In situations where we want to use mixed reality systems over larger areas, it is necessary for these systems to maintain a correct orientation with respect to the real world. A solution for synchronizing the mixed reality and the real world over time is therefore essential to provide a good user experience. This thesis proposes such a solution, utilizing both a local positioning system named WISPR using Ultra Wide Band technology and an internal positioning system based on Google ARCore utilizing feature tracking. This is done by presenting a prototype mobile application utilizing the positions from these two positioning systems to align the physical environment with a corresponding virtual 3D-model. This enables increased environmental awareness by displaying virtual objects in accurately placed locations in the environment that otherwise are difficult or impossible to observe. Two transformation algorithms were implemented to align the physical environment with the corresponding virtual 3D-model: Singular Value Decomposition and Orthonormal Matrices. The choice of algorithm showed minimal effect on both positional accuracy and computational cost. The most significant factor influencing the positional accuracy was found to be the quality of sampled position pairs from the two positioning systems. The parameters used to ensure high quality for the sampled position pairs were the LPS accuracy threshold, sampling frequency, sampling distance, and sample limit. A fine-tuning process of these parameters is presented and resulted in a mean Euclidean distance error of less than 10 cm to a predetermined path in a sub-optimal environment. The aim of this thesis was not only to achieve high positional accuracy but also to make the application usable in environments such as mines, which are prone to worse conditions than those able to be evaluated in the available test environment. The design of the application, therefore, focuses on robustness and being able to handle connection losses from either positioning system. The resulting implementation can detect a connection loss, determine if the loss is destructive enough through performing quality checking of the transformation, and with this can apply both essential recovery actions and identify when such a recovery is deemed unnecessary.