Next Article in Journal
Assessing Performance of Three BIM-Based Views of Buildings for Communication and Management of Vertically Stratified Legal Interests
Next Article in Special Issue
Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
Previous Article in Journal
Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap
Previous Article in Special Issue
A Multiple Ant Colony Optimization Algorithm for Indoor Room Optimal Spatial Allocation

A Novel Semantic Matching Method for Indoor Trajectory Tracking

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Authors to whom correspondence should be addressed.
Academic Editors: Sisi Zlatanova and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(7), 197;
Received: 15 May 2017 / Revised: 29 June 2017 / Accepted: 29 June 2017 / Published: 1 July 2017
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
The rapid development of smartphone sensors has provided rich indoor pedestrian trajectory data for indoor location-based applications. To improve the quality of these collected trajectory data, map matching methods are widely used to correct trajectories. However, these existing matching methods usually cannot achieve satisfactory accuracy and efficiency and have difficulty in exploiting the rich information contained in the obtained trajectory data. In this study, we proposed a novel semantic matching method for indoor pedestrian trajectory tracking. Similar to our previous work, pedestrian dead reckoning (PDR) and human activity recognition (HAR) are used to obtain the raw user trajectory data and the corresponding semantic information involved in the trajectory, respectively. To improve the accuracy and efficiency for user trajectory tracking, a semantic-rich indoor link-node model is then constructed based on the input floor plan, in which navigation-related semantics are extracted and formalized for the following trajectory matching. PDR and HAR are further utilized to segment the trajectory and infer the semantics (e.g., “Turn left”, “Turn right”, and “Go straight”). Finally, the inferred semantic information is matched with the semantic-rich indoor link-node model to derive the correct user trajectory. To accelerate the matching process, the semantics inferred from the trajectory are also assigned weights according to their relative importance. The experiments confirm that the proposed method achieves accurate trajectory tracking results while guaranteeing a high matching efficiency. In addition, the resulting semantic information has great application potential in further indoor location-based services. View Full-Text
Keywords: semantic matching; trajectory tracking; indoor model semantic matching; trajectory tracking; indoor model
Show Figures

Figure 1

MDPI and ACS Style

Guo, S.; Xiong, H.; Zheng, X. A Novel Semantic Matching Method for Indoor Trajectory Tracking. ISPRS Int. J. Geo-Inf. 2017, 6, 197.

AMA Style

Guo S, Xiong H, Zheng X. A Novel Semantic Matching Method for Indoor Trajectory Tracking. ISPRS International Journal of Geo-Information. 2017; 6(7):197.

Chicago/Turabian Style

Guo, Sheng, Hanjiang Xiong, and Xianwei Zheng. 2017. "A Novel Semantic Matching Method for Indoor Trajectory Tracking" ISPRS International Journal of Geo-Information 6, no. 7: 197.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop