# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 2 State of Technology
### Localization without External Sensors  
The problem of pre-deployment effort can be negated by simply not using external sensors for localization. Localization procedures that do not rely on external sensors have to rely on only the local sensory information and their prior knowledge of the environment. Such a system usually has low running costs, as only the localization device itself has to be kept running. Without external sensors to be installed, such a system can be transferred easily to different locations and environments. A problem arises in self-similar environments. Similar to how a human can be lost in a building by mistaking one place for another, a localization algorithm can be susceptible to self similarities in buildings. In the following, we will discuss different localization methods without the use of external sensors. The AR localization problem shares similarity with the point-cloud registration problem. The goal of point-cloud registration is to find a transformation to a point cloud such that it aligns optimally with a different set of points. A simple and ubiquitous algorithm is the Iterative Closest Point (ICP) algorithm [11]. While efficient and accurate, the algorithm is often susceptible to local minima. Since localization usually requires registering a subset of a whole model, getting caught in a local minimum is likely. Attempts to make ICP globally optimal have been made [12] but suffer from increased run-time complexity. [PERSON] et al. [13] showed that using point clouds for real-time localization is feasible by using normal distribution transforms for loop closure. Using the geometry of the surroundings has the advantage of being independent of lighting conditions, as shown by [PERSON] et al. [14]. The authors achieve a localization accuracy of 20 cm and 20\({}^{\circ}\) for localizing Unmanned Aerial Vehicles (UAVs) positions in known environments, but execution time for large environments can be up to 60 s. Using point-cloud matching in AR localization offers a significant simplification to the domain: AR localization may apply domain-specific heuristics to achieve a registration of its surrounding environment, while general point-cloud algorithms are built to work on arbitrary geometry. For example, [PERSON] et al. [15] are able to perform localization by matching point clouds with building models using plane features, as rooms in buildings usually consist of multiple faces perpendicular to each other. [PERSON] et al. [16] are also able to use domain specifics for localization by recognizing and matching tree structures in orchards using a Hidden Markov Model. A different approach to localization is observing the environment through vision. This is a very similar way to which humans or animals learn to navigate their surroundings: Natural landmarks, such as object edges or lettering, are recognized and remembered. The observer can then use these landmarks to build a map of its surroundings and to simultaneously determine its own position in the map. This procedure is usually called Simultaneous Mapping and Localization (SLAM) and has prominent use in the robotics field. [PERSON] et al. [4] summarize many of the state-of-the-art visual place-recognition approaches. Another vision approach is the use of Convolutional Neural Networks (CNNs) to learn mapping between images of the environment and their global positions. Since CNNs are efficient to execute, they can provide a very fast method of localization. CNNs have been successfully applied to localization in outdoor environments using PoseNet [17]. The network that PoseNet provides also performs well in indoor environments [18], with an accuracy of 1-3 m. [PERSON] et al. [19] have even trained PoseNet on simulated data obtained from BIM models, training on computer-generated images of an indoor space to learn a pose regression.