Global ETD Search

271	In pursuit of consumer-accessible augmented virtuality / En strävan efter konsumenttillgänglig augmented virtuality Berggrén, Rasmus January 2017 (has links) This project is an examination of the possibility of using existing software to develop Virtual Reality (VR) software that includes key aspects of objects in a user’s surroundings into a virtual environment, producing Augmented Virtuality (AV). A defining limitation is the requirement that the software be consumer-accessible, meaning it needs run on a common smartphone with no additional equipment. Two related AV concepts were considered: shape reconstruction and positional tracking. Two categories of techniques were considered for taking the measurements of reality necessary to achieve those AV concepts using only a monocular RGB camera as sensor: monocular visual SLAM (mvSLAM) and Structure from Motion (SfM). Two lists of requirements were constructed, formalising the notions of AV and consumer-accessibility. A search process was then conducted, where existing software packages were evaluated for their suitability to be included in a piece of software fulfilling all requirements. The evaluations of SfM systems were made in combination with Multi-View Stereo (MVS) systems – a necessary complement for achieving visible shape reconstruction using a system that outputs point clouds. After thoroughly evaluating a variety of software, it was concluded that consumer-accessible AV can not currently be achieved by combining existing packages, due to several issues. While future hardware performance increases and new software implementations would solve complexity and availability issues, some inaccuracy and usability issues are inherent to the limitation of using a monocular camera. / Detta projekt är en undersökning av möjligheten att använda befintlig programvara till att utveckla Virtual Reality (VR)-programvara som infogar framstående aspekter av objekt från en användares omgivning in i en virtuell miljö och därmed skapar Augmented Virtuality (AV). En definierande begränsning är kravet på att programvaran skall vara konsumenttillgänglig, vilket innebär att den behöver kunna köras på en vanlig smartphone utan extra utrustning. Två besläktade AV-koncept beaktades: formrekonstruktion och positionsspårning. Två kategorier av tekniker togs i beaktande, vilka kunde användas för att göra de uppmätningar av verkligheten som var nödvändiga för att uppnå de tänkta AV-koncepten med hjälp av endast en monokulär RGB-kamera som sensor: monocular visual SLAM (mvSLAM) och Structure from Motion (SfM). Två listor med kriterier konstruerades, vilka formaliserade begreppen AV och konsumenttillgänglighet. En sökprocess utfördes sedan, där befintliga programvarupaket utvärderades för sin lämplighet att inkluderas i en programvara som uppfyllde alla kriterier. Utvärderingarna av SfM-system gjordes i kombination med Multi-View Stereo (MVS)-system – ett nödvändigt komplement för att uppnå synlig formrekonstruktion med ett system vars utdata är punktmoln. Efter att noggrant ha utvärderat en mängd programvara var slutsatsen att konsumenttillgänglig AV inte för närvarande kan uppnås genom att kombinera befintliga programvarupaket, på grund av ett antal olika problem. Medan framtida prestandaökningar hos maskinvara och nya programvarutillämpningar skulle lösa problem med komplexitet och tillgänglighet, är vissa problem med tillförlitlighet och användbarhet inneboende hos begränsningen till att använda en monokulär kamera. virtual reality VR mixed reality MR augmented virtuality AV SLAM SfM interaction design consumer-accessible consumer-accessibility smartphone 3D reconstruction shape reconstruction positional tracking computer vision point cloud Multi-View Stereo MVS Unity Google Cardboard feature detection Media and Communication Technology Medieteknik
272	Clustering on groups for human tracking with 3D LiDAR Utterström, Simon January 2023 (has links) 3D LiDAR people detection and tracking applications rely on extracting individual people from the point cloud for reliable tracking. A recurring problem for these applications is under-segmentation caused by people standing close or interacting with each other, which in turn causes the system to lose tracking. To address this challenge, we propose Kernel Density Estimation Clustering with Grid (KDEG) based on Kernel Density Estimation Clustering. KDEG leverages a grid to save density estimates computed in parallel, finding cluster centers by selecting local density maxima in the grid. KDEG reaches a remarkable accuracy of 98.4%, compared to HDBSCAN and Scan Line Run (SLR) with 80.1% and 62.0% accuracy respectively. Furthermore, KDEG is measured to be highly efficient, with