Real Time Kinematic paikannuksen avustaminen 3D pintamallilla sekä taivaskameralla

This study presents an approach to obstruction masking for GNSS Real Time Kinematic (RTK) positioning using information from 3D models and sky-view images. The signals propagating from obstructed satellites to the receiver are one of the largest error sources in RTK positioning. The effects of signal masking were assessed by conducting a test campaign in varying environments. The data recorded in the test campaign was used to compare the results of aided RTK solutions to unaided ones. From the results of the tests it was found that combining image and 3D model masking is the optimal mask generation method providing the most significant improvement in the RTK positioning performance. The effects of signal masking were found to depend on the used processing software, with the largest performance improvement in terms of positioning accuracy and fix availability obtained when using open-source RTKLIB processing software and in lesser extent when using commercial Septentrio PPSDK RTK processing software. The commercial feasibility of such system consequently depends on the used RTK processing algorithms.Tässä tutkimuksessa esitellään menetelmä esteiden peittämien satelliittien suodattamiseen GNSS Real Time Kinematic (RTK) -paikannuksesta käyttämällä hyödyksi 3D-malleja sekä taivaasta otettavia valokuvia. Esteiden peittämien satelliittien signaalit saattavat kulkeutua satelliiteista vastaanottimeen heijastumalla vastaanotinta ympäröivistä pinnoista. Satelliittien suodattamisen vaikutuksia paikannuksen laadulle selvitettiin toteuttamalla testimittauskampanja vaihtelevissa satelliittipaikannusympäristöissä. Testeissä kerättyä dataa analysoitiin vertailemalla ilman sateliittisuodatusta, sekä satelliittisuodatuksen kanssa prosessoituja paikannusratkaisuja. Analyysin tulosten perusteella havaittiin, että optimaalisin keino hyödyntää satelliittipaikannusympäristöstä testijärjestelmällä kerättyä tietoa oli yhdistää sekä 3D-mallilta, että kuvista saatu tieto. Kuvien sekä 3D-mallien keräämän tiedon yhdistäminen satelliittisuodatukseen tuotti merkittävimmän parannuksen RTK-paikannuksen laatuun. Suodatusmenetelmän vaikutusten havaittiin yhtäältä riippuvan myös RTK-prosessointiin käytetystä ohjelmistosta. Avoimen lähdekoodin RTKLIB ohjelmistolla prosessoitaessa RTK-paikannuksen tarkkuus ja saatavuus paranivat merkittävästi, kun taas kaupallisella Septentrio PPSDK -ohjelmistolla prosessoitaessa vaikutukset paikannuksen suorituskykyyn olivat vähäisempiä. Kaupallisen satelliittisuodatusjärjestelmän kannattavuus riippuukin RTK prosessointiin käytetyistä algoritmeista

Navigation Recommender:Real-Time iGNSS QoS Prediction for Navigation Services

Global Navigation Satellite Systems (GNSSs), especially Global Positioning System (GPS), have become commonplace in mobile devices and are the most preferred geo-positioning sensors for many location-based applications. Besides GPS, other GNSSs under development or deployment are GLONASS, Galileo, and Compass. These four GNSSs are planned to be integrated in the near future. It is anticipated that integrated GNSSs (iGNSSs) will improve the overall satellite-based geo-positioning performance. However, one major shortcoming of any GNSS and iGNSSs is Quality of Service (QoS) degradation due to signal blockage and attenuation by the surrounding environments, particularly in obstructed areas. GNSS QoS uncertainty is the root cause of positioning ambiguity, poor localization performance, application freeze, and incorrect guidance in navigation applications. In this research, a methodology, called iGNSS QoS prediction, that can provide GNSS QoS on desired and prospective routes is developed. Six iGNSS QoS parameters suitable for navigation are defined: visibility, availability, accuracy, continuity, reliability, and flexibility. The iGNSS QoS prediction methodology, which includes a set of algorithms, encompasses four modules: segment sampling, point-based iGNSS QoS prediction, tracking-based iGNSS QoS prediction, and iGNSS QoS segmentation. Given that iGNSS QoS prediction is data- and compute-intensive and navigation applications require real-time solutions, an efficient satellite selection algorithm is developed and distributed computing platforms, mainly grids and clouds, for achieving real-time performance are explored. The proposed methodology is unique in several respects: it specifically addresses the iGNSS positioning requirements of navigation systems/services; it provides a new means for route choices and routing in navigation systems/services; it is suitable for different modes of travel such as driving and walking; it takes high-resolution 3D data into account for GNSS positioning; and it is based on efficient algorithms and can utilize high-performance and scalable computing platforms such as grids and clouds to provide real-time solutions. A number of experiments were conducted to evaluate the developed methodology and the algorithms using real field test data (GPS coordinates). The experimental results show that the methodology can predict iGNSS QoS in various areas, especially in problematic areas

