# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 5 Conclusions  
The proposed algorithm provides a novel way of performing pose estimation of an augmented reality device in a digital twin. The digital twin is obtained through existing BIM models, allowing localization in all buildings which provide such a model. We break the problem of pose estimation down from a 6-DOF problem into multiple independent dimensions. Two rotational degrees of freedom are obtained by hardware sensors, the height above ground is calculated using a ground-plane estimation, and the position in the building is determined by template matching. The input for our algorithm is point-cloud data and sensor information from AR hardware, making the approach independent of external sensors. Our algorithm achieves higher accuracy than current vision-based approaches such as BIM-PoseNet [19], which is around 2 m, and requires no extensive training phase. While [PERSON] et al. [23] achieve a higher accuracy and reliability, the approach requires knowledge about the initial position of the device. The UAV localization presented by [PERSON] et al. [14] has a similar accuracy to our algorithm but requires a significantly longer computation time on similar-sized environments, making it not applicable for AR localization. In our algorithm, aligning the recorded point cloud through normal analysis allows for a significant reduction in computation time and applying template matching on floor plans ensures a high accuracy with good performance. Performance of the algorithm proves to be in line with modern AR hardware capabilities, which allows for localization even when no network connection is available. Scalability of the algorithm can be a problem, since complexity scales with the size of the digital twin and the observed environment. Still, the performance impact of the observed environment can be alleviated by using template caching. The accuracy performs well over all environment sizes and improves with more observed environments. Large localization errors occur in most part when not enough of the environment is observed and may offer widely inaccurate results. This could be mitigated by introducing a certainty value which could inform a user that more information about the environment is necessary for a localization. Since the localization may drift over time, a value for point age could be introduced. Older observations could be weighted less favorably than new observations or be removed from the point cloud entirely. This would allow for admission of mistakes made by the device's local positioning systems. Self similarities are still a problem for the proposed algorithm, but since all the previously observed environments are considered for localization, we achieve better results in self-similar environments than stateless image-based methods. Accuracy in self-similar environments could be improved by introducing sensor fusion. If external beacons such as Wi-Fi signals are used to narrow in on the correct location, self similarities over multiple floors and reflections could be reduced. Utilizing the semantics of the surroundings may also improve localization, e.g., recognizing certain structures such as doors or windows or recognizing signage. Our approach shows that floor-plan matching is an appropriate approach for AR localization. With the proposed algorithm, it is possible to compute a mapping between the local device coordinates and the BIM model. From the 2D mapping, the position of the device can be determined to about 10 cm and 1\({}^{\circ}\) after exploring the surrounding geometry sufficiently. This exploration phase may require the user to walk 15-20 m, which may depend on the amount of self similarity inherent to the building. Maintenance workers may benefit from an augmented reality room-layout display with data provided by BIM models or databases. Since reliability can only be assured when enough distinct geometry has been observed, a user would have to explore the space around them before they are correctly localized.