a running time similar to state-of-the-art methods SLR and Curved Voxel Clustering. To show the potential of KDEG, an experiment with a real tracking application on two people walking shoulder to shoulder was performed. This experiment saw a significant increase in the number of accurately tracked frames from 5% to 78% by utilizing KDEG, displaying great potential for real-world applications. In parallel, we also explored HDBSCAN as an alternative to DBSCAN. We propose a number of modifications to HDBSCAN, including the projection of points to the groundplane, for improved clustering on human groups. HDBSCAN with the proposed modifications demonstrates a commendable accuracy of 80.1%, surpassing DBSCAN while maintaining a similar running time. Running time is however found to be lacking for both HDBSCAN and DBSCAN compared to more efficient methods like KDEG and SLR. / <p>Arbetet är gjort på plats i Tokyo på Chuo Universitet utan samverkan från Umeå Universitet såsom utbytesprogram eller liknande.</p><p>Arbetet är delvis finansierat av Scandinavia-Japan Sasakawa Foundation.</p><p>Arbetet gick inte under vanlig termin, utan började 2023/05/01 och slutade 2023/08</p> Computer Vision Computer Science AI Machine Learning clustering Kernel Density Clustering tracking LiDAR 3D LiDAR tracking human pedestrian real time Datavetenskap Dataseende clustering SLR CVC KDEG KDE Kernel Density Clustering HDBSCAN DBSCAN LiDAR point cloud tracking human pedestrian Computer Sciences Datavetenskap (datalogi)
273	Probabilistic Multi-Modal Data Fusion and Precision Coordination for Autonomous Mobile Systems Navigation : A Predictive and Collaborative Approach to Visual-Inertial Odometry in Distributed Sensor Networks using Edge Nodes / Sannolikhetsbaserad fermodig datafusion och precision samordning för spårning av autonoma mobila system : En prediktiv och kant-samarbetande metod för visuell-inertial navigation i distribuerade sensornätverk Luppi, Isabella January 2023 (has links) This research proposes a novel approach for improving autonomous mobile system navigation in dynamic and potentially occluded environments. The research introduces a tracking framework that combines data from stationary sensing units and on-board sensors, addressing challenges of computational efficiency, reliability, and scalability. The work innovates by integrating spatially-distributed LiDAR and RGB-D Camera sensors, with the optional inclusion of on-board IMU-based dead-reckoning, forming a robust and efficient coordination framework for autonomous systems. Two key developments are achieved. Firstly, a point cloud object detection technique, "Generalized L-Shape Fitting”, is advanced, enhancing bounding box fitting over point cloud data. Secondly, a new estimation framework, the Distributed Edge Node Switching Filter (DENS-F), is established. The DENS-F optimizes resource utilization and coordination, while minimizing reliance on on-board computation. Furthermore, it incorporates a short-term predictive feature, thanks to the Adaptive-Constant Acceleration motion model, which utilizes behaviour-based control inputs. The findings indicate that the DENS-F substantially improves accuracy and computational efficiency compared to the Kalman Consensus Filter (KCF), particularly when additional inertial data is provided by the vehicle. The type of sensor deployed and the consistency of the vehicle's path are also found to significantly influence the system's performance. The research opens new viewpoints for enhancing autonomous vehicle tracking, highlighting opportunities for future exploration in prediction models, sensor selection, and precision coordination. / Denna forskning föreslår en ny metod för att förbättra autonom mobil systemsnavigering i dynamiska och potentiellt skymda miljöer. Forskningen introducerar ett spårningsramverk som kombinerar data från stationära sensorenheter och ombordssensorer, vilket hanterar utmaningar med beräkningsefektivitet, tillförlitlighet och skalbarhet. Arbetet innoverar genom att integrera spatialt distribuerade LiDAR- och RGB-D-kamerasensorer, med det valfria tillägget av ombord IMU-baserad dödräkning, vilket skapar ett robust och efektivt samordningsramverk för autonoma system. Två nyckelutvecklingar uppnås. För det första avanceras en punktmolnsobjektdetekteringsteknik, “Generaliserad L-formig anpassning”, vilket förbättrar anpassning av inneslutande rutor över punktmolnsdata. För det andra upprättas ett nytt uppskattningssystem, det distribuerade kantnodväxlingsfltret (DENSF). DENS-F optimerar resursanvändning och samordning, samtidigt som det minimerar beroendet av ombordberäkning. Vidare införlivar det en kortsiktig prediktiv funktion, tack vare den adaptiva konstanta