Unmanned aerial vehicle (UAV) networks, a flying platform, are a promising wireless communications infrastructure with wide-ranging applications in both commercial and military domain. Owing to the appealing characteristics, such as high mobility, high feasibility, and low cost, UAV networks can be applied in various scenarios, such as emergency communications, cellular networks, device-to-device (D2D) networks, and sensor networks, regardless of infrastructure and spatial constraints. To handle complicated missions, provide wireless coverage for a large range, and have a long lifetime, a UAV network may consist of a large amount of UAVs, working cooperatively as a swarm, also referred to as swarm UAV networks. Although high mobility and numerous UAVs offer high flexibility, high scalability, and performance enhancement for swarm UAV networks, they also incur some technical challenges. One of the major challenges is the routing protocol design. With high mobility, a dynamic network topology may be encountered. As a result, traditional routing protocols based on routing path discovery are not applicable in swarm UAV networks, as the discovered routing path may be outdated especially when the amount of UAVs is large causing considerable routing path discovery delay. Multicast is an essential and key technology in the scenarios, where swarm UAV networks are employed as aerial small base station (BSs), like relay or micro BS. Swarm UAV networks consisting of a large amount of UAVs will encounter severe multicast delay with existing multicast methods using acknowledgement (ACK) feedback and retransmissions. This issue will be deteriorated when a swarm UAV network is deployed far away from BSs, causing high packet loss. Data exchange is another major technical challenge in swarm UAV networks, where UAVs exchange data packets with each other, such as requesting and retrieving lost packets. Due to numerous UAVs, data exchange between UAVs can cause message and signaling storm, resulting in a long data exchange delay and severe ovehead. In this dissertation, I focus on developing novel routing protocols, multicast schemes, and data exchange schemes, enabling efficient, robust, and high-performance routing, multicast, and data exchange in swarm UAV networks. To be specific, two novel flooding-based routing protocols are designed in this dissertation, where random network coding (RNC) is utilized to improve the efficiency of the flooding-based routing in swarm UAV networks without relying on network topology information and routing path discovery. Using the property of RNC that as long as sufficient different versions of encoded packets/generations are accumulated, original packets could be decoded, RNC is naturally able to accelerate the routing process. This is because the use of RNC can reduce the number of encoded packets that are required to be delivered in some hop. In a hop, the receiver UAV may have already overheard some generations in previous hops, so that it only needs to receive fewer generations from the transmitter UAV in the current hop. To further expedite the flooding-based routing, the second flooding-based routing protocol is designed, where each forwarding UAV creates a new version of generation by linearly combining received generations rather than by decode original packets. Despite the flooding-based routing significantly hastened by RNC, the inherent drawback of the flooding-based routing is still unsolved, namely numerous hops. Aiming at reducing the amount of hops, a novel enhanced flooding-based routing protocol leveraging clustering is designed, where the whole UAV network will be partitioned into multiple clusters and in each cluster only one UAV will be selected as the representative of this cluster, participating in the flooding-based routing process. By this way, the number of hops is restricted by the number of representatives, since packets are only flooded between limited representatives rather than numerous UAVs. To address the multicast issue in swarm UAV networks, a novel multicast scheme is proposed based on clustering, where a UAV experiencing packet loss will retrieve the lost packets by requesting other UAVs in the same cluster without depending on retransmissions of BSs. In this way, the lost packet retrieval is carried out through short-distance data exchange between UAVs with reliable transmissions and a short delay. Tractable stochastic geometry tools are used to model swarm UAV networks with a dynamic network topology, based on which comprehensive analytical performance analysis is given. To enable efficient data exchange between UAVs in swarm UAV networks, a data exchange scheme is proposed utilizing unsupervised learning. With the proposed scheme, all UAVs are assigned to multiple clusters and a UAV can only carry out data exchange within its cluster. By this way, UAVs in different clusters perform data exchange in a parallel fashion to expedite data exchange. The agglomerative hierarchical clustering, a type of unsupervised learning, is used to conduct clustering in order to guarantee that UAVs in the same cluster are able to supply and supplement each other's lost packets. Additionally, a data exchange mechanism, including a novel random backoff procedure, is designed, where the priorities of UAVs in data exchange determined by the number of their lost packets or requested packets that they can provide. As a result, each request-reply process would be taken fully advantage, maximally supplying lost packets not only to the UAV sending request, but also to other UAVs in the same cluster. For all the developed technologies in this dissertation, their technical details and the corresponding system procedures are designed based on low-complexity and well-developed technologies, such as the carrier sense multiple access/collision avoidance (CSMA/CA), for practicability in practice and without loss of generality. Moreover, extensive simulation studies are conducted to demonstrate the effectiveness and superiority of the proposed and developed technologies. Additionally, system