As a result of the rapid development of smartphone-based indoor localization technology, location-based services in indoor spaces have become a topic of interest. However, to date, the rich data resulting from indoor localization and navigation applications have not been fully exploited, which is significant for trajectory correction and advanced indoor map information extraction. In this paper, an integrated location acquisition method utilizing activity recognition and semantic information extraction is proposed for indoor mobile localization. The location acquisition method combines pedestrian dead reckoning (PDR), human activity recognition (HAR) and landmarks to acquire accurate indoor localization information. Considering the problem of initial position determination, a hidden Markov model (HMM) is utilized to infer the user’s initial position. To provide an improved service for further applications, the landmarks are further assigned semantic descriptions by detecting the user’s activities. The experiments conducted in this study confirm that a high degree of accuracy for a user’s indoor location can be obtained. Furthermore, the semantic information of a user’s trajectories can be extracted, which is extremely useful for further research into indoor location applications.

Mobile Augmented Reality (MAR) systems are becoming ideal platforms for visualization, permitting users to better comprehend and interact with spatial information. Subsequently, this technological development, in turn, has prompted efforts to enhance mechanisms for registering virtual objects in real world contexts. Most existing AR 3D Registration techniques lack the scene recognition capabilities needed to describe accurately the positioning of virtual objects in scenes representing reality. Moreover, the application of such registration methods in indoor AR-GIS systems is further impeded by the limited capacity of these systems to detect the geometry and semantic information in indoor environments. In this paper, we propose a novel method for fusing virtual objects and indoor scenes, based on indoor scene recognition technology. To accomplish scene fusion in AR-GIS, we first detect key points in reference images. Then, we perform interior layout extraction using a Fully Connected Networks (FCN) algorithm to acquire layout coordinate points for the tracking targets. We detect and recognize the target scene in a video frame image to track targets and estimate the camera pose. In this method, virtual 3D objects are fused precisely to a real scene, according to the camera pose and the previously extracted layout coordinate points. Our results demonstrate that this approach enables accurate fusion of virtual objects with representations of real world indoor environments. Based on this fusion technique, users can better grasp virtual three-dimensional representations on an AR-GIS platform.