accelerationsrörelsemodellen, som använder beteendebaserade styrentréer. Resultaten visar att DENS-F väsentligt förbättrar noggrannhet och beräknings-efektivitet jämfört med Kalman Consensus Filter (KCF), särskilt när ytterligare tröghetsdata tillhandahålls av fordonet. Den typ av sensor som används och fordonets färdvägs konsekvens påverkar också systemets prestanda avsevärt. Forskningen öppnar nya synvinklar för att förbättra spårning av autonoma fordon, och lyfter fram möjligheter för framtida utforskning inom förutsägelsemodeller, sensorval och precisionskoordinering. / Questa ricerca propone un nuovo approccio per migliorare la navigazione dei sistemi mobili autonomi in ambienti dinamici e potenzialmente ostruiti. La ricerca introduce un sistema di tracciamento che combina dati da unità di rilevazione stazionarie e sensori di bordo, afrontando le sfde dell’effcienza computazionale, dell’affdabilità e della scalabilità. Il lavoro innova integrando sensori LiDAR e telecamere RGB-D distribuiti nello spazio, con l’inclusione opzionale di una navigazione inerziale basata su IMU di bordo, formando un robusto ed effciente quadro di coordinamento per i sistemi autonomi. Vengono raggiunti due sviluppi chiave. In primo luogo, viene perfezionata una tecnica di rilevazione di oggetti a nuvola di punti, “Generalized L-Shape Fitting”, migliorando l’adattamento del riquadro di delimitazione sui dati della nuvola di punti. In secondo luogo, viene istituito un nuovo framework di stima, il Distributed Edge Node Switching Filter (DENS-F). Il DENS-F ottimizza l’utilizzo delle risorse e il coordinamento, riducendo al minimo la dipendenza dal calcolo di bordo. Inoltre, incorpora una caratteristica di previsione a breve termine, grazie al modello di movimento Adaptive-Constant Acceleration, che utilizza input di controllo basati sul comportamento del veicolo. I risultati indicano che il DENS-F migliora notevolmente l’accuratezza e l’effcienza computazionale rispetto al Kalman Consensus Filter (KCF), in particolare quando il veicolo fornisce dati inerziali aggiuntivi. Si scopre anche che il tipo di sensore impiegato e la coerenza del percorso del veicolo infuenzano signifcativamente le prestazioni del sistema. La ricerca apre nuovi punti di vista per migliorare il tracciamento dei veicoli autonomi, evidenziando opportunità per future esplorazioni nei modelli di previsione, nella selezione dei sensori e nel coordinamento di precisione. Distributed Sensor Networks Point Cloud Processing Bounding Box Fitting Trajectory Tracking Distributed Estimation Predictive Estimation Edge-Computing Reti di Sensori Distribuiti Elaborazione di Nuvole di Punti Riquadri di Delimitazione Tracciamento della Traiettoria Stima Distribuita Stima Predittiva Calcolo Distribuito. Distribuerade Sensornätverk Bearbetning av Punktmoln Anpassning av Begränsningsruta Trajektorieuppföljning Distribuerad Uppskattning Prediktiv Uppskattning Edge-datorbehandling Computer and Information Sciences Data- och informationsvetenskap
274	Long Strip Registration of TLS Point Clouds and Propagation of Uncertainty / Långsträckt registrering av TLS punktmoln och fortplantning av mätosäkerhet Wahlén, Edvin January 2024 (has links) Terrestrial laser scanning systems provide quick three-dimensional data acquisition with high spatial resolution. The technology can serve multiple applications such as monitoring and 3D modeling but several problems arise when scanning long objects such as tunnels, bridges, and corridors. Uncertainties in the data tend to propagate when conducting sequential registration on a long strip of point clouds and the errors can reach unacceptable levels only with a few scans. This thesis aims to examine this propagation of uncertainty caused by long strip registration and its effects on point cloud accuracy and precision. It further aims to evaluate the results of different ground control point distributions used for georeferencing and finally to construct guidelines and recommendations for the planning of terrestrial laser scanning projects of long objects. The thesis was divided into an analytical- and an experimental study. The analytical study explored the theoretical effects of long strip registration on a point cloud’s uncertainty by simulating repeated target-based registration in a 95-meter-long and 2.5-meter-wide corridor. This resulted in standard uncertainties of the point cloud coordinates on dm-level after the last registration and a clear trend in exponential growth. Tie point distribution in the overlaps between scans proved to have a big effect on the precision of the final point cloud. In the experimental study, a corridor of the same dimension was scanned from 16 stations and georeferenced