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Bedingt durch die zunehmende Prävalenz der Rhythmusstörung Vorhofflimmern mit den assoziierten Morbiditäts- und Mortalitätsrisiken ist die Entwicklung und Etablierung kurativer Therapieverfahren von klinischem und wissenschaftlichem Interesse. Entsprechend dem pathophysiologischen Verständnis der Arrhythmie-induzierenden Triggeraktivität und des Arrhythmie-erhaltenden Flimmersubstrat mit der vorrangigen anatomisch-strukturellen Lokalisation beider Entitäten im Übergangsbereich der großen Pulmonalvenentrichter in den posterioren linken Vorhof wurden katheterinterventionelle Ablationskonzepte als potentiell kurative Therapien entwickelt. Limitationen der praktischen Umsetzung theoretischer Linienkonzepte ergeben sich aus komplexen anatomischen Gegebenheiten und instabilen Zugangsbedingungen infolge Atmung und kardialer Mobilität. Aufbauend auf non-fluoroskopischen Navigationssystemen wurden Verfahren für vollständig Modell-integrierte Ansätze der Ablationslinienplatzierung entwickelt, bei denen Planung, Durchführung und Validierung der Ablation an anatomisch korrekten dreidimensionalen CT-Modellen des linken Vorhofes erfolgen. Zur Verbesserung instabiler Zugangsbedingungen wurden Verfahren der Katheternavigation mittels steuerbaren Schleusensystemen eingeführt und in entsprechenden Studien mit klinischen Endpunkten validiert. Zu objektivierbaren Erfassung von Energietransfer und myokardialer Läsionsbildung während der Ablation wurden katheterinterventionelle Kontakttechnologien, die auf der Messung lokaler komplexer Impedanzen zwischen Katheter und Gewebe beruhen, in der ersten klinischen Anwendung erprobt und validiert. Mit diesen technologischen Entwicklungen gelangen eine zunehmend akkuratere klinische Umsetzung theoretischer Ablationskonzepte und damit eine Etablierung des Therapiekonzeptes als klinisches Standardverfahren. Eine zur Abschätzung des Nutzen/Risiko-Profils nötige detaillierte Komplikationsanalyse stellte die Ösophagusverletzung als schwerste Komplikation heraus, die mit 0,3% selten auftrat, aber für nahezu alle langfristigen Folgeschäden verantwortlich war. Entwicklungen zur periprozeduralen Visualisierung des Ösophagus mit paralleler intraösophagealer Temperaturmessung sind Ansätze zur Vermeidung dieser Komplikation in der Zukunft. Bedingt durch das Auftreten und die postinterventionelle Zunahme asymptomatischer Flimmerrezidive ist die Frage eines objektiven Vorhofflimmermonitorings von entscheidender Bedeutung für die Beurteilung der Effektivität der Ablation sowie weitergehende klinische und wissenschaftliche Fragestellungen; wie die Indikation zur Antikoagulation oder den Vergleich von Rhythmus- und Frequenzkontrolle. 7-Tage-LzEKGs und transtelephonische EKGs wurden als Standard zum Monitoring innerhalb klinischer Vorhofflimmerstudien etabliert. Entwicklungen im Bereich implantierbarer kontinuierlicher Rhythmusmonitore werden in Zukunft das Netz zur Erfassung asymptomatischen Vorhofflimmerns weiter verdichten.

In an existing process, Rapid 3D Mapping datasets are automatically generated and converted to a format suitable for simulators and mission support systems. However, this high level automation does not allow manual error fixes and the addition of important features to the map data. The aim of this thesis is to create a process that allows the editing of a number of R3DM tiles with an existing 3D modeling tool and, after editing, to propagate the changes to the lower levels of detail in the data. Two solutions for the propagation, a standard solution and an alternative, are presented in this thesis. The motivation behind the alternative solution is to keep the original geometry of the R3DM data to retain the good rendering performance which those data sets permit. To achieve this the new model will be added to the R3DM data at only the highest level of detail, and this model is then simplified before propagation to the lower levels of detail. The resulting procedure to fix errors and add new important features is very effective, and reduces the time required for users to edit the many levels of the R3DM data.