design insights are also explored and revealed through simulations. / Doctor of Philosophy / Compared to fixed infrastructures in wireless communications, unmanned aerial vehicle (UAV) networks possess some significant advantages, such as low cost, high mobility, and high feasibility, making UAV networks have a wide range of applications in both military and commercial fields. However, some characteristics of UAV networks, including dynamic network topology and numerous UAVs, may become technical barriers for wireless communications. One of the major challenges is the routing protocol design. Routing is the process of selecting a routing path, enabling data delivered from a node (source) to another desired node (destination). Traditionally, routing is performed based on routing path discovery, where control packets are broadcasted and the path, on which a control packet first reaches the destination, will be selected as routing path. However, in UAV networks, routing path discovery may experience a long delay, as control packets go through many UAVs. Besides, the discovered routing path may be outdated, as the topology of UAV networks change over time. Another key technology in wireless communications that may not work well in UAV networks is multicast, where a transmitter, like a base station (BS), broadcasts data to UAVs and all UAVs are required to receive this data. With numerous UAVs, multicast delay may be severe, since the transmitter will keep retransmitting a data packet to UAVs until all UAVs successfully receive the packet. This issue will be deteriorated when a UAV network is deployed far away from BSs, causing high packet loss. Data exchange between UAVs is a fundamental and important system procedure in UAV networks. A large amount of UAV in a UAV network will cause serious data exchange delay, as many UAVs have to compete for limited wireless resources to request or send data. In this dissertation, I focus on developing novel technologies and schemes for swarm UAV networks, where a large amount of UAVs exist to make UAV networks powerful and handle complicated missions, enable efficient, robust, and high-performance routing, multicast, and data exchange system procedures. To be specific, two novel flooding-based routing protocols are designed, where random network coding (RNC) is utilized to improve the efficiency of flooding-based routing without relying on any network topology information or routing path discovery. The use of RNC could naturally expedite flooding-based routing process. With RNC, a receiver can decode original packets as long as it accumulates sufficient encoded packets, which may be sent by different transmitters in different hops. As a result, in some hops, fewer generations may be required to be transmitted, as receivers have already received and accumulated some encoded in previous hops. To further improve the efficiency of flooding-based routing, another routing protocol using RNC is designed, where UAVs create new encoded packets by linearly combining received encoded packets rather than linearly combing original packets. Apparently, this method would be more efficient. UAVs do not need to collect sufficient encoded packets and decode original packets, while only linearly combining all received encoded packets. Although RNC could effectively improve the efficiency of flooding-based routing, the inherent drawback is still unsolved, which is a large amount of hops caused by numerous UAVs. Thus, an enhanced flooding-based routing protocol using clustering is designed, where the whole UAV network will be partitioned into multiple clusters. In each cluster only one UAV will be selected as the representative of this cluster, participating in the flooding-based routing process. By this way, the number of hops could be greatly reduced, as packets are only flooded between limited representatives rather than numerous UAVs. To address the multicast issue in swarm UAV networks, a novel multicast scheme is proposed, where a UAV experiencing packet loss will retrieve its lost packets by requesting other UAVs in the same cluster without depending on retransmissions of BSs. In this way, the lost packet retrieval is carried out through short-distance data exchange between UAVs with reliable transmissions and a short delay. Then, the optimal number of clusters and the performance of the proposed multicast scheme are investigated by tractable stochastic geometry tools. If all UAVs closely stay together in a swarm UAV network, long data exchange delay would be significant technical issue, since UAVs will cause considerable interference to each other and all UAVs will compete for spectrum access. To cope with that, a data exchange scheme is proposed leveraging unsupervised learning. To avoid interference between UAVs and a long-time waiting for spectrum access, all UAVs are assigned to multiple clusters and different clusters use different frequency bands to carry out data exchange simultaneously. The agglomerative hierarchical clustering, a type of unsupervised learning, is used to conduct clustering, guaranteeing that UAVs in the same cluster are able to supply and supplement each other's lost packets. Additionally, a data exchange mechanism is designed, facilitating that a UAV with more lost packets or more requested packets has a higher priority to carry out data exchange. In this way, each request-reply process would be taken fully advantage, maximally supplying lost packets not only to the UAV sending request, but also to other UAVs in the same cluster. For all the developed technologies in this dissertation, their technical details and the corresponding system procedures are designed based on low-complexity and well-developed technologies, such as the carrier sense multiple access/collision avoidance (CSMA/CA), for practicability in reality and without loss of generality. Moreover, extensive simulation studies are conducted to demonstrate the effectiveness and superiority of the developed technologies. Additionally, system design insights are also explored and revealed through simulations.