in a local control network established at the scan site. Indirect registration in two steps was used, first by performing automatic feature-based registration in RealWorks before the point cloud was georeferenced. The results of different ground control point (GCP) scenarios were compared and evaluated by studying check point residuals. All scenarios had numerous check points that failed verification as the residuals exceeded the limit set to 15 mm. An even distribution of almost one GCP in every scan scene yielded the best results with a maximum check point residual of 3 cm in 3D. When the GCPs were clustered at one side of the corridor the check point residuals reached dm level at the opposite end. Also, a linear trend of increasing residuals as a function of distance from the point to the nearest GCP was evident. The linear trend was less evident when the GCPs were distributed evenly across the corridor. Their distribution proved more important than their quantity. The vertical coordinate component and the horizontal component perpendicular to the long side of the object proved less precise and accurate after long strip registration. This was apparent in both the analytical and the experimental study. / Terrester laserskaning erbjuder snabb, tre-dimensionell datafångst med hög punkttäthet. Teknologin täcker flertalet användningsområden såsom monitorering och 3D-modellering men problem kan uppstå vid skanning av långa objekt såsom tunnlar, broar och korridorer. Osäkerheter i data tenderar att fortplantas när registrering av en lång rad punktmoln genomförs sekventiellt och felen kan nå oacceptabla nivåer bara med några få skanningar. Detta examensarbete syftar till att undersöka denna fortplantning av mätosäkerhet orsakad vid registrering av långsträckta objekt och dess påverkan på punktmolnets noggrannhet och precision. Vidare syftar arbetet till att utvärdera resultatet av olika distributioner av utgångspunkter vid georeferering och slutligen till att utforma riktlinjer och rekommendationer för planering av terrestra laserskanningsprojekt över långa objekt. Arbetet delades upp i en analytisk- och en experimentell studie. Den analytiska studien undersökte de teoretiska effekterna av långsträckt registrering på ett punktmolns osäkerhet genom att simulera upprepad punktbaserad registrering i en 95-meter-lång och 2.5-meter-bred korridor. Detta resulterade i standardosäkerheter av punktmolnets koordinater på dm-nivå efter den sista registreringen samt en tydlig exponentiell ökning. Distributionen av konnektionspunkter i överlappen mellan skanningarna visade sig ha en stor effekt på precisionen av det slutliga punktmolnet. I den experimentella studien skannades en korridor av samma dimensioner från 16 stationer innan det georefererades till ett lokalt stomnät som etablerats på platsen. Indirekt georeferering i två steg användes, först genomfördes automatisk objektbaserad registrering i RealWorks innan punktmolnet georefererades. Resultatet av olika scenarion av utgångspunkter jämfördes och utvärderades genom att studera avvikelser hos kontrollpunkter. Alla scenarion hade flertalet kontrollpunkter vars avvikelser överskred den satta gränsen på 15 mm. En jämn distribution med upp till en utgångspunkt i för varje skanning medförde de bästa resultaten med en maximal avvikelse hos kontrollpunkterna på 3 cm i 3D. När utgångspunkterna var placerade på ena änden av korridoren så nådde avvikelserna dm-nivå på den motsatta änden. Dessutom så uppstod en tydlig linjär trend med ökande avvikelser som en funktion av avståndet till närmsta utgångspunkt. Den linjära trenden var mindre tydlig när utgångspunkterna var jämnt fördelade över korridoren. Deras distribution visade sig vara viktigare än deras kvantitet. Punktkoordinaternas höjdkomponent och plana komponent vinkelrätt mot objektets långsida visade sig vara mindre precis och noggrann efter långsträckt registrering. Detta var tydligt både i den analytiska och den experimentella studien. "Terrestrial Laser Scanning" "Registration" "Error Propagation" "Surveying" "Point Cloud" "Long Objects" "Tunnels" "Corridors" "Terrester laserskanning" "Registrering" "Felfortplantning" "Mätningsteknik" "Punktmoln" "Långsträckta objekt" "Tunnlar" "Korridorer" Civil Engineering Samhällsbyggnadsteknik
