Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios

Abstract

The demand for positioning, localisation and navigation services is on the rise, largely owing to the fact that such services form an integral part of applications in areas such as agriculture, robotics, and eHealth. Depending on the field of application, these services must accomplish high levels of accuracy, flexibility, and integrability. This dissertation focuses on improving computing efficiency, data pre-processing, and software architecture for indoor positioning solutions without leaving aside position and location accuracy. The dissertation begins by presenting a systematic review of current cloud-based indoor positioning solutions. Secondly, we focus on the study of data optimisation techniques such as data cleansing and data augmentation. The third contribution suggests two algorithms to group similar fingerprints into clusters. The fourth contribution explores the use of Machine Learning (ML) models to enhance position estimation accuracy. Finally, this dissertation summarises the key findings in an open-source cloud platform for indoor positioning.