The development of Unmanned Aerial Vehicles (UAVs) and fifth-generation (5G) wireless technology provides more possibilities for wireless networks. The application of UAVs is gradually evolving from individual UAVs performing tasks to UAV swarm performing tasks in concert. A UAV swarm network is when many drones work cooperatively in a swarm mode to achieve a particular goal. Due to the UAV swarm's easy deployment, self-organization, self-management, and high flexibility, it can provide robust and efficient wireless communications in some unique scenarios, such as emergency communications, hotspot region coverage, sensor networks, and vehicular networks. Therefore, UAV networks have attracted more and more attention from commercial and military; however, many problems need to be resolved before UAV cellular communications become a reality. One of the most challenging core components is the routing protocol design in the UAV swarm network. Due to the high mobility of UAVs, the position of each UAV changes dynamically, so problems such as high latency, high packet loss rate, and even loss of connection arise when UAVs are far apart. These problems dramatically reduce the transmission rate and data integrity for traditional routing protocols based on path discovery. This thesis focuses on developing, emulating, and optimizing a flooding-based routing protocol for UAV swarm using Random Linear Network Coding (RLNC) to improve the latency and bit rate and solve the packet loss problem without routing information and network topology. RLNC can reduce the number of packets demand in some hops. Due to this feature of RLNC, when relay transmitter UAVs or the destination receiver UAV receive sufficient encoded packets from any transmitter UAVs, the raw data can be decoded. For those relay transmitter UAVs in the UAV swarm network that already received some encoded packets in previous hops but not enough to decode the raw data, only need to receive the rest of the different encoded packets needed for decoding. Thus, flooding-based routing protocol significantly improves transmission efficiency in the UAV swarm network. / Master of Science / People are used to using fiber, 4G, and Wi-Fi in the city, but numerous people still live in areas without Internet access. Moreover, in some particular scenarios like large-scale activities, remote areas, and military operations, when the cellular network cannot provide enough bandwidth or good signal, UAV wireless network would be helpful and provide stable Internet access. Successful UAV test flights can last for several weeks, and researchers' interest in high-altitude long-endurance (HALE) UAVs are booming. HALE UAVs will create Wi-Fi or other network signals for remote areas, including polar regions, which will allow millions of people to enter the information society and connect to the Internet. The development of UAV and 5G provides more possibilities for wireless networks. UAV applications have evolved from individual UAV performing tasks to UAV swarm performing tasks. A UAV swarm network is where multiple drones work in tandem to achieve a particular goal. It can provide robust and efficient wireless communications in unique scenarios. As a result, UAVs are receiving attention from both commercial and military. However, there are still many problems that need to be resolved before the actual use of UAVs. One of the biggest challenges is routing protocol which is how UAVs communicate with each other and select routes. As the location of UAVs is constantly changing, this leads to delays, data loss, or complete loss of connectivity. Ultimately these issues can lead to slow transmission speed and lack of data integrity for traditional routing protocols based on path discovery. This thesis focuses on developing, emulating, and optimizing a flooding-based routing protocol for the UAV swarm. Specifically, this protocol uses RLNC, which can reduce the number of packets demand in some hops so that the latency and transmission speed will be improved, and the data loss problem will also be solved. Due to this feature of RLNC, when any receiver receives enough encoded packets from any transmitter, the original data can be decoded. Some receivers that already received some encoded packets in the previous transmission only need to receive the rest of the different encoded packets needed for decoding. Therefore, flooding-based routing protocol significantly improves transmission efficiency for UAV swarm networks.

One of the major application areas for UAVs is the automated traversing and inspection of infrastructure. Much of this infrastructure is linear, such as roads, pipelines, rivers, and railroads. Rather than hard coding all of the GPS coordinates along these linear components into a flight plan for the UAV to follow, one could take advantage of computer vision and machine learning techniques to detect and travel along them. With regards to roads and railroads, two separate algorithms were developed to detect the angle and distance offset of the UAV from these linear infrastructure components to serve as control inputs for a flight controller. The road algorithm relied on applying a Gaussian SVM to segment road pixels from rural farmland using color plane and texture data. This resulted in a classification accuracy of 96.6% across a 62 image dataset collected at Kentland Farm. A trajectory can then be generated by fitting the classified road pixels to polynomial curves. These trajectories can even be used to take specific turns at intersections based on a user defined turn direction and have been proven through hardware-in-the-loop simulation to produce a mean cross track error of only one road width. The combined segmentation and trajectory algorithm was then implemented on a PC (i7-4720HQ 2.6 GHz, 16 GB RAM) at 6.25 Hz and a myRIO 1900 at 1.5 Hz proving its capability for real time UAV control. As for the railroad algorithm, template matching was first used to detect railroad patterns. Upon detection, a region of interest around the matched pattern was used to guide a custom edge detector and Hough transform to detect the straight lines on the rails. This algorithm has been shown to detect rails correctly, and thus the angle and distance offset error, on all images related to the railroad pattern template and can run at 10 Hz on the aforementioned PC. / Master of Science

The underground limestone mining industry is a small, but growing segment of the U.S. crushed stone industry. However, its fatality rate has been amongst the highest of the mining sector in recent years due to ground control issues related to ground collapses. It is therefore important to improve the engineering design, monitoring and visualization of ground control by utilizing new technologies that can help an underground limestone company maintain a safe and productive operation. Photogrammetry and laser scanning are remote sensing technologies that are useful tools for collecting three-dimensional spatial data with high levels of precision for many types of mining applications. Due to the reality of budget constraints for many underground stone mining operations, this research concentrates on photogrammetry as a more accessible technology for the average operation. Despite the challenging lighting conditions and size of underground limestone mines that has previous hindered photogrammetric surveys in these environments, over 13,000 photographic images were taken over a 3-year period in active mines to compile these models. This research summarizes that work and highlights the many applications of terrestrial close-range photogrammetry, including practical methodologies for implementing the techniques in working operations to better visualize hazards and pragmatic approaches for geotechnical analysis, improved engineering design and monitoring. / M.S. / The underground limestone mining industry is a small, but growing segment of the U.S. crushed stone industry. However, its fatality rate has been amongst the highest of the mining sector in recent years due to ground control issues related to ground collapses. It is therefore important to improve the engineering design, monitoring and visualization of ground control by utilizing new technologies that can help maintain safe and productive underground stone operations. Photogrammetry and laser scanning are remote sensing technologies that are useful tools for collecting three-dimensional spatial data with high levels of precision for many different mining applications. Due to the reality of budget constraints for many mining operations, this research concentrates on photogrammetry as a more accessible technology for the average operation, despite the challenging lighting conditions and expansive size of underground limestone mines that has previous hindered photogrammetric surveys in these environments. This research focuses on the applications of photogrammetry in underground stone mines and practical methodologies for implementing the techniques in working operations to better visualize hazards for improved engineering design and infrastructure management.