Many potential indoor applications exist for autonomous vehicles, such as automated surveillance, inspection, and document delivery. A key requirement for autonomous operation is for the vehicles to be able to detect and map obstacles in order to avoid collisions. This work develops a comprehensive 3D scene reconstruction algorithm based on known vehicle motion and vision data that is specifically tailored to the indoor environment. Visible light cameras are one of the many sensors available for capturing information from the environment, and their key advantages over other sensors are that they are light weight, power efficient, cost effective, and provide abundant information about the scene. The emphasis on 3D indoor mapping enables the assumption that a large majority of the area to be mapped is comprised of planar surfaces such as floors, walls and ceilings, which can be exploited to simplify the complex task of dense reconstruction of the environment from monocular vision data. In this thesis, the Planar Surface Reconstruction (PSR) algorithm is presented. It extracts surface information from images and combines it with 3D point estimates in order to generate a reliable and complete environment map. It was designed to be used for single cameras with the primary assumptions that the objects in the environment are flat, static and chromatically unique. The algorithm finds and tracks Scale Invariant Feature Transform (SIFT) features from a sequence of images to calculate 3D point estimates. The individual surface information is extracted using a combination of the Kuwahara filter and mean shift segmentation, which is then coupled with the 3D point estimates to fit these surfaces in the environment map. The resultant map consists of both surfaces and points that are assumed to represent obstacles in the scene. A ground vehicle platform was developed for the real-time implementation of the algorithm and experiments were done to assess the PSR algorithm. Both clean and cluttered scenarios were used to evaluate the quality of the surfaces generated from the algorithm. The clean scenario satisfies the primary assumptions underlying the PSR algorithm, and as a result produced accurate surface details of the scene, while the cluttered scenario generated lower quality, but still promising, results. The significance behind these findings is that it is shown that incorporating object surface recognition into dense 3D reconstruction can significantly improve the overall quality of the environment map.

ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / The Western Aeronautical Test Range of the National Aeronautics and Space Administration's Dryden Flight Research Center needed to address the aging software and hardware of its current situational awareness display application, the Global Real-Time Interactive Map (GRIM). GRIM was initially developed in the late 1980s and executes on older PC architectures using a Linux operating system that is no longer supported. Additionally, the software is difficult to maintain due to its complexity and loss of developer knowledge. It was decided that a replacement application must be developed or acquired in the near future. The replacement must provide the functionality of the original system, the ability to monitor test flight vehicles in real-time, and add improvements such as high resolution imagery and true 3-dimensional capability. This paper will discuss the process of determining the best approach to replace GRIM, and the functionality and capabilities of the first release of the Positional Awareness Map 3D.

Many potential indoor applications exist for autonomous vehicles, such as automated surveillance, inspection, and document delivery. A key requirement for autonomous operation is for the vehicles to be able to detect and map obstacles in order to avoid collisions. This work develops a comprehensive 3D scene reconstruction algorithm based on known vehicle motion and vision data that is specifically tailored to the indoor environment. Visible light cameras are one of the many sensors available for capturing information from the environment, and their key advantages over other sensors are that they are light weight, power efficient, cost effective, and provide abundant information about the scene. The emphasis on 3D indoor mapping enables the assumption that a large majority of the area to be mapped is comprised of planar surfaces such as floors, walls and ceilings, which can be exploited to simplify the complex task of dense reconstruction of the environment from monocular vision data. In this thesis, the Planar Surface Reconstruction (PSR) algorithm is presented. It extracts surface information from images and combines it with 3D point estimates in order to generate a reliable and complete environment map. It was designed to be used for single cameras with the primary assumptions that the objects in the environment are flat, static and chromatically unique. The algorithm finds and tracks Scale Invariant Feature Transform (SIFT) features from a sequence of images to calculate 3D point estimates. The individual surface information is extracted using a combination of the Kuwahara filter and mean shift segmentation, which is then coupled with the 3D point estimates to fit these surfaces in the environment map. The resultant map consists of both surfaces and points that are assumed to represent obstacles in the scene. A ground vehicle platform was developed for the real-time implementation of the algorithm and experiments were done to assess the PSR algorithm. Both clean and cluttered scenarios were used to evaluate the quality of the surfaces generated from the algorithm. The clean scenario satisfies the primary assumptions underlying the PSR algorithm, and as a result produced accurate surface details of the scene, while the cluttered scenario generated lower quality, but still promising, results. The significance behind these findings is that it is shown that incorporating object surface recognition into dense 3D reconstruction can significantly improve the overall quality of the environment map.

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A Ground Penetrating Radar (GPR) is a non-invasive measurement tool to locate objects in the subsurface. The GPR transmits electromagnetic waves into the ground and records the waves reflected from surface interfaces of different materials. To accurately find these surfaces after measuring, it is important to record the precise location of the GPR and minimize reflected noise. Since a GPR cannot distinguish the direction from which the waves were reflected, this can result in a misinterpretation of the data if waves are reflected from surrounding objects. This problem can be reduced by also mapping objects in the surroundings. The work of this thesis is aimed at implementing a system that uses a Real-Time Kinematics (RTK) GNSS (Global Navigation Satellite System) receiver for precise positioning together with a 2D-LiDAR (Light Detection And Ranging) to record a 3D map of the surroundings. We used the 3D-LiDAR system to record vertical planes (cross-sections) that were processed into a 3D volume map. We found that the RTK GNSS receiver performed well and delivered the position within centimeters when provided with corrections, while it was about 2.5 m off without corrections. The performance was compared with a professional-grade Leica RTK receiver and the difference in latitude and longitude ranged from 0.001-0.002 m and 0.002-0.004 m, respectively. By fusing the RTK position with the LiDAR data using the software Robot Operating System (ROS), we created 3D maps that represented the surroundings along the traveled path. Our developed system, consisting of an RTK GNSS receiver and the 2D LiDAR, gave promising results and we are optimistic that combining the system with a GPR can improve the interpretation of the subsurface. Thus, the proposed method seems promising to be used during GPR mapping.