Closely-coupled integration of Locata and GPS for engineering applications

GPS has become an almost indispensable part of our infrastructure and modern life. Yet because its accuracy, reliability, and integrity depend on the number and geometric distribution of the visible satellites, it is not reliable enough for the safety of life, environmental or economically critical applications. Traditionally, this has been addressed by augmentation from dedicated support systems, or integration with other sensors. However, from an engineering perspective only expensive inertial systems or pseudolites offer the accuracy required. In the case of pseudolites, the equivalent of ground based satellites, geometry constraints, fading multipath, imprecise clocks, the near-far effect, tropospheric delay and legislative obstructions make them difficult to implement. This thesis takes a step forward, by proposing a loosely coupled integration with Locata, a novel, terrestrial positioning technology, based on the pseudolite concept. It avoids the above pitfalls by utilising frequency and spatially separated antennas and a license-free frequency band, though this comes at the cost of in-bound interference. Its ability to provide stand-alone position and network synchronisation at nanosecond level is used commercially in open-cast mining and in military aviation. Discussion of Locata and GPS technology has identified their shortcomings and main limiting factors as well as the advantages of the proposed integration. During the course of this research, tropospheric delay, planar solution and known point initialisation ambiguity resolution methods have been identified as the main limiting factors for Locata. These are analysed in various static and kinematic scenarios. Discussion also includes ambiguity resolution, noise and interference detection and system performance in indoor and outdoor scenarios. The proposed navigation engine uses a closely coupled integration at the measurement level and LAMBDA as the ambiguity resolution method for Locata and GPS. A combined solution is demonstrated to offer a geometrical improvement, especially in the respect of height determination, with centimetre to decimetre accuracy and a minimum requirement of two signals from any component. This study identifies that proper separation and de-correlation of Locata and GPS ambiguities and better tropospheric models are essential to reach centimetre level accuracy. The thesis concludes with examples of system implementation including: seamless navigation, city-wide network deployment, urban canyons, a long term-monitoring scenario and indoor positioning. This demonstrates how the proposed navigation engine can be an advantage in areas such as: civil engineering, GIS, mobile mapping, deformation, machine navigation and control

Closely-coupled integration of Locata and GPS for engineering applications

GPS has become an almost indispensable part of our infrastructure and modern life. Yet because its accuracy, reliability, and integrity depend on the number and geometric distribution of the visible satellites, it is not reliable enough for the safety of life, environmental or economically critical applications. Traditionally, this has been addressed by augmentation from dedicated support systems, or integration with other sensors. However, from an engineering perspective only expensive inertial systems or pseudolites offer the accuracy required. In the case of pseudolites, the equivalent of ground based satellites, geometry constraints, fading multipath, imprecise clocks, the near-far effect, tropospheric delay and legislative obstructions make them difficult to implement. This thesis takes a step forward, by proposing a loosely coupled integration with Locata, a novel, terrestrial positioning technology, based on the pseudolite concept. It avoids the above pitfalls by utilising frequency and spatially separated antennas and a license-free frequency band, though this comes at the cost of in-bound interference. Its ability to provide stand-alone position and network synchronisation at nanosecond level is used commercially in open-cast mining and in military aviation. Discussion of Locata and GPS technology has identified their shortcomings and main limiting factors as well as the advantages of the proposed integration. During the course of this research, tropospheric delay, planar solution and known point initialisation ambiguity resolution methods have been identified as the main limiting factors for Locata. These are analysed in various static and kinematic scenarios. Discussion also includes ambiguity resolution, noise and interference detection and system performance in indoor and outdoor scenarios. The proposed navigation engine uses a closely coupled integration at the measurement level and LAMBDA as the ambiguity resolution method for Locata and GPS. A combined solution is demonstrated to offer a geometrical improvement, especially in the respect of height determination, with centimetre to decimetre accuracy and a minimum requirement of two signals from any component. This study identifies that proper separation and de-correlation of Locata and GPS ambiguities and better tropospheric models are essential to reach centimetre level accuracy. The thesis concludes with examples of system implementation including: seamless navigation, city-wide network deployment, urban canyons, a long term-monitoring scenario and indoor positioning. This demonstrates how the proposed navigation engine can be an advantage in areas such as: civil engineering, GIS, mobile mapping, deformation, machine navigation and control