Unmanned aerial systems have been widely used for variety of civilian applications over the past few years. Some of these applications require accurate guidance and control. Consequently, Unmanned Aerial Vehicle (UAV) guidance and control attracted many researchers in both control theory and aerospace engineering. Flying wings, as a particular type of UAV, are considered to have one of the most efficient aerodynamic structures. It is however difficult to design robust controller for such systems. This is due to the fact that flying wings are highly sensitive to control inputs. The focus of this thesis is on modeling and control design for a UAV system. The platform understudy is a flying wing developed by SmartPlanes Co. located in Skellefteå, Sweden. This UAV is particularly used for topological mapping and aerial photography. The novel approach suggested in this thesis is to use two controllers in sequence. More precisely, Linear Quadratic Regulator (LQR) is suggested to provide robust stability, and Proportional, Integral, Derivative (PID) controller is suggested to provide reference signal regulation. The idea behind this approach is that with LQR in the loop, the system becomes more stable and less sensitive to control signals. Thus, PID controller has an easier task to do, and is only used to provide the required transient response. The closed-loop system containing the developed controller and a UAV non-linear dynamic model was simulated in Simulink. Simulated controller was then tested for stability and robustness with respect to some parametric uncertainty. Obtained results revealed that the LQR successfully managed to provide robust stability, and PID provided reference signal regulation.

A channel model is a mathematical or conceptual representation employed to describe the behavior and characteristics of a communication channel through which signal or data can be transferred from the transmitter (Tx) to the receiver (Rx) or between two transceivers. In wireless communication, the channel model represents the wireless medium with parameters like pathloss, impulse response, and multipath effects. A2A channel poses various challenges when UAVs operate at a higher altitude greater than 1000 ft (305 m). This thesis involves experiments having a range of altitudes from 20 m to 2 km and distances between two transceivers from 5 m to 3 km. This thesis aims to introduce A2A channel by considering and analyzing inherent channel characteristics such as pathloss in terms of line-of-sight (LOS) and non-line-of-sight (NLOS), multipath fading, delay spread, and power delay profile (PDP). These characteristics depend on frequency, altitude of transmitter (Tx) and receiver (Rx), distance between two transceivers, antenna properties, paths taken by the signals, and obstacles. Pathloss, RMS delay spread, and power delay profile have been discussed with the simulated graphs by varying the distances and altitudes. These channel characteristics have been analyzed for different conditions like varying building heights of the city, changing building material, and also changing both building height and material at the same time. Two empirical models, the EL model and the CI model, have been presented along with simulations. Simulation results using mmWave frequency have been shown. The simulations have been performed by Wireless Insite software.

In this thesis we explore an infrared light-based approach to the problem of pose estimation during aircraft landing in urban environments where GPS is unreliable or unavailable. We introduce a novel fiducial constellation composed of sparse infrared lights that incorporates projective invariant properties in its design to allow for robust recognition and association from arbitrary camera perspectives. We propose a pose estimation pipeline capable of producing high accuracy pose measurements at real-time rates from monocular infrared camera views of the fiducial constellation, and present as part of that pipeline a data association method that is able to robustly identify and associate individual constellation points in the presence of clutter and occlusions. We demonstrate the accuracy and efficiency of this pose estimation approach on real-world data obtained from multiple flight tests, and show that we can obtain decimeter level accuracy from distances of over 100 m from the constellation. To achieve greater robustness to the potentially large number of outlier infrared detections that can arise in urban environments, we also explore learning-based approaches to the outlier rejection and data association problems. By formulating the problem of camera image data association as a 2D point cloud analysis, we can apply deep learning methods designed for 3D point cloud segmentation to achieve robust, high-accuracy associations at constant real-time speeds on infrared images with high outlier-to-inlier ratios. We again demonstrate the efficiency of our learning-based approach on both synthetic and real-world data, and compare the results and limitations of this method to our first-principles-based data association approach.

The use of aerial platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, has emerged as a promising solution for providing reliable and cost-effective wireless communications. In particular, UAVs can be quickly and efficiently deployed to support cellular networks and enhance their quality-of-service (QoS) by establishing line-of-sight communication links. With their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. Remarkably, despite these inherent advantages of UAVbased communications, little work has analyzed the performance tradeoffs associated with using UAVs as aerial wireless platforms. The key goal of this dissertation is to develop the analytical foundations for deployment, performance analysis, and optimization of UAV-enabled wireless networks. This dissertation makes a number of fundamental contributions to various areas of UAV communications that include: 1) Efficient deployment of UAVs, 2) Performance evaluation and optimization, and 3) Design of new flying, three-dimensional (3D) wireless systems. For deployment, using tools from optimization theory, holistic frameworks are developed for the optimal 3D placement of UAV base stations in uplink and downlink scenarios. The results show that the proposed deployment approaches significantly improve the downlink coverage for ground users, and enable ultra-reliable and energy-efficient uplink communications in Internet of Things (IoT) applications. For performance optimization, a novel framework is developed for maximizing the performance of a UAV-based wireless system, in terms of data service, under UAVs’ flight time constraints. To this end, using the mathematical framework of optimal transport theory, the