Comparison of hybrid localization methods using images and Wi-Fi signals

Indoor localization is important for many applications such as navigation, movement tracking, geotagging, and augmented reality. Most studies have used either Wi-Fi or image signals to determine the user’s location. However, each localization method has advantages and disadvantages. In this study, we propose a hybrid localization system combining the advantages of Wi-Fi and image-based methods. The localization is calculated based on the best four outputs of either image or Wi-Fi localization system. The system was evaluated by comparing the accuracy and unit errors of image-based, Wi-Fi-based, hybrid (image + Wi-Fi), hybrid (Wi-Fi + image) methods. The results showed accuracies of 77.2%, 49.5%, 73.1%, and 81.6% in the image-based, Wi-Fi-based, hybrid (image + Wi-Fi), and hybrid (Wi-Fi + image) methods, respectively. The hybrid (Wi-Fi and image) method has the lowest error and highest accuracy of the four methods compared. In addition, the image-based localization system shows the highest error, while the Wi-Fi-based localization system shows the lowest accuracy. The robot tests prove that the proposed hybrid system can achieve excellent performance in indoor localization. The proposed hybrid system uses both image processing and Wi-Fi fingerprinting methods to determine the mobile device's location by creating the two-phase framework, which can help improve the accuracy of indoor localization.