Name
Abstract | ||
|---|---|---|
Traditionally, there have been few options for navigational aids for the blind and visually impaired (BVI) in large indoor spaces. Some recent indoor navigation systems allow users equipped with smartphones to interact with low cost Bluetoothbased beacons deployed strategically within the indoor space of interest to navigate their surroundings. A major challenge in deploying such beacon-based navigation systems is the need to employ a time and labor-expensive beacon planning process to identify potential beacon placement locations and arrive at a topological structure representing the indoor space. This work presents a technique called IBeaconMap for creating such topological structures to use with beacon-based navigation that only needs the floor plans of the indoor spaces of interest. IBeaconMap employs a combination of computer vision and machine learning techniques to arrive at the required set of beacon locations and a weighted connectivity graph (with directional orientations) for subsequent navigational needs. Evaluations show IBeaconMap to be both fast and reasonably accurate, potentially proving to be an essential tool to be utilized before mass deployments of beacon-based indoor wayfinding systems of the future. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-58796-3_14 | arXiv: Human-Computer Interaction |
Field | DocType | Volume |
Beacon,Graph,Computer science,Planning process,Human–computer interaction | Journal | abs/1802.05735 |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seyed Ali Cheraghi | 1 | 2 | 1.08 