275	Automatic Point Cloud Registration for Mobile Mapping LiDAR Data : Developing an Automated Method for Registration of Light Rail Environment / Automatisk registrering av punktmoln från Mobile Mapping LiDAR data : Framställning av en automatisk metod för registrering i spårvägsmiljö Larsson, Milton, Wardman, Ellinor January 2024 (has links) Maintaining an inventory of transportation infrastructure assets is vital for effective management and maintenance. LiDAR (Light Detection and Ranging) can be a useful resource for this purpose by collecting detailed 3D information. Mobile Mapping Systems (MMS) refers to collecting geospatial data by mounting laser scanners on top of a moving vehicle, e.g. a car. The LiDAR collects XYZ-coordinates of the environment by emitting laser pulses toward the surveyed objects. This enables an effective way to store and survey built-up urban areas that otherwise would need an on-site presence. WSP uses Mobile Mapping (MM) to capture and visualize infrastructure, primarily for inventory purposes. Currently, the point cloud registration in the MM-process is labor-intensive, so the company is looking to automate it. This thesis aims to investigate methods to automate the process of point cloud registration that eliminates manual labor. The proposed method was evaluated with regards to its accuracy, advantages and disadvantages. The study area of the thesis was a light rail facility with surrounding residential buildings and vegetation. The proposed method was implemented in Python and utilizes open source libraries. The registration uses Fast Global Registration (FGR) for coarse alignment with Iterative Closest Point (ICP) for fine refinement. The FGR algorithm finds a rigid transformation between a pair of point clouds by establishing a feature correspondence set between the point clouds. The algorithm utilizes Fast Point Feature Histograms (FPFH) that simplifies the description of 3D point relationships as the feature descriptors. The object used for registration is the general area around catenary poles. The segments between poles is adjusted by linear interpolation of the obtained transformation matrices from the registration. The results of this thesis show that automatic point cloud registration is feasible. However, while the proposed method improves registration over raw data, it does not fully replace WSP's current procedure. The advantages of the proposed method are that it does not require classified data and is open source. The main source of error in the method is the presence of vegetation, and an experiment was conducted to support this hypothesis. The experiment shows that dense vegetation skews the registration, and generates an incorrect transformation matrix. Furthermore, the proposed method is only semi-automated, as it still needs manual post-processing. Accuracy assessment showed that removing outlier, presumably caused by vegetation, improved the planar offsets. Further studies to improve the result could utilize machine learning which could identify and extract poles for registration or remove surrounding vegetation. / Att upprätthålla inventering av tillgångar av transportinfrastruktur är avgörande för effektiv förvaltning och underhåll samt för att tillhandahålla korrekta data och underlätta beslutsfattande. LiDAR-data (Light Detection and Ranging) kan vara ett användbart verktyg för detta ändamål genom att samla in detaljerad 3D-information. Mobile Mapping Systems (MMS) refererar till att samla geospatial data genom att montera laserskannrar ovanpå taket på ett rörligt fordon, exempelvis en bil. LiDAR samlar XYZ-koordinater av kringliggande miljö genom att sända ut laserpulser mot de undersökta objekten. Detta möjliggör ett effektivt sätt att förvara och undersöka bebyggda stadsmiljöer som annars skulle behöva fysisk närvaro. WSP använder Mobile Mapping (MM) för att samla och visualisera infrastruktur, främst för inventeringsändamål. För närvarande är punktmolnregistreringen i MM-processen manuellt arbetskrävande, och därför vill WSP se en automatisering av processen. Detta examensarbete syftar till att undersöka metoder för att automatisera processen för registrering av punktmoln som eliminerar manuellt arbete. Den utvecklade metoden kommer att utvärderas med avseende på dess noggrannhet, för- och nackdelar. Arbetets studieområde är en järnvägsanläggninng med omgivande av bostadshus och vegetation. Den föreslagna metoden implementerades i Python och använder sig av open source-bibliotek. Registeringen tillämpar Fast Global Registration (FGR) för grov justering av punktmolnen, och Iterative Closest Point (ICP) för finjustering. FGR-algoritmen hittar en stel transformation mellan två punktmoln genom att etablera ett set av korresponderande attribut. Algoritmen använder Fast Point Feature Histograms (FPFH) som förenklar euklidiska förhållanden till attributbaserade förhållanden. Objekt som används för registrering är det generella området kring kontaktledningsstolpar. Segmenten mellan stolpar justeras genom linjär interpolation av de erhållna transformationsmatriserna från registreringen. Resultaten av detta arbete visar att automatisk registrering av punktmoln