optimal cell associations, that lead to a maximum data service to ground users within the limited UAVs’ hover duration, are analytically derived. The results shed light on the tradeoff between hover time and quality-of-service in UAV-based wireless networks. For performance evaluation, this dissertation provides a comprehensive analysis on the performance of a UAV-based communication system in coexistence with a terrestrial network. In particular, a tractable analytical framework is proposed for analyzing the coverage and rate performance of a network with a UAV base station and deviceto-device (D2D) users. The results reveal the fundamental tradeoffs in such a UAV-D2D network that allow adopting appropriate system design parameters. Then, this dissertation sheds light on the design of three new drone-enabled wireless systems. First, a novel framework for effective use of cache-enabled UAVs in wireless networks is developed. The results demonstrate how the users’ quality of experience substantially improves by exploiting UAVs’ mobility and user-centric information. Second, a new framework is proposed for deploying and operating a drone-based antenna array system that delivers wireless service to ground users within a minimum time. The results show significant enhancement in QoS, spectral and energy efficiency while levering the proposed drone antenna array system. Finally, to effectively incorporate various use cases of drones ranging from aerial users to base stations, the new concept of a fully-fledged 3D cellular network is introduced. For this new type of 3D wireless network, a unified framework for deployment, network planning, and performance optimization is developed that yields a maximum coverage and minimum latency in the network. In a nutshell, the analytical foundations and frameworks presented in this dissertation provide key guidelines for effective design and operation of UAV-based wireless communication systems. / Ph. D. / Unmanned aerial vehicles (UAVs), commonly known as drones, have been the subject of concerted research over the past few years, owing to their autonomy, flexibility, and broad range of application domains. Indeed, UAVs have been considered as enablers of various applications that include military, surveillance and monitoring, telecommunications, delivery of medical supplies, and rescue operations. The unprecedented recent advances in drone technology has made it possible to widely deploy UAVs, such small aircrafts, balloons, and airships for wireless communication purposes. In particular, if properly deployed and operated, UAVs can provide reliable and cost-effective wireless communication solutions for a variety of real-world scenarios. On the one hand, drones can be used as aerial base stations that can deliver reliable, cost-effective, and on-demand wireless communications to desired areas. On the other hand, drones can function as aerial user equipments, known as cellular-connected UAVs, in coexistence with ground users. Despite such promising opportunities for drones, one must address a number of technical challenges in order to effectively use them for each specific networking application. For instance, while using drone-BS, the key design considerations include performance characterization, optimal 3D deployment of drones, wireless and computational resource allocation, flight time and trajectory optimization, and network planning. Meanwhile, in the drone-UE scenario, handover management, channel modeling, low-latency control, 3D localization, and interference management are among the main challenges. Therefore, this dissertation addresses the fundamental challenges in UAV-enabled wireless communications that allows providing broadband, wide-scale, cost-effective, and reliable wireless connectivity. To this end, various mathematical frameworks and efficient algorithms are proposed to design, optimize, deploy, and operate UAV-based communication systems. The results shows that, the proposed aerial communication system can deliver ultra-reliable, and cost-effective wireless services, thus providing ubiquitous high speed Internet connectivity for the whole world.

Im Bereich der Industrieisolierung ist das Handaufmaß gängige Praxis, um Rohrleitungsgeometrien zu erfassen und anschließend eine passende Dämmung zu fertigen. Laserbasierte Messverfahren (LiDAR) aus dem geodätischen Bereich konnten sich in der Industrieisolierung nicht durchsetzen. Trotz verschiedener Automationsansätze ist die Modellierung auf Basis der Punktwolken mit Standardsoftware aufwändig und nur geschulten Anwendern vorbehalten. Ergänzt wird diese Tatsache um die Problematik teurer Hardwarekosten. Photogrammetrische Ansätze bilden hierzu eine kostengünstige Alternative. Zugehörige Rekonstruktionsverfahren für Rohrleitungen existieren bereits, sind jedoch wenig automatisiert. In dieser Arbeit werden daher die Automationsmöglichkeiten für eine photogrammetrische Rekonstruktion von Rohrleitungen im industriellen Umfeld thematisiert. Der Forschungsbeitrag dieser Arbeit kann in drei Schwerpunkte gegliedert werden: a) Automationsansätze auf Basis von Objektwissen, b) Automationsansätze mittels Deep Learning und c) ein Ausgleichungsverfahren für Aufnahmen eines Unmanned Aerial System (UAS) zur simultanen Bildorientierung und Rekonstruktion von Rohren im Außenbereich in Anlehnung an eine Bündelblockausgleichung. Ausgehend von bekannten Rohrdurchmessern aus den Normungstexten wird zunächst für eine Kamerakonstellation mit bekannter Orientierung eine abgewandelte Art der Epipolargeometrie für Zylinder präsentiert. Diese erlaubt die Einschränkung des Suchbereichs in den Bildern und kann somit zur Detektion von Zylindern beitragen. Die Suche von Flanschen und Bögen erfolgt über bereits rekonstruierte Objekte. Eine Alternative dazu stellt die Detektion und Rekonstruktion der Rohrleitungsbauteile mithilfe von Deep Learning und Instanzsegmentierung in den Bildern dar. Hierzu werden verschiedene Modelle trainiert und Berechnungsverfahren implementiert. Die Optimierung der Rekonstruktion sowie die Integration des vorhandenen Objektwissens gelingt mithilfe eines Ausgleichungsansatzes. Die entwickelte Methodik wird schließlich für den Einsatz an UAS-Aufnahmen adaptiert. Das Ausgleichungsverfahren wird dazu um die Schätzung der Orientierungsparameter erweitert. Für eine automatisierte Berechnung können die mittels Deep Learning detektierten Rohre als Eingabedaten verwendet werden. Die einzelnen Forschungsbeiträge und neu entwickelten Ansätze werden sowohl an realen Daten zur Ermittlung der Robustheit, wie auch im Rahmen von Labortests zum Zwecke eine Genauigkeitsbeurteilung untersucht. Die Automation anhand von Objektwissen bietet sich vor allem für einfachere Aufnahmen an. Hinsichtlich der Genauigkeit sind die Ergebnisse nach der