Name
Abstract | ||
|---|---|---|
Increasing population density and rapid urbanization lead to a burst of multifunctional, complicated and comprehensive buildings. Within these buildings, people often have difficulty to locate themselves or get to their destinations. Although real-time outdoor navigation has been widely applied, it is not the case in indoor space due to the lack of feasible or affordable indoor positioning technologies. Instead of a thorough solution, an alternative approach is proposed to indoor positioning and navigation in this paper. This approach is physically based on a mobile device, such as a smart phone, equipped with acceleration sensors. Accessible space of a building is represented with a 3D geographic information system network model, based on which, all points of interests are located spatially. Initial input from a user is used to figure out its current location, as the origin of navigation, and then the approach can generate an optimal path to the destination. While the user moves along this path, its linear displacement can be calculated with acceleration sensors in real time and further matched onto this path as user's real-time location. Accordingly, real-time guiding information can be delivered to users like outdoor navigation. This approach has been implemented in Android-based devices, and a series of experiments are conducted to demonstrate and validate the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1080/19475683.2016.1276101 | ANNALS OF GIS |
Keywords | Field | DocType |
Indoor navigation,positioning,acceleration sensors,mobile device | Geographic information system,Hybrid positioning system,Remote sensing,Turn-by-turn navigation,Mobile device,Acceleration,Mobile robot navigation,Smart phone,Geography,Network model | Journal |
Volume | Issue | ISSN |
23 | 1 | 1947-5683 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huijun Ren | 1 | 0 | 0.34 |
| Peishan Chai | 2 | 0 | 0.34 |
| Yanbing Zhang | 3 | 0 | 0.34 |
| Dong Xu | 4 | 7616 | 291.96 |
| Tao Xu | 5 | 1 | 1.03 |
| Xiang Li | 6 | 110 | 11.84 |