Algorithms for map-aided autonomous indoor pedestrian positioning and navigation

The personal positioning and navigation became a very challenging topic in our dynamic time. The urban canyons and particularly indoors represent the most difficult areas for personal navigation problematic. Problems like disturbed satellite signals make the positioning impossible indoors. Recently developed systems for indoor positioning do not assure the necessary positioning accuracy or are very expensive. Our concept stands for a fully autonomous positioning and navigation process. That is, a method that does not rely on the reception of external information, like satellite or terrestrial signals. Therefore, this research is based on the use of inertial measurements of the human walk and the map database which contains the graphic representation of the elements of the building, created by applying the link-node model. Using this reduced set of information the task is to develop methodology, based on the interaction of the data from both sources, to assure reliable positioning and navigation process. This research is divided in three parts. The first part consists in the development of a methodology for initial localization of the person indoors. The problem to solve is to localize the person in the building. Consider a person equipped with a system which contains set of inertial sensors and map database of the building. Speed, turn rate and barometric altitude are measured and time-stamped on each step of the person. A pre-processing phase uses these raw measurements in order to construct a polyline, thus representing user's trajectory. In the localization approach central place takes the association of the user's trajectory with the graph representation of the building, process known as map-matching. The solution is based on statistical method where the determination of the user's position is entirely represented by its probability density function (PDF) in the frame of Bayesian inference. Initial localization determines the edge of the graph occupied by the person. The second part aims at continuous localization, where user's position is estimated on every step. Besides the application of the classical map-matching techniques, two new methods are developed. Both rely on the similarity of the geometry of the trajectory and the elements of the graph. The first is based on the Bayesian inference, where the estimation is computed considering the walked distance and azimuth. The second method represents a new application of the Fréchet distance as degree of similarity between two polylines. The third part is pointed at the pedestrian guidance. Once the user's position is known it is easy to compute the path to his destination and to give him directions. The problem is to assure continuance of the process of navigation in the case when the person has lost his path. In that case the solution consists in either giving instructions to the user to go back on the path or computation of a new path from the actual position of the user to his destination. Based on that methodology, algorithms for initial localization, continuous localization, and guidance were created. Numerous tests with the participation of several persons have been provided in order to validate the algorithms and to show their performance, robustness and limits

Adaptive, reliable, and accurate positioning model for location-based services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a new strategy in achieving highly reliable and accurate position solutions fulfilling the requirements of Location-Based Services (LBS) pedestrians’ applications. The new strategy is divided into two main parts. The first part integrates the available positioning technology within the surrounding LBS application context by introducing an adaptive LBS framework. The context can be described as a group of factors affecting the application behaviour; this includes environmental states, available resources and user preferences. The proposed adaptive framework consists of several stages, such as defining the contextual factors that have a direct effect on the positioning performance, identifying preliminary positioning performance requirements associated with different LBS application groups, and introducing an intelligent positioning services selection function. The second part of this work involves the design and development of a novel positioning model that is responsible for delivering highly reliable, accurate and precise position solutions to LBS users. This new model is based on the single frequency GPS Standard Positioning Service (SPS). Additionally, it is incorporated within the adaptive LBS framework while providing the position solutions, in which all identified contextual factors and application requirements are accounted. The positioning model operates over a client-server architecture including two main components, described as the Localisation Server (LS) and the Mobile Unit (MU). Hybrid functional approaches were developed at both components consisting of several processing procedures allowing the positioning model to operate in two position determination modes. Stand-alone mode is used if enough navigation information was available at the MU using its local positioning device (GPS/EGNOS receiver). Otherwise, server-based mode is utilised, in which the LS intervenes and starts providing the required position solutions. At the LS, multiple sources of GPS augmentation services were received using the Internet as the sole augmentation data transportation medium. The augmentation data was then processed and integrated for the purpose of guaranteeing the availability of valid and reliable information required for the provision of accurate and precise position solutions. Two main advanced position computation methods were developed at the LS, described as coordinate domain and raw domain. The positioning model was experimentally evaluated. According to the reported results, the LS through the developed position computation methods, was able to provide position samples with an accuracy of less than 2 meters, with high precision at 95% confidence level; this was achieved in urban, rural, and open space (clear satellite view) navigation environments. Additionally, the integrity of the position solutions was guaranteed in such environments during more than 90% of the navigation time, taking into consideration the identified integrity thresholds (Horizontal Alert Limits (HAL)=11 m). This positioning performance has outperformed the existing GPS/EGNOS service which was implemented at the MU in all scenarios and environments. In addition, utilising a simulation evaluation facility the developed positioning model performance was quantified with reference to a hybrid positioning service that will be offered by future Galileo Open Service (OS) along with GPS/EGNOS. Using the statistical t-test, it was concluded that there is no significant difference in terms of the position samples’ accuracy achieved from the developed positioning model and the hybrid system at a particular navigation environment described as rural area. The p-value was 0.08 and the level of significance used was 0.05. However, a significant difference in terms of the service integrity for the advantage of the hybrid system was experienced in all remaining scenarios and environments more especially the urban areas due to surrounding obstacles and conditions

