the delays can be so significant that they can render the entire operation void. This dissertation investigates the use of a novel heterogeneous stereo-vision system to mitigate the effects of time delays in a UAV-based visual interface presented to a human operator. The heterogeneous stereo-vision system consists of an omnidirectional camera and a pan-tilt-zoom camera. Two predictive display setups were developed that modify the delayed video imagery that would otherwise be presented to the operator in a way that provides an almost immediate visual response to the operator's control actions. The usability of the system is determined through human performance testing with and without the predictive algorithms. The results indicate that the predictive algorithm allows more efficient, accurate, and user-friendly operation. The second half of the dissertation deals with improving the performance of the predictive display and expanding the concept of the prediction from a stationary gimbal-camera system to a moving 6 DoF aircraft. Specifically, it talks about a novel extended Kalman filter (EKF)-based nonlinear predictor – the extended Kalman predictor (EKP) – and compares its performance with two linear predictors, the Smith predictor (SP) and the Kalman predictor (KP). This dissertation provides the mathematical formulation of the EKP, as well as the two linear predictors, and describes their use with simulated flight data obtained using a nonlinear motion model for a small, fixed-wing UAV. The EKP performs comparably to the KP when the aircraft motion experiences small perturbations from a nominal trajectory, but the EKP outperforms the KP for larger excursions. The SP performs poorly in every case. / Doctor of Philosophy / Semi-autonomous uncrewed aerial vehicles (UAVs) are telerobotic operations by definition where the aerial vehicle assumes the role of a telerobot and the human assumes the role of a supervisor. This dissertation addresses the challenges posed by time delays in uncrewed aerial vehicle operations, particularly for long-distance operations such as interplanetary exploration and deep-sea missions. It investigates the use of a novel heterogeneous stereo-vision system to mitigate these delays, providing operators with nearly real-time visual feedback. Human performance testing confirms the predictive algorithm allows more efficient, accurate, and user-friendly operation. Additionally, the dissertation presents advancements in the predictive display performance for moving UAVs with six degrees of freedom. It introduces a novel extended Kalman predictor and compares it to traditional linear predictors like the Smith predictor and the Kalman predictor using simulated flight data. The extended Kalman predictor demonstrates superior performance for larger deviations from trajectory, highlighting its effectiveness in predicting the motion of an aircraft when there are time delays present.

This master's thesis deals with the interest point detection in images. The main goal is to create an application for making panoramic photos, which is based on this detection. The application uses the SIFT detector for finding keypoints of the image. Afterwards the geometrical consistence of image points using the RANSAC algorithm is finds out and images are transform into panorama in accordance with this geometrical consistence. Finally techniques for blending of transition between photos are used.

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This thesis analyzes the problem of acquiring stereoscopic images in all gazing directions around a reference viewpoint in space with the purpose of creating stereoscopic panoramas of non-static scenes. The generation of immersive stereoscopic imagery suitable to stimulate human stereopsis requires images from two distinct viewpoints with horizontal parallax in all gazing directions, or to be able to simulate this situation in the generated imagery. The available techniques to produce omnistereoscopic imagery for human viewing are not suitable to capture dynamic scenes stereoscopically. This is a not trivial problem when considering acquiring the entire scene at once while avoiding self-occlusion between multiple cameras. In this thesis, the term omnidirectional refers to all possible gazing directions in azimuth and a limited set of directions in elevation. The acquisition of dynamic scenes restricts the problem to those techniques suitable for collecting in one simultaneous exposure all the necessary visual information to recreate stereoscopic imagery in arbitrary gazing directions. The analysis of the problem starts by defining an omnistereoscopic viewing model for the physical magnitude to be measured by a panoramic image sensor intended to produce stereoscopic imagery for human viewing. Based on this model, a novel acquisition model is proposed, which is suitable to describe the omnistereoscopic techniques based on horizontal stereo. From this acquisition model, an acquisition method based on multiple cameras combined with the rendering by mosaicking of partially overlapped stereoscopic images is identified as a good candidate to produce omnistereoscopic imagery of dynamic scenes. Experimental acquisition and rendering tests were performed for different multiple-camera configurations. Furthermore, a mosaicking criterion between partially overlapped stereoscopic images based on the continuity of the perceived depth and the prediction of the location and magnitude of unwanted vertical disparities in the final stereoscopic panorama are two main contributions of this thesis. In addition, two novel omnistereoscopic acquisition and rendering techniques were introduced. The main contributions to this field are to propose a general model for the acquisition of omnistereoscopic imagery, to devise novel methods to produce omnistereoscopic imagery, and more importantly, to contribute to the awareness of the problem of acquiring dynamic scenes within the scope of omnistereoscopic research.