Real-time and Precise Indoor Localization System in Multi-Floor Buildings for Pedestrian using Cloud Platform - Kim, Taehun; Shin, Beomju; Kang, Chung G.; Jung Ho Lee; Yu, Changsoo; Kyung, Hankyeol; Shin, Donghyun; Lee, Taikjin (2022 22nd International Conference on Control, Automation and Systems (ICCAS 2022), 2022)
Pedestrians use their smartphones to determine their location. Accordingly, there is a growing demand for seamless localization that can estimate the location regardless of indoor and outdoor space. However, the Global Navigation Satellite System used for outdoor location estimation has poor reception in the indoor environment, making it difficult to use in indoor space. Therefore, we propose the indoor localization technology based on Surface Correlation (SC). This indoor localization technology can estimate the location of pedestrians on only one floor. In this study, floor detection was performed using only RF signal without using other sensors such as barometric pressure sensor in the multifloor building. The most important thing in floor detection is the reliability of the current floor and floor change detection. We can estimate the coarse floor using the unique ID of the RF source installed on each floor. Then, the virtual trajectory is generated using only RF signal, and the degree of similarity with the floor is determined by identifying the fine floor of the coarse floor estimated by applying the existing SC-based localization. Once the fine floor of pedestrians is identified, the final absolute location of pedestrians in the multi-floor building can be estimated by calculating the indoor location of the estimated floor using conventional SC-based localization. To verify the performance of the proposed algorithm in real-time, the algorithm was implemented in Google Cloud Platform. Pedestrians can check the indoor location results through real-time connection with the smartphone. In the actual multi-floor building, the similarity between the floor estimated by the proposed algorithm and the floor estimated using the barometric pressure sensor is about 95.0%. And the RMSE of the indoor localization results of the proposed system is about 3.662m.