Localization and Mapping for Autonomous Driving: Fault Detection and Reliability Analysis

Autonomous driving has advanced rapidly during the past decades and has expanded its application for multiple fields, both indoor and outdoor. One of the significant issues associated with a highly automated vehicle (HAV) is how to increase the safety level. A key requirement to ensure the safety of automated driving is the ability of reliable localization and navigation, with which intelligent vehicle/robot systems could successfully make reliable decisions for the driving path or react to the sudden events occurring within the path. A map with rich environment information is essential to support autonomous driving system to meet these high requirements. Therefore, multi-sensor-based localization and mapping methods are studied in this Thesis. Although some studies have been conducted in this area, a full quality control scheme to guarantee the reliability and to detect outliers in localization and mapping systems is still lacking. The quality of the integration system has not been sufficiently evaluated. In this research, an extended Kalman filter and smoother based quality control (EKF/KS QC) scheme is investigated and has been successfully applied for different localization and mapping scenarios. An EKF/KS QC toolbox is developed in MATLAB, which can be easily embedded and applied into different localization and mapping scenarios. The major contributions of this research are: a) The equivalence between least squares and smoothing is discussed, and an extended Kalman filter-smoother quality control method is developed according to this equivalence, which can not only be used to deal with system model outlier with detection, and identification, can also be used to analyse, control and improve the system quality. Relevant mathematical models of this quality control method have been developed to deal with issues such as singular measurement covariance matrices, and numerical instability of smoothing. b) Quality control analysis is conducted for different positioning system, including Global Navigation Satellite System (GNSS) multi constellation integration for both Real Time Kinematic (RTK) and Post Processing Kinematic (PPK), and the integration of GNSS and Inertial Navigation System (INS). The results indicate PPK method can provide more reliable positioning results than RTK. With the proposed quality control method, the influence of the detected outlier can be mitigated by directly correcting the input measurement with the estimated outlier value, or by adapting the final estimation results with the estimated outlier’s influence value. c) Mathematical modelling and quality control aspects for online simultaneous localization and mapping (SLAM) are examined. A smoother based offline SLAM method is investigated with quality control. Both outdoor and indoor datasets have been tested with these SLAM methods. Geometry analysis for the SLAM system has been done according to the quality control results. The system reliability analysis is essential for the SLAM designer as it can be conducted at the early stage without real-world measurement. d) A least squares based localization method is proposed that treats the High-Definition (HD) map as a sensor source. This map-based sensor information is integrated with other perception sensors, which significantly improves localization efficiency and accuracy. Geometry analysis is undertaken with the quality measures to analyse the influence of the geometry upon the estimation solution and the system quality, which can be hints for future design of the localization system. e) A GNSS/INS aided LiDAR mapping and localization procedure is developed. A high-density map is generated offline, then, LiDAR-based localization can be undertaken online with this pre-generated map. Quality control is conducted for this system. The results demonstrate that the LiDAR based localization within map can effectively improve the accuracy and reliability compared to the GNSS/INS only system, especially during the period that GNSS signal is lost

On the use of smartphones as novel photogrammetric water gauging instruments: Developing tools for crowdsourcing water levels

