ARKit as indoor positioning system This paper describes the possibilities of indoor space mapping and user movement tracking using augmented reality technologies. The stateoftheart options for indoor navigation are discussed, including the one, when the sensors deployed on a regular smartphone, are utilized. One way is to use data from accelerometer, gyroscope, and magnetometer sensors. When combined with the analysis of the image from the camera, we can get a relatively accurate result of the devicexe2x80x99s position and motion in the real world. This is what offer todayxe2x80x99s frameworks ARKit on iOS and ARCore on Android. Technologies such as GPS are suitable to use for an outdoor environment and do not work indoor. For indoor positioning, we can use beacons, but it required additional hardware and setup. The paper discusses the possibility of their use to analyze movement in space without using other navigation technologies. Emphasis is placed on optimizing the algorithms created to track device position in space. Paper on the end mentions possible use cases to use the developed algorithm.

Indoor Mapping and Positioning using Augmented Reality Locationbased services are becoming an important part of life and there is an increasing demand for indoor positioning. Combination of GPS and mobile cellular networks has solved the problem for outdoor environments. However, the same level of precision has not been achieved for indoor location estimation. The problem of locating an indoor environment has been studied only recently. Much research contributed to the innovative concept of an indoor positioning system. Considering the cost and the effort involved in the existing location estimation approaches, Augmented Reality (AR) based positioning method is one of the most promising methods to determine the location of a mobile device. Accordingly, in this paper, we propose, implement, and evaluate an ARbased location estimation smartphone application that can be used indoors. The evaluation results show indicate that the proposed application can estimate the location in small areas with an errorrate of 0.31 meters and in large areas with errorrate of 7.8 meters.)