är genomförbar, och att metoden förbättrar registreringen jämfört med den råa datan. Den är dock inte tillräckligt bra för att helt ersätta den nuvarande proceduren som används av WSP. Fördelarna med den föreslagna metoden är att den inte kräver klassificerad data och är open source. Den huvudsakliga felkällan i metoden är förekomsten av vegetation, och ett experiment utfördes för att stödja denna hypotes. Experimentet visar att tät vegetation snedvrider registreringen och genererar en felaktig transformationsmatris. Vidare, är den föreslagna metoden endast semi-automatiserad, eftersom den fortfarande kräver manuell efterbearbetning. Noggrannhetsbedömningn visade att borttagningen av avvikande värden, förmodligen orsakade av vegetation, förbättrade den plana förskjutningen. Vidare studier för att ge ett mer tillfredsställande resultatet kan möjligen vara att använda maskininlärning för att identifiera och extrahera stolpar för matching, samtidigt som växtligheten kan elimineras. "Mobile Mapping" "Point Cloud Registration" "LiDAR" "Automation" "Global Registration" "Iterative Closest Point" "Mobile Mapping" "Punktmolnsregistrering" "LiDAR" "Automatisering" "Global Registration" "Iterative Closest Point" Civil Engineering Samhällsbyggnadsteknik
276	Generation and Optimization of Local Shape Descriptors for Point Matching in 3-D Surfaces Taati, BABAK 01 September 2009 (has links) We formulate Local Shape Descriptor selection for model-based object recognition in range data as an optimization problem and offer a platform that facilitates a solution. The goal of object recognition is to identify and localize objects of interest in an image. Recognition is often performed in three phases: point matching, where correspondences are established between points on the 3-D surfaces of the models and the range image; hypothesis generation, where rough alignments are found between the image and the visible models; and pose refinement, where the accuracy of the initial alignments is improved. The overall efficiency and reliability of a recognition system is highly influenced by the effectiveness of the point matching phase. Local Shape Descriptors are used for establishing point correspondences by way of encapsulating local shape, such that similarity between two descriptors indicates geometric similarity between their respective neighbourhoods. We present a generalized platform for constructing local shape descriptors that subsumes a large class of existing methods and allows for tuning descriptors to the geometry of specific models and to sensor characteristics. Our descriptors, termed as Variable-Dimensional Local Shape Descriptors, are constructed as multivariate observations of several local properties and are represented as histograms. The optimal set of properties, which maximizes the performance of a recognition system, depend on the geometry of the objects of interest and the noise characteristics of range image acquisition devices and is selected through pre-processing the models and sample training images. Experimental analysis confirms the superiority of optimized descriptors over generic ones in recognition tasks in LIDAR and dense stereo range images. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2009-09-01 11:07:32.084 computer vision range data object recognition tracking local shape descriptor point matching pose estimation pose acquisition 3-D 3D point cloud satellite tracking optimization range image processing range image RANSAC registration alignment surface computational geometry detection localization model-based object identification point correspondence feature selection VD-LSD LSD genetic algorithm simulated annealing forward feature selection multivariate features subset selection local properties LIDAR dense stereo stereo precision feature matching machine learning training learning phase preprocessing
277	Reconnaissance Radar Robot Holm, Kasper, Henrysson, Erik January 2023 (has links) During the last century, various countries' armed forces have used unmanned aerial vehicles, commonly known as drones. In recent years, strives have been made to develop small commercial drones that have allowed the general public to afford and use them for recreational purposes. The availability of drones has led to immoral applications of the technology. Such applications need to be faced with anti-measures and effective detection methods. Therefore, this thesis aims to develop a mobile reconnaissance robot that can detect commercial drones with radar. It describes integrating radar sensors with single-board computers to detect and localise air-bound objects. The finished product aims to be used for educational and exhibition purposes at the Swedish Armed Forces technical school to increase awareness of the technology. / <p>Försvarsmaktens tekniska skola i Halmstad var intressenter för uppsatsen.