The term global climate change is omnipresent since the beginning of the last decade. Changes in the global climate are associated with an increase in heavy rainfalls that can cause nearly unpredictable flash floods. Consequently, spatio-temporally high-resolution monitoring of rivers becomes increasingly important. Water gauging stations continuously and precisely measure water levels. However, they are rather expensive in purchase and maintenance and are preferably installed at water bodies relevant for water management. Small-scale catchments remain often ungauged. In order to increase the data density of hydrometric monitoring networks and thus to improve the prediction quality of flood events, new, flexible and cost-effective water level measurement technologies are required. They should be oriented towards the accuracy requirements of conventional measurement systems and facilitate the observation of water levels at virtually any time, even at the smallest rivers. A possible solution is the development of a photogrammetric smartphone application (app) for crowdsourcing water levels, which merely requires voluntary users to take pictures of a river section to determine the water level. Today’s smartphones integrate high-resolution cameras, a variety of sensors, powerful processors, and mass storage. However, they are designed for the mass market and use low-cost hardware that cannot comply with the quality of geodetic measurement technology. In order to investigate the potential for mobile measurement applications, research was conducted on the smartphone as a photogrammetric measurement instrument as part of the doctoral project. The studies deal with the geometric stability of smartphone cameras regarding device-internal temperature changes and with the accuracy potential of rotation parameters measured with smartphone sensors. The results show a high, temperature-related variability of the interior orientation parameters, which is why the calibration of the camera should be carried out during the immediate measurement. The results of the sensor investigations show considerable inaccuracies when measuring rotation parameters, especially the compass angle (errors up to 90° were observed). The same applies to position parameters measured by global navigation satellite system (GNSS) receivers built into smartphones. According to the literature, positional accuracies of about 5 m are possible in best conditions. Otherwise, errors of several 10 m are to be expected. As a result, direct georeferencing of image measurements using current smartphone technology should be discouraged. In consideration of the results, the water gauging app Open Water Levels (OWL) was developed, whose methodological development and implementation constituted the core of the thesis project. OWL enables the flexible measurement of water levels via crowdsourcing without requiring additional equipment or being limited to specific river sections. Data acquisition and processing take place directly in the field, so that the water level information is immediately available. In practice, the user captures a short time-lapse sequence of a river bank with OWL, which is used to calculate a spatio-temporal texture that enables the detection of the water line. In order to translate the image measurement into 3D object space, a synthetic, photo-realistic image of the situation is created from existing 3D data of the river section to be investigated. Necessary approximations of the image orientation parameters are measured by smartphone sensors and GNSS. The assignment of camera image and synthetic image allows for the determination of the interior and exterior orientation parameters by means of space resection and finally the transfer of the image-measured 2D water line into the 3D object space to derive the prevalent water level in the reference system of the 3D data. In comparison with conventionally measured water levels, OWL reveals an accuracy potential of 2 cm on average, provided that synthetic image and camera image exhibit consistent image contents and that the water line can be reliably detected. In the present dissertation, related geometric and radiometric problems are comprehensively discussed. Furthermore, possible solutions, based on advancing developments in smartphone technology and image processing as well as the increasing availability of 3D reference data, are presented in the synthesis of the work. The app Open Water Levels, which is currently available as a beta version and has been tested on selected devices, provides a basis, which, with continuous further development, aims to achieve a final release for crowdsourcing water levels towards the establishment of new and the expansion of existing monitoring networks.Der Begriff des globalen Klimawandels ist seit Beginn des letzten Jahrzehnts allgegenwärtig. Die Veränderung des Weltklimas ist mit einer Zunahme von Starkregenereignissen