Every year, the ports of entry of the continental United States receive millions of containers from container ships for processing. These containers contain everything that the country imports, and sometimes regulated items can be hidden inside them in attempt to smuggle them illegally into the country. Some of these items may be radioactive material meant for criminal purposes and represent a threat to national security. The containers are currently being scanned for radioactivity as they leave the port, but before leaving the port, containers can sit inside the port for weeks. It can be beneficial to scan these containers before they are picked up to catch the illegal material sooner and reduce the risk of danger to those nearby. Uncrewed Aerial Systems can be useful for scanning container stacks in container fields since they can be attached with sensors and reach heights that are difficult for humans. They can also scan autonomously, requiring less over watch from people. This thesis attempts to solve the problem of autonomous search by using an initial 3D scan of the search area to input into a 3D path planning algorithm to generate a flight path that will sufficiently scan the search area while minimizing flight time. Coverage is a main area of concern, as well is computational complexity and time. In order to maintain security of the aircraft, the path must be generated on-board the aircraft, and as such use on-board, lightweight, computers. The approach taken in this thesis is by breaking the problem down into 2D layers, and then developing paths on each layer based on where the obstacles are. In order to maximize coverage, contours are generated around the obstacles. The vertices of the contours are then treated like points to visit in a Travelling Salesman Problem. To incentivize paths that run alongside the obstacles for better radiation detection, paths that do not run close to the obstacles are given a higher cost than those that do, resulting in a cost-minimizing path planning algorithm yielding paths that stay close to obstacles. The Travelling Salesman Problem algorithm then yields the most time effective path to cover the area while maintaining a distance healthy for radiation scanning from the obstacles. / Master of Science / Every year, the ports of entry of the continental United States receive millions of containers from container ships for processing. These containers contain everything that the country imports, and sometimes regulated items can be hidden inside them in attempt to smuggle them illegally into the country. Some of these items may be radioactive material meant for criminal purposes and represent a threat to national security. It can be beneficial to scan these containers before they are picked up to catch the illegal material sooner and reduce the risk of danger to those nearby. Uncrewed Aerial Systems can be useful for scanning container stacks in container fields since they can be attached with sensors and reach heights that are difficult for humans. They can also scan autonomously, requiring less over watch from people. This thesis attempts to solve the problem of autonomous search by using an initial 3D scan of the search area to input into a 3D path planning algorithm to sufficiently scan the search area while minimizing flight time.

The robotics field has grown in recent years to a point where unmanned systems are no longer limited by their capabilities. As such, the mission profiles for unmanned systems are becoming more and more complicated, and a demand has risen for the deployment of unmanned systems into the most complex of environments. Additionally, the objectives for unmanned systems are once more complicated by the necessity for beyond line of sight teleoperation, and in some cases complete vehicle autonomy. Such systems require adequate sensory devices for appropriate situational awareness. Additionally, a large majority of what is currently being done with unmanned systems requires visual data acquisition. A stereo vision system is ideal for such missions as it doubles as both an image acquisition device, and a range finding device. The 2D images captured with a stereo vision system can be mapped to three dimensional point clouds with reference to the optic center of one of the stereo cameras. While stand alone commercial stereo vision systems are capable of doing just that, the GPS/INS aided stereo vision system also has integrated 3-axis accelerometers, 3-axis gyros, 3-axis magnetometer, and GPS receiver allowing for the measurement of the system's position and orientation in global coordinates. This capability provides the potential to geo-reference the 3D data captured with the stereo camera. The GPS/INS aided stereo vision system integrates a combination of commercial and in-house developed devices. The total system includes a Point Grey Research Bumblebee stereovision camera, a Versalogic PC104 computer, a PCB designed for sensor acquisition and power considerations, and a self contained battery. The entire system is all contained within a 9.5â x 5â x 6.5â aluminum enclosure and weighs approximately 6 lbs. The system is also accompanied with a graphical user interface which displays the geo-referenced data within a 3D virtual environment providing adequate sensor feedback for a teleoperated unmanned vehicle. This thesis details the design and implementation of the hardware and software included within this system as well as the results of operation. / Master of Science