Image-based indoor localization using smartphones has become popular, leveraging visual landmarks and fingerprint extraction for localization. Fingerprint density significantly affects accuracy, but collecting dense, high-resolution fingerprints during on-site surveys is labor-intensive and incurs high computation/storage costs during matching. Additionally, efficient fingerprint extraction often constrains users to specific shooting poses, with deviations markedly reducing localization accuracy. To address these challenges, we introduce ARGILS, an Automated Real-time Generative Image Localization System. The key idea is to use cross sparse sampling instead of dense sampling, generate fingerprint features for missing locations, and quickly match locations through feature orthogonal decomposition. Cross sparse sampling ensures full coverage of scene features and helps to generate missing fingerprints. To maintain high localization resolution with sparse sampling, we designed a distance-constrained generative adversarial network to generate fingerprints for unsampled locations. Additionally, we developed an orthogonal fingerprint extraction method to decompose image features into horizontal and vertical directions in 2D space. To improve robustness against obstacles, we implemented a scanning localization scheme using key frame filtering and clustering. We have implemented ARGILS and performed extensive real-world evaluations. Experiment results show that when reducing 75% site survey effort, the average location error of ARGILS is around 2.5m in a shopping mall, 48% higher than state-of-the-art methods. ARGILS can also efficiently speed up localization process, with the time consumption ranging from 0.1 to 0.3 seconds on smartphones of various configurations.

Among the numerous indoor localization technologies, image-based solution has great advantages on convenient access from smartphone and its infrastructure-less deployment. However, the image-based localization also suffers from two key disadvantages, which hinders the universal application. Firstly, it requires a large amount of computing and storage resources, which is difficult to achieve for the mobile device, while cloud-based scheme incurs unacceptable delay. Secondly, although this solution doesn't require infrastructure, it still suffers from labor intensive image-database construction and updates. To overcome these limitations, we propose an image-based indoor localization method featured with realtime localization and labor-less image database update. This method mainly innovates in two aspects. First, we propose a mobile device compatible image database compressing framework, which enable realtime and accurate on-device image searching even in a large scenario. Our localization method achieves resource efficiency (in terms of storage and processing) by only keeping image feature vectors, and employing the efficient k-mean tree to search for the best matched image. Secondly, to achieve labor-less image database updating, we mainly add high-quality and informative query image into the database. These query image could compensate the missing information or changed scenario in a up-to-date manner. We conduct real experiments in Android Platform to verify the feasibility and performance of the localization method. Experiment results show that our method has good accuracy (90% location errors are within 1.5m) and high real-time performance (average location delay is less than 0.5s).