verbunden, die nahezu unvorhersehbare Sturzfluten verursachen können. Folglich gewinnt die raumzeitlich hochaufgelöste Überwachung von Fließgewässern zunehmend an Bedeutung. Pegelmessstationen erfassen kontinuierlich und präzise Wasserstände, sind jedoch in Anschaffung und Wartung sehr teuer und werden vorzugsweise an wasserwirtschaftlich-relevanten Gewässern installiert. Kleinere Gewässer bleiben häufig unbeobachtet. Um die Datendichte hydrometrischer Messnetze zu erhöhen und somit die Vorhersagequalität von Hochwasserereignissen zu verbessern, sind neue, kostengünstige und flexibel einsetzbare Wasserstandsmesstechnologien erforderlich. Diese sollten sich an den Genauigkeitsanforderungen konventioneller Messsysteme orientieren und die Beobachtung von Wasserständen zu praktisch jedem Zeitpunkt, selbst an den kleinsten Flüssen, ermöglichen. Ein Lösungsvorschlag ist die Entwicklung einer photogrammetrischen Smartphone-Anwendung (App) zum Crowdsourcing von Wasserständen mit welcher freiwillige Nutzer lediglich Bilder eines Flussabschnitts aufnehmen müssen, um daraus den Wasserstand zu bestimmen. Heutige Smartphones integrieren hochauflösende Kameras, eine Vielzahl von Sensoren, leistungsfähige Prozessoren und Massenspeicher. Sie sind jedoch für den Massenmarkt konzipiert und verwenden kostengünstige Hardware, die nicht der Qualität geodätischer Messtechnik entsprechen kann. Um das Einsatzpotential in mobilen Messanwendungen zu eruieren, sind Untersuchungen zum Smartphone als photogrammetrisches Messinstrument im Rahmen des Promotionsprojekts durchgeführt worden. Die Studien befassen sich mit der geometrischen Stabilität von Smartphone-Kameras bezüglich geräteinterner Temperaturänderungen und mit dem Genauigkeitspotential von mit Smartphone-Sensoren gemessenen Rotationsparametern. Die Ergebnisse zeigen eine starke, temperaturbedingte Variabilität der inneren Orientierungsparameter, weshalb die Kalibrierung der Kamera zum unmittelbaren Messzeitpunkt erfolgen sollte. Die Ergebnisse der Sensoruntersuchungen zeigen große Ungenauigkeiten bei der Messung der Rotationsparameter, insbesondere des Kompasswinkels (Fehler von bis zu 90° festgestellt). Selbiges gilt auch für Positionsparameter, gemessen durch in Smartphones eingebaute Empfänger für Signale globaler Navigationssatellitensysteme (GNSS). Wie aus der Literatur zu entnehmen ist, lassen sich unter besten Bedingungen Lagegenauigkeiten von etwa 5 m erreichen. Abseits davon sind Fehler von mehreren 10 m zu erwarten. Infolgedessen ist von einer direkten Georeferenzierung von Bildmessungen mittels aktueller Smartphone-Technologie abzusehen. Unter Berücksichtigung der gewonnenen Erkenntnisse wurde die Pegel-App Open Water Levels (OWL) entwickelt, deren methodische Entwicklung und Implementierung den Kern der Arbeit bildete. OWL ermöglicht die flexible Messung von Wasserständen via Crowdsourcing, ohne dabei zusätzliche Ausrüstung zu verlangen oder auf spezifische Flussabschnitte beschränkt zu sein. Datenaufnahme und Verarbeitung erfolgen direkt im Feld, so dass die Pegelinformationen sofort verfügbar sind. Praktisch nimmt der Anwender mit OWL eine kurze Zeitraffersequenz eines Flussufers auf, die zur Berechnung einer Raum-Zeit-Textur dient und die Erkennung der Wasserlinie ermöglicht. Zur Übersetzung der Bildmessung in den 3D-Objektraum wird aus vorhandenen 3D-Daten des zu untersuchenden Flussabschnittes ein synthetisches, photorealistisches Abbild der Aufnahmesituation erstellt. Erforderliche Näherungen der Bildorientierungsparameter werden von Smartphone-Sensoren und GNSS gemessen. Die Zuordnung von Kamerabild und synthetischem Bild erlaubt die Bestimmung der inneren und äußeren Orientierungsparameter mittels räumlichen Rückwärtsschnitt. Nach Rekonstruktion der Aufnahmesituation lässt sich die im Bild gemessene 2D-Wasserlinie in den 3D-Objektraum projizieren und der vorherrschende Wasserstand im Referenzsystem der 3D-Daten ableiten. Im Soll-Ist-Vergleich mit konventionell gemessenen Pegeldaten zeigt OWL ein erreichbares Genauigkeitspotential von durchschnittlich 2 cm, insofern synthetisches und reales Kamerabild einen möglichst konsistenten Bildinhalt aufweisen und die Wasserlinie zuverlässig detektiert werden kann. In der vorliegenden Dissertation werden damit verbundene geometrische und radiometrische Probleme ausführlich diskutiert sowie Lösungsansätze, auf der Basis fortschreitender Entwicklungen von Smartphone-Technologie und Bildverarbeitung sowie der zunehmenden Verfügbarkeit von 3D-Referenzdaten, in der Synthese der Arbeit vorgestellt. Mit der gegenwärtig als Betaversion vorliegenden und auf ausgewählten Geräten getesteten App Open Water Levels wurde eine Basis geschaffen, die mit kontinuierlicher Weiterentwicklung eine finale Freigabe für das Crowdsourcing von Wasserständen und damit den Aufbau neuer und die Erweiterung bestehender Monitoring-Netzwerke anstrebt

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%