Ausgleichung mit einer manuellen Rekonstruktion beziehungsweise einer LiDAR-Messung vergleichbar. Bei komplexeren Bildern empfiehlt sich dagegen die Detektion und Rekonstruktion mittels Instanzsegmentierung. Rohre, Flansche und Bögen lassen sich mit einer Erkennungsrate von knapp 60 % gut detektieren und mit einer Genauigkeit von circa 5 mm berechnen. Gegenüber der Rekonstruktion durch Objektwissen ist die Berechnung außerdem unabhängig von anderen Objekten möglich. Eine Aufnahme mittels UAS ist besonders für größere Objekte geeignet. Die erweiterte Ausgleichung zur Bildorientierung liefert in Bezug auf die Orientierungsparameter etwas schlechtere Werte als eine Berechnung über Passpunkte. Nichtsdestotrotz werden, sofern keine groben Abweichungen, zum Beispiel aufgrund von mehrdeutigen Kanten in den Bildern vorliegen, Genauigkeiten von 5-10 mm erreicht. Die Erfüllung der Genauigkeitsanforderungen für den eingangs erwähnten Einsatz in der Industrieisolierung ist damit gegeben.:1 Einführung 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Stand der Forschung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Rohrleitungsvermessung mittels LiDAR . . . . . . . . . . . . . . . . . . . . . . 3 1.2.2 Rohrleitungsvermessung mittels Photogrammetrie . . . . . . . . . . . . . . . . 6 1.2.3 Rohrleitungsvermessung mittels kombinierter Verfahren . . . . . . . . . . . . . 9 1.2.4 Rohrleitungsvermessung mittels UAS . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.5 Deep Learning in der Photogrammetrie . . . . . . . . . . . . . . . . . . . . . . 10 1.2.6 Alternative photogrammetrische Orientierungsverfahren . . . . . . . . . . . . . 11 1.2.7 Fazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3 Zielsetzung und Forschungsfragen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4 Aufbau der Arbeit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Grundlagen 17 2.1 Bauteile im Anlagenbau . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.1 Rohre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.2 Flansche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.3 Formstücke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.1.4 Armaturen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.1.5 Druckbehälter und -kessel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2 Drehung mittels Quaternionen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 Ausgewählte photogrammetrische Grundlagen . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.1 Äußere Orientierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.2 Innere Orientierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.3 Abbildung eines Punktes ins Bild - Kollinearitätsgleichungen . . . . . . . . . . 26 2.3.4 Verfahren zur Lösung der Inneren und Äußeren Orientierung . . . . . . . . . . . 26 2.4 Abbildung von 3D-Volumenkörpern . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4.1 Projektion von Kegeln und Zylindern . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.2 Projektion von Tori . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.3 Sichtbarkeitsanalyse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5 Extraktion von Bildkanten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.6 Bildbasierte Zylinderrekonstruktion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3 Entwicklung eines Messsystems zur Rohrleitungsrekonstruktion 35 3.1 Anforderungen an das System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2 Aufbau und Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.3 Systemkalibrierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.4 Arbeitsablauf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.5 Rekonstruktionsverfahren für Rohre und Fittings . . . . . . . . . . . . . . . . . . . . . 39 3.5.1 Rohre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.5.2 Reduzierstücke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.5.3 Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.5.4 Flansche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.5.5 T-Stücke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.5.6 Armaturen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.5.7 Behälter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.6 Optimierung der Rekonstruktion mittels Ausgleichungsrechnung . . . . . . . . . . . . . 44 3.6.1 Formulierung der Beobachtungsgleichungen . . . . . . . . . . . . . . . . . . . 44 3.6.2 Definition der Unbekannten . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.6.3 Funktionale Modellierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.6.4 Prozessierung der Rohrleitung . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4 Automationsansätze durch Objektwissen 55 4.1 Rekonstruktion von Zylindern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.1.1 Vollautomatischer Ansatz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.1.2 Halbautomatischer Ansatz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2 Rekonstruktion von Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.3 Detektion von Flanschen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.4 Objektbasierte Transformation mehrerer Standpunkte . . . . . . . . . . . . . . . . . . . 62 4.4.1 Zuordnung korrespondierender Rohrbauteile . . . . . . . . . . . . . . . . . . . 63 4.4.2 Berechnung der Vortransformation . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4.3 Finale Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.5 Ergebnisse und Genauigkeitsbetrachtung . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.5.1 Zylinderrekonstruktion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.5.2 Bogenrekonstruktion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.5.3 Flanschrekonstruktion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.5.4 Automatische Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.6 Zwischenfazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5 Automation mittels Deep Learning und Instanzsegmentierung 75 5.1 Objekterkennung mittels Instanzsegmentierung . . . . . . . . . . . . . . . . . . . . . . 75 5.1.1 Übersicht über die verschiedenen Modelle . . . . . . . . . . . . . . . . . . . . . 75 5.1.2 Eigene Implementierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.2 Rekonstruktion von Rohren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.3 Rekonstruktion von Flanschen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.3.1 Strategie für das Labeln der Bilder . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.3.2 Prozessierung und Verschneidung der Masken . . . . . . . . . . . . . . . . . . . 84 5.4 Rekonstruktion von Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.5 Ergebnisse und Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.5.1 Erkennung von Rohren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.5.2 Rekonstruktion von Rohren . