Nowadays, the evolution of localisation and navigation technologies is vast, aiding towards facilitating users’ guidance in various environments. Outdoor positioning can be easily achieved, with the widely used Global Navigation Satellite Systems (GNSS), which comprise a universal standard for positioning and are included in every person’s mobile device. However, due to the presence of high buildings in dense urban environments and bad reception in indoor environments, the performance of GNSS is significantly degraded. Therefore, alternative ways of positioning and localisation respectively, need to be explored. In indoor environments, unlike outdoors, there is no universal standard, as the different indoor localisation techniques, that are currently implemented have their own bottlenecks. The most widely used Wi-Fi fingerprinting, requires a constantly up-to-date radio map of the signals from the Wi-Fi access points, whose creation is also a heavy and time-consuming technique. Additionally, other techniques require an installation of costly sensors or either equipment.

Therefore, this thesis investigates the possibility of the ceilings in public or semi-public buildings, being used for indoor localisation, by using features that are included in a simple mobile device. The research additionally involves location tracking of different users, in order to discover different movement patterns in an indoor facility. Indoor localisation is achieved based on the comparison of user and reference data, that can be both point clouds and images, using the Light detection and ranging (LiDAR) of an iPad 12 pro and camera sensors of an Android device. The point cloud-based localisation is implemented based on different combinations of global and local registration techniques, while the image-based approach involves different feature detection, description and matching techniques. Using a web-application to visualise the indoor localisation results, an indoor model and a network graph of the Faculty of Architecture and the Built Environment, location tracking of different users is implemented and visualised in a heat-map. Additionally, a dashboard is created that can be used by a facility manager to translate the user paths to valuable information and reveal different movement patterns in an indoor facility.

The followed methodology showed promising results, concerning the reliability of ceilings for real-time indoor localisation, based on LiDAR and camera sensors, that are incorporated in up-to-date mobile devices. The robustness of Colored Iterative Closest Point (ICP) algorithm for indoor localisation based on point clouds was revealed, both in terms of time efficiency and quality, while the combination of Speeded-Up Robust Features (SURF) feature detector and Scale Invariant Feature Transform (SIFT) descriptor provides the optimal indoor localisation results with image data. The proposed pipeline revealed encouraging results for use in emergency situations, based on static data acquisition of a user, while it is also suitable for dynamic applications, in case a sensor is mounted on an automated device for indoor mapping operations.

Nowadays, humans rely in technology more and more when it comes to navigation and localisation and in many aspects of life as well. While most concepts related to localisation and navigation of outdoors environments are already well derived from various researches and softwares, the indoor environment remains a significantly unexplored area. Nevertheless, lately there have been increased interest on Location Based Services (LBS) and Indoor Positioning Systems (IPS). There are already several methods available for indoor localisation such as Wi-Fi Fingerprinting and Bluetooth Beacons, but none of them is fully functional yet. It remains a field that requires more and further research and investigation in order to reach a satisfactory and complete Indoor Localisation-Navigation method.
Therefore, this thesis's main objective is to investigate and explore a new method for Indoor Localisation based on Isovists. The exploration and evaluation of Isovist-Fingerprinting approach for Indoor Localisation can extend the fields of LBS and Geomatics. The main research question is “To what extent can isovist support Indoor Localisation” and through this and a series of sub-questions to analyse the Isovist concept in relation to the Indoor Localisation. This is achieved by forming a proof of concept and a methodology that investigates how the Isovists would benefit an LBS.
To succeed that the methodology is divided into 4 main sections. The Data Acquisition for which the newly supported from smartphones Light Detection And Ranging (LiDAR) technology were used. The Space Syntax and Isovist Analysis Measures, where all the concepts related such as the Isovist Parameters were analysed in depth for better understanding of their effect. Then the Matching and Localisation Algorithms, where the possibilities and options on how to reach the localisation were investigated and analysed. And finally, the Tests and Experiments took place in order to evaluate all the prior stages of the methodology.
The main conclusion of this research is that a method for Indoor Localisation based on Isovists is feasible and can indeed support an LBS. The analysis and evaluation of all related components has be done and if putting all the parts in the right order they can be of high value for LBS applications. Since is a new method of Indoor Localisation, there is plenty of future work to be done which mainly focuses on how to connect it with existing techniques and integrate all together into a user application.