A Smartphone based visual global localization method with high usability in large indoor spaces

Objectives: Smartphone based visual global localization is a research hotspot in the location based services community. Existing methods suffer from the problems of poor reliability and low computing efficiency especially when they are used for large indoor environments such as airport and shopping mall. M ethods: This paper proposes a “Rough localization to Accurate localization”two-level localization method, it is based on 3d real map and applied to large indoor scenes such as shopping malls. To reduces the location computing time, a method of limiting the scope of image database is proposed: Use WiFi fingerprint matching algorithm to obtain the location results, then limit the image database according to the location results. In order to improve the positioning accuracy, a new method of constructing database is proposed. The whole scene is divided into multiple regions, each region completes the database establishment independently, and then splices different databases. In order to reduce the location error, a scene recognition method is proposed. Deep learning method is used to remove the ceiling image. It can reduce the feature points matching errors. Results： Comparing the location computing time before limiting the scope of image database and after, the proposed method reduced the positioning time from 6s to 0.8s per image. Comparing the location error and positioning precision before removing the ceiling image and after, the scene recognition method reduced the location error from 5.76m to 2.78m and improved the positioning precision from 0.98 m to 0.84 m. The new method of constructing database can improve the positioning precision from 0.82m to 0.45m. All in all, the proposed method can improve the positioning precision from 0.98 m to 0.45 m, reduce the positioning time more than 5 times. Conclusions： Although the proposed method achieves sub-meter accuracy of indoor vision global positioning, the feature points matching errors affects the positioning precision. In future work, feature lines will be used to improve the positioning accuracy.