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.5.3 Erkennung von Flanschen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.4 Rekonstruktion von Flanschen . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.5 Erkennung von Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.5.6 Rekonstruktion von Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5.6 Zwischenfazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6 Objektaufnahme mittels UAS 107 6.1 Zylinderbasierte Bündelausgleichung zur Orientierung und Rekonstruktion . . . . . . . 109 6.1.1 Szenario 1: Bildaufnahme mit einer GNSS-Einzellösung und Passpunkten . . . . 110 6.1.2 Szenario 2: Aufnahme mit differentiellem GNSS und ohne Verknüpfungspunkte 114 6.2 Testmessungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.2.1 Feldtest in einer Industrieanlage . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.2.2 Genauigkeitsuntersuchung im Labor . . . . . . . . . . . . . . . . . . . . . . . . 119 6.3 Ergebnisse und Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6.3.1 Genauigkeit der Bildorientierung im Feldversuch . . . . . . . . . . . . . . . . . 120 6.3.2 Genauigkeit der Rekonstruktion im Feldversuch . . . . . . . . . . . . . . . . . . 122 6.3.3 Qualität der Näherungswerte für die Bildorientierung . . . . . . . . . . . . . . . 124 6.3.4 Genauigkeitsuntersuchung im Labor . . . . . . . . . . . . . . . . . . . . . . . . 125 6.4 Zwischenfazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 7 Zusammenfassung und Ausblick 131 7.1 Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 7.2 Potenziale für zukünftige Forschungsarbeiten . . . . . . . . . . . . . . . . . . . . . . . 134 7.3 Ausblick zur Integration der Daten in den Fertigungsprozess . . . . . . . . . . . . . . . 135 Literaturverzeichnis 137 / In the field of industrial insulation, manual measurement of pipe geometries is common practice in order to produce exactly fitting insulation. Laser-based measurement methods (LiDAR) from the geodetic sector have not been able to establish themselves in industrial insulation. Despite various automation approaches, modeling on the basis of point clouds with standard software is time-consuming and reserved for skilled users only. This fact is compounded by the problem of expensive hardware costs. Photogrammetric approaches are a cost-effective alternative. Reconstruction methods for pipelines already exist, but are not very automated. This thesis therefore focuses on the automation possibilities for photogrammetric reconstruction of pipelines in an industrial environment. The research contribution of this work can be divided into three main areas: a) automation approaches based on object knowledge, b) automation approaches using deep learning and c) an adjustment method for UAS imagery for simultaneous image orientation and reconstruction of pipes similar to a bundle block adjustment. Based on known pipe diameters from the standardization texts, a modified type of epipolar geometry for cylinders is first presented for a camera constellation with a known orientation. This allows the restriction of the search area in the images and can thus contribute to the detection of cylinders. Flanges and elbows are searched for using already reconstructed objects. Alternatively, pipeline components can be detected in the images using deep learning and instance segmentation. Various models were trained for this purpose. The 3D geometry can then be derived directly from the images. An adjustment approach is used to optimize the reconstruction and to integrate the existing object knowledge. Finally, the developed methodology is adapted for use on UAS imagery. The adjustment is extended to estimate the orientation parameters. For this purpose, the detected pipes can serve as input data to automate the calculation. The individual research contributions and newly developed approaches were examined both using real data to determine robustness and in laboratory tests to assess accuracy. Automation based on object knowledge is particularly suitable for simpler images. In terms of accuracy, the results after the adjustment are comparable with manual reconstruction or LiDAR measurements. For more complex images, however, detection and reconstruction using instance segmentation is recommended. Pipes, flanges and elbows in particular can be recognized well with a precision of almost 60 % and can be calculated with an accuracy of approx. 5 mm Compared to reconstruction using object knowledge, calculation is also possible independently of other objects. UAS imagery is particularly suitable for larger objects. The extended adjustment for image orientation provides slightly worse values for the orientation parameters than a calculation using ground control points. Nevertheless, an accuracy of 5-10 mm can be achieved, provided there are no gross deviations, for example due to ambiguous edges in the images. This therefore meets the accuracy requirements for the aforementioned use in industrial insulation.:1 Einführung 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Stand der Forschung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Rohrleitungsvermessung mittels LiDAR . . . . . . . . . . . . . . . . . . . . . . 3 1.2.2 Rohrleitungsvermessung mittels Photogrammetrie . . . . . . . . . . . . . . . . 6 1.2.3 Rohrleitungsvermessung mittels kombinierter Verfahren . . . . . . . . . . . . . 9 1.2.4 Rohrleitungsvermessung mittels UAS . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.5 Deep Learning in der Photogrammetrie . . . . . . . . . . . . . . . . . . . . . . 10 1.2.6 Alternative photogrammetrische Orientierungsverfahren . . . . . . . . . . . . . 11 1.2.7 Fazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.3 Zielsetzung und Forschungsfragen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.4 Aufbau der Arbeit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Grundlagen 17 2.1 Bauteile im Anlagenbau . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.1 Rohre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.2 Flansche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.3 Formstücke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.1.4 Armaturen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.1.5 Druckbehälter und -kessel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2 Drehung mittels Quaternionen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 Ausgewählte photogrammetrische Grundlagen . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.1 Äußere Orientierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.2 Innere Orientierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.3 Abbildung eines Punktes ins Bild - Kollinearitätsgleichungen . . . . . . . . . . 26 2.3.4 Verfahren zur Lösung der Inneren und Äußeren Orientierung . . . . . . . . . . . 