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.

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Diese Arbeit untersucht die Funktionsweise, Herausforderungen und Lösungsansätze des 2D- und 3D Projection Mappings. Insbesondere wird darauf eingegangen, wie dreidimensionale Abbildungen der Realität (3D Scans) genutzt werden und wie sie erstellt werden können, wobei der Fokus auf 3D Scans liegt, die durch photogrammetrische Rekonstruktion mit der Software Meshroom entstanden sind. Ein Ziel dieser Arbeit besteht darin, die beschriebenen Themen so darzustellen, dass sie auch für semiprofessionelle Endanwender verständlich sind. Die Arbeit soll als umfassende und gebündelte Informationssammlung dienen, die als Grundlage für eigene Projekte und vertiefte Forschungen genutzt werden kann.:I. Einleitung II. Begriffe und Definitionen III. Augmented Reality 1. Direct Augmentation 1.1. Projektionsinhalte 1.2. Einrichtung der Projektoren 1.3. Previsualisierung IV. 3D Rekonstruktionen 2. Analog-Digital-Wandlung 3. Rekonstruktionsmethoden 3.1. Aktive Rekonstruktionen 3.2. Passive Rekonstruktionen 3.3. Stereo- und Multiple-Kamera-Setups 3.4. Tiefenberechnung 3.5. Photogrammetrische Pipeline V. Praxisteil 4. Methodik 5. Vorgehensweise Experimentreihe 1 5.1. Referenzmodelle 5.2. Wahl der Referenzobjekte 5.3. Scanning der Referenzobjekte 5.4. Photogrammetrische Rekonstruktion 5.4.1. Aufzeichnung der Fotodatenbanken 5.4.2. Aufbereitung der Aufnahmen 5.4.3. Meshroom-Pipeline 5.4.4. Aufbereitung der Meshes 5.5. Vergleich der Meshes 6. Ergebnisse 7. Auswertung 8. Vorgehensweise Experimentreihe 2 8.1. Aufbau des Experiments 8.2. Aufzeichung der initialen Rekonstruktionsdatenbank 8.3. Rekonstruktion des Gebäudes 8.4. Aufbereitung des Meshes 8.5. Anfertigung von Vorlagen für die Projektionseinrichtung 8.6. Kreation von Projektionsinhalten 8.7. Virtuelle Visualisierung 9. Ergebnisse 10.Auswertung 11.Diskussion 12.Fazit und Ausblick A. Literaturverzeichnis B. Abbildungsverzeichnis C. Messergebnisse D. Datenbanken E. Abbildungen

Autonomous features in vehicles is already a big part of the automobile area and now many companies are looking for ways to make vehicles fully autonomous. Autonomous vehicles need to get information about the surrounding environment. The information is extracted from exteroceptive sensors and today vehicles often use laser scanners for this purpose. Laser scanners are very expensive and fragile, it is therefore interesting to investigate if cheaper radar sensors could be used. One big challenge when it comes to autonomous vehicles is to be able to use the exteroceptive sensors and extract a position of the vehicle and at the same time get a map of the environment. The area of Simultaneous Localization and Mapping (SLAM) is a well explored area when using laser scanners but is not that well explored when using radars. It has been investigated if it is possible to use radar sensors on a truck to create a map of the area where the truck drives. The truck has been equipped with ego-motion sensors and radars and the data from them has been fused together to get a position of the truck and to get a map of the surrounding environment, i.e. a SLAM algorithm has been implemented. The map is represented by an Occupancy Grid Map (OGM) which should only consist of static objects. The OGM is updated probabilistically by using a binary Bayes filter. To localize the truck with help of motion sensors an Extended Kalman Filter (EKF) is used together with a map and a scan match method. All these methods are put together to create a SLAM algorithm. A range rate filter method is used to filter out noise and non-static measurements from the radar. The results of this thesis show that it is possible to use radar sensors to create a map of a truck's surroundings. The quality of the map is considered to be good and details such as space between parked trucks, signs and light posts can be distinguished. It has also been proven that methods with low performance on their own can together with other methods work very well in the SLAM algorithm. Overall the SLAM algorithm works well but when driving in unexplored areas with a low number of objects problems with positioning might occur. A real time system has also been implemented and the map can be seen at the same time as the truck is manoeuvred.

Radar map generation using binary Bayes filter or what is commonly known as Inverse Sensor Model; which translates the sensor measurements into grid cells occupancy estimation, is a classical problem in different fields. In this work, the focus will be on development of Inverse Sensor Model for parking space using 77 GHz FMCW (Frequency Modulated Continuous Wave) automotive radar, that can handle different environment geometrical complexity in a parking space. There are two main types of Inverse Sensor Models, where each has its own assumption about the sensor noise. One that is fixed and is similar to a lookup table, and constructed based on combination of sensor-specific characteristics, experimental data and empirically-determined parameters. The other one is learned by using ground truth labeling of the grid map cell, to capture the desired Inverse Sensor Model. In this work a new Inverse Sensor Model is proposed, that make use of the computational advantage of using fixed Inverse Sensor Model and capturing desired occupancy estimation based on ground truth labeling. A derivation of the occupancy grid mapping problem using binary Bayes filtering would be performed from the well known SLAM (Simultaneous Localization and Mapping) problem, followed by presenting the Adaptive Inverse Sensor Model, that uses fixed occupancy estimation but with adaptive occupancy shape estimation based on statistical analysis of the radar measurements distribution across the acquisition environment. A prestudy of the noise nature of the radar used in this work is performed, to have a common Inverse Sensor Model as a benchmark. Then the drawbacks of such Inverse Sensor Model would be addressed as sub steps of Adaptive Inverse Sensor Model, to be able to haven an optimal grid map occupancy estimator. Finally a comparison between the generated maps using the benchmark and the adaptive Inverse Sensor Model will take place, to show that under the fulfillment of the assumptions of the Adaptive Inverse Sensor Model, the Adaptive Inverse Sensor Model can offer a better visual appealing map to that of the benchmark.