Localisation and navigation technologies have vastly evolved during the last years, facilitating users’ guidance in various environments. Unlike outdoor environments where GNSS comprises a universal solution, in indoor environments various localisation techniques have been used, each one with its drawbacks. Thus, this research investigates the reliability of the ceilings towards indoor localisation, by using components that are included in a simple mobile device. The choice of ceilings lies in their advantages, which include the incorporation of various characteristic components, as well as the absence of obstacles between them and the sensor. Indoor localisation is achieved based on LiDAR point clouds and images from RGB sensors of mobile devices. Additionally, this research involves location tracking of different users, to discover different movement patterns in an indoor facility. The proposed methodology revealed the robustness of the Coloured ICP algorithm for in-door localisation based on point clouds, both in terms of time efficiency and quality, while the combination of the SURF feature detector and SIFT descriptor provides the optimal indoor localisation results with image data. The proposed pipeline revealed encouraging results for use in emergencies, 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. The captured point clouds of the ceilings can also be used as a reference to CAD and BIM models, to help the modelling of the existing utilities and their components in an indoor facility.

Indoor localisation methods are an essential part for the management of COVID-19 restrictions, social distancing, and the flow of people in the indoor environment. Moving towards an open work space in this scenario requires effective real-time localisation services and tools, along with a comprehensive understanding of the 3D indoor space. This project’s main objective is to analyse how ArcGIS Indoors can be used with location awareness methods to elaborate and develop space management tools for COVID-19 restrictions in order to reopen the workspace for TU Delft Campus. This was accomplished by using six Arduino micro controllers, which were programmed in C++ to scan all available Wi-Fi fingerprints in the east wing of the Faculty of Architecture and the Built Environment of TU Delft and send over the data to an ArcGIS Indoor Information Model (AIIM). The data stored on the AIIM is then accessed using the app on the user’s Android device using REST Application Programming Interface (API) where a kNN based matching algorithm then identifies the location of the user. The results show that the localisation is not consistent for rooms that are directly above each other or share common access points. However, when functioning to locate different tables inside a room, the system proved to uniquely distinguish between the specific tables. As a result, we can conclude that based on the size of the rooms, more Arduino devices should be installed to achieve an ideal accuracy. Finally, recommendations are made for the continuation of this research.