26 2.4 Abbildung von 3D-Volumenkörpern . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4.1 Projektion von Kegeln und Zylindern . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.2 Projektion von Tori . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.3 Sichtbarkeitsanalyse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5 Extraktion von Bildkanten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.6 Bildbasierte Zylinderrekonstruktion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3 Entwicklung eines Messsystems zur Rohrleitungsrekonstruktion 35 3.1 Anforderungen an das System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2 Aufbau und Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.3 Systemkalibrierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.4 Arbeitsablauf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.5 Rekonstruktionsverfahren für Rohre und Fittings . . . . . . . . . . . . . . . . . . . . . 39 3.5.1 Rohre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.5.2 Reduzierstücke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.5.3 Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.5.4 Flansche . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.5.5 T-Stücke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.5.6 Armaturen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.5.7 Behälter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.6 Optimierung der Rekonstruktion mittels Ausgleichungsrechnung . . . . . . . . . . . . . 44 3.6.1 Formulierung der Beobachtungsgleichungen . . . . . . . . . . . . . . . . . . . 44 3.6.2 Definition der Unbekannten . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.6.3 Funktionale Modellierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.6.4 Prozessierung der Rohrleitung . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4 Automationsansätze durch Objektwissen 55 4.1 Rekonstruktion von Zylindern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.1.1 Vollautomatischer Ansatz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.1.2 Halbautomatischer Ansatz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.2 Rekonstruktion von Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.3 Detektion von Flanschen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.4 Objektbasierte Transformation mehrerer Standpunkte . . . . . . . . . . . . . . . . . . . 62 4.4.1 Zuordnung korrespondierender Rohrbauteile . . . . . . . . . . . . . . . . . . . 63 4.4.2 Berechnung der Vortransformation . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4.3 Finale Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.5 Ergebnisse und Genauigkeitsbetrachtung . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.5.1 Zylinderrekonstruktion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.5.2 Bogenrekonstruktion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.5.3 Flanschrekonstruktion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.5.4 Automatische Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.6 Zwischenfazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5 Automation mittels Deep Learning und Instanzsegmentierung 75 5.1 Objekterkennung mittels Instanzsegmentierung . . . . . . . . . . . . . . . . . . . . . . 75 5.1.1 Übersicht über die verschiedenen Modelle . . . . . . . . . . . . . . . . . . . . . 75 5.1.2 Eigene Implementierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.2 Rekonstruktion von Rohren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.3 Rekonstruktion von Flanschen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.3.1 Strategie für das Labeln der Bilder . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.3.2 Prozessierung und Verschneidung der Masken . . . . . . . . . . . . . . . . . . . 84 5.4 Rekonstruktion von Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.5 Ergebnisse und Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.5.1 Erkennung von Rohren . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.5.2 Rekonstruktion von Rohren . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.5.3 Erkennung von Flanschen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.4 Rekonstruktion von Flanschen . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.5 Erkennung von Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.5.6 Rekonstruktion von Bögen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5.6 Zwischenfazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6 Objektaufnahme mittels UAS 107 6.1 Zylinderbasierte Bündelausgleichung zur Orientierung und Rekonstruktion . . . . . . . 109 6.1.1 Szenario 1: Bildaufnahme mit einer GNSS-Einzellösung und Passpunkten . . . . 110 6.1.2 Szenario 2: Aufnahme mit differentiellem GNSS und ohne Verknüpfungspunkte 114 6.2 Testmessungen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.2.1 Feldtest in einer Industrieanlage . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.2.2 Genauigkeitsuntersuchung im Labor . . . . . . . . . . . . . . . . . . . . . . . . 119 6.3 Ergebnisse und Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6.3.1 Genauigkeit der Bildorientierung im Feldversuch . . . . . . . . . . . . . . . . . 120 6.3.2 Genauigkeit der Rekonstruktion im Feldversuch . . . . . . . . . . . . . . . . . . 122 6.3.3 Qualität der Näherungswerte für die Bildorientierung . . . . . . . . . . . . . . . 124 6.3.4 Genauigkeitsuntersuchung im Labor . . . . . . . . . . . . . . . . . . . . . . . . 125 6.4 Zwischenfazit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 7 Zusammenfassung und Ausblick 131 7.1 Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 7.2 Potenziale für zukünftige Forschungsarbeiten . . . . . . . . . . . . . . . . . . . . . . . 134 7.3 Ausblick zur Integration der Daten in den Fertigungsprozess . . . . . . . . . . . . . . . 135 Literaturverzeichnis 137

Approved for public release, distribution is unlimited / This thesis is part of an ongoing research conducted at the Naval Postgraduate School to achieve the autonomous shipboard landing of Unmanned Aerial Vehicles (UAV). Two main problems are addressed in this thesis. The first is to establish communication between the UAV's ground station and the Autonomous Landing Flight Control Computer effectively. The second addresses the design and implementation of an autonomous landing controller using classical control techniques. Device drivers for the sensors and the communications protocol were developed in ANSI C. The overall system was implemented in a PC104 computer running a real-time operating system developed by The Mathworks, Inc. Computer and hardware in the loop (HIL) simulation, as well as ground test results show the feasibility of the algorithm proposed here. Flight tests are scheduled to be performed in the near future. / Lieutenant Junior Grade, Mexican Navy