Over the last years researchers proposed numerous indoor localisation and navigation systems. However, solutions that use WiFi or Radio Frequency Identification require infrastructure to be deployed in the navigation area and infrastructureless techniques, e.g. the ones based on mobile cell ID or dead reckoning suffer from large accuracy errors. In this Thesis, we present a novel approach of infrastructure-less indoor navigation system based on computer vision Structure from Motion techniques. We implemented a prototype localisation and navigation system which can build a navigation map using area photos as input and accurately locate a user in the map. In our client-server architecture based system, a client is a mobile application, which allows a user to locate her or his position by simply taking a photo. The server handles map creation, localisation queries and pathnding. After the implementation, we evaluated the localisation accuracy and latency of the system by benchmarking navigation queries and the model creation algorithm. The system is capable of successfully navigating in Aalto University computer science department library. We were able to achieve an average error of 0.26 metres for successfully localised photos. In the Thesis, we also present challenges that we solved to adapt computer vision techniques for localisation purposes. Finally we observe the possible future work topics to adapt the system to a wide use. / Forskare har de senaste åren framfört olika inomhusnavigations- och lokaliseringssystem. Dock kräver lösningar som använder WiFi eller radiofrekvens identifikation en utbyggdnad av stödjande infrastruktur i navigationsområdena. Även teknikerna som används lider av precisionsfel. I det här examensarbetet redovisar vi en ny taktik för inomhusnavigation som använder sig av datorvisualiserings Structure from Motion-tekniker. Vi implementerade en navigationssystemsprototyp som använder bilder för att bygga en navigationskarta och kartlägga användarens position. I vårt klient-server baserat system är en klient en mobilapplikation som tillåter användaren att hitta sin position genom att ta en bild. På server-sidan hanteras kartor, lokaliseringsförfrågor och mättningar av förfrågorna och algoritmerna som används. Systemet har lyckats navigera genom Aalto Universitets datorvetenskapsbiblioteket. Vi lyckades uppnå en felmarginal pa 0.26 meter för lyckade lokaliseringsbilder. I arbetet redovisar vi utmaningar som vi har löst för att anpassa datorvisualiseringstekniker for lokalisering. Vi har även diskuterat potentialla framtida implementationer for att utvidga systemet.

In der vorliegenden Publikation wird ein Verfahren beschrieben, dass eine infrastrukturlose Positionierung im Inneren von Gebäuden ermöglicht. Unter infrastrukturlos wird in diesem Zusammenhang die autarke Positionierung eines Systems auf Basis seiner Inertialsensorik ohne den Einsatz von im Gebäude installierter Zusatzlösungen, wie Funksysteme, verstanden. Aufgrund der insbesondere über die Zeit erhöhten Fehlerbehaftung solcher Sensoren werden bei diesem Verfahren Innenraumkarten in den Lokalisierungsprozess einbezogen. Diese Kartendaten erlauben den Ausschluss invalider Positionen und Bewegungen, wie das Durchqueren von Wänden, und ermöglichen somit eine wesentliche Verbesserung der Ortungsgenauigkeit.

This thesis is concentrated on the Angle / Direction of Arrival (A/DOA) estimation and Beamforming techniques that can be used in the current and future engineering applications such as tracking of targets, wireless mobile communications, radar systems, etc. This thesis firstly investigates different types of AOA and beamforming techniques. A comprehensive comparison between the common AOA algorithms is performed to evaluate the estimation accuracy and illustrate the computational complexity of each algorithm. The effect of mutual coupling between the radiators and the impact of the position-error of the antenna elements on the estimation accuracy is also studied. Then, several new efficient AOA methods for current wireless localisation systems are proposed. The estimation accuracy and computational complexity are compared with well-known AOA methods over a wide range of scenarios. New methodologies for Covariance Matrix (CM) sampling are proposed to enhance and improve operational performance without increasing the computational burden. A new beamforming algorithm is proposed and implemented on a compact mm-Wave linear and planar antenna arrays to enhance the desired signal and suppress the interference sources in wireless communication systems. The issue of asset tracking in dense environments where the performance of the Global Positioning System (GPS) becomes unavailable or unreliable is addressed in the thesis as well. The proposed solution uses a low-profile array of sensors mounted on a finite conducting ground. A compact-size omnidirectional spiral sensor array of six electrically small dual-band antenna elements was designed to operate in the 402 and 837 MHz spectrum bands. For the lower band, a three-element superposition method is applied to support the estimated AOA whereas six sensors are considered for the higher band. An efficient and low complexity Projection Vector (PV) AOA method is proposed. An Orthogonal Frequency Division Multiplexing (OFDM) modulation is integrated with the PV technique to enhance the estimation resolution. The system was found to be suitable for installation on top of vehicles to localise the position of assets. The proposed system was tested to track non-stationary objectives, and then two scenarios were investigated: outdoor to outdoor and outdoor to indoor environments using Wireless In-Site Software. The results confirm that the proposed tracking system works efficiently with a single snapshot. / Higher Commission for Education Development (HCED) in Iraq Basra Oil Company Ministry of Oil