Unlike outdoor environments, there is no wide-spread solution to positioning inside a building. Indoorsolutions rely on pre-installation of infrastructure, such as Bluetooth beacons or ultra-wide bandtechnology. Recently, there has been growing interest in the use of Augmented Reality (AR) for indoorpositioning. AR devices use an algorithm known as Simultaneously Localisation and Mapping (SLAM)to scan an environment and find a position inside. This make it possible to estimate a position on-theflywithout any pre-installations. The Microsoft Hololens (MH) is a head-mounted display AR devicethat is able to perform SLAM. The use of SLAM for indoor positioning can be beneficial in the case ofEmergency Response (ER). The place of impact in ER is unknown and there is no time to install anyinfrastructure beforehand.Two problems arise with the use of SLAM for indoor positioning. (1) A position is a pin point in spacethat is defined by a reference frame. In the case of SLAM, the frame is the scanned object. In mostsituations, a map or floor plan is more appropriate as reference frame, in order to give a full context.(2) The SLAM algorithm suffers from drift, a growing error over time. This research tries to solve theseproblems using shape registration. The SLAM output can be aligned to a reference floor plan, by use ofa spatial matching technique. This alignment is a continuous process to account for the drift errors ofthe SLAM algorithm.Three spatial matching techniques are compared: Iterative Closest Points (ICP) iteratively tries to minimizethe distance between two shapes using least squares; Instantaneous Kinematics (IK) is a varianton ICP that makes use of a velocity vector; and Hough Transform makes use of the Hough domainproperties, where rotation is invariant to translation and scale. These algorithms are compared on theiraccuracy, computation time and robustness.It is concluded that the Hough Transform algorithm gives the most accurate results and is fastest. In thisresearch it was found that an average accuracy of < 1m can be maintained over 80% of the experiments,with maximum errors up to 5 meter. In 20% of the experiments, the error can extremely diverge upto >100 meter. It is suggested that the quality of the scan and the existence of artefacts (e.g.furniture,people) are the cause of these errors. That makes the method unsuitable for indoor positioning in thecase of Emergency Response, that needs extremely reliable systems. Research is needed to create a morerobust method, that uses better outlier detection methods. However, the results are promising and doopen the door for indoor navigation using Augmented Reality.

This paper has its focus on the accessibility, interoperability and quality of different point cloud datasets. The main objective of the project is to research the process of harmonizing point cloud datasets of different origins and to incorporate the integrated datasets in a web viewer that allows for visualization and analysis. As a case study, the Three-Country Point (TCP) where Germany, Belgium, and the Netherlands share a border is evaluated.