</p> radar signal processing signal processing state machine state machine test testing radar testing radar technology frequency modulated continuous wave fmcw pulse radar raspberry pi single board computer sbc poe power over ethernet mqtt mqtt package python drones uav unmanned aerial vehicle drone detection radar robot swedish armed forces swedish armed forces technical school fmts beamforming fast fourier transform fft point cloud point clustering software design Computer and Information Sciences Data- och informationsvetenskap Computer Sciences Datavetenskap (datalogi) Software Engineering Programvaruteknik Signal Processing Signalbehandling Computer Systems Datorsystem
278	Implementation and Analysis of Co-Located Virtual Reality for Scientific Data Visualization Jordan M McGraw (8803076) 07 May 2020 (has links) <div>Advancements in virtual reality (VR) technologies have led to overwhelming critique and acclaim in recent years. Academic researchers have already begun to take advantage of these immersive technologies across all manner of settings. Using immersive technologies, educators are able to more easily interpret complex information with students and colleagues. Despite the advantages these technologies bring, some drawbacks still remain. One particular drawback is the difficulty of engaging in immersive environments with others in a shared physical space (i.e., with a shared virtual environment). A common strategy for improving collaborative data exploration has been to use technological substitutions to make distant users feel they are collaborating in the same space. This research, however, is focused on how virtual reality can be used to build upon real-world interactions which take place in the same physical space (i.e., collaborative, co-located, multi-user virtual reality).</div><div><br></div><div>In this study we address two primary dimensions of collaborative data visualization and analysis as follows: [1] we detail the implementation of a novel co-located VR hardware and software system, [2] we conduct a formal user experience study of the novel system using the NASA Task Load Index (Hart, 1986) and introduce the Modified User Experience Inventory, a new user study inventory based upon the Unified User Experience Inventory, (Tcha-Tokey, Christmann, Loup-Escande, Richir, 2016) to empirically observe the dependent measures of Workload, Presence, Engagement, Consequence, and Immersion. A total of 77 participants volunteered to join a demonstration of this technology at Purdue University. In groups ranging from two to four, participants shared a co-located virtual environment built to visualize point cloud measurements of exploded supernovae. This study is not experimental but observational. We found there to be moderately high levels of user experience and moderate levels of workload demand in our results. We describe the implementation of the software platform and present user reactions to the technology that was created. These are described in detail within this manuscript.</div> Software Engineering Computer Software Computer Graphics Simulation and Modelling Virtual Reality and Related Simulation Mobile Technologies Networking and Communications Computer-Human Interaction Collaborative Virtual Reality Virtual Reality Collaborative Virtual Reality Networking Networked VR Mulitplayer Multiplayer VR Data Visualization Data Exploration Head Mounted Display Cyberspace Augmented Reality Emerging Technologies Consumer Devices Virtual Environment Virtual Aesthetics Collaborative VR Simulation Training Simulation and Training Colocated Colocated Virtual Environment Immersive Media Immersive Technology Collaborative Tools Educational Virtual Environment Point Cloud Representations Isosurface Representations Mesh Smoothng Supernova Supernovae Supernova Remnant Modeling Astronomy and Astrophysics Cassiopeia N132D E0102 Crab Nebula CAVE CAVE Theater Mobile VR Virtual Classrooms STEM Workload Workload Assessment Tool Presence Engagement Immersion Experience Consequence Simulator sickness Simulator Sickness Questionnaire NASA TLX NASA Task Load Index Unified User Experience Quesionnaire Modified User Experience Questionnaire Oculus Oculus Go Oculus Quest Novel Solutions Optitrack Motive Motion Capture Mocap User Experience Unity3D Game Engine Lidgren Room Scale VR Hall Scale VR Co-location Colocation Virtual Training Collaborative Virtual Environments Shared Virtual Environments Collaboration Whiteboard Mobile Virtual Reality VR HMD LAN UX CSCL UXQ TLX AR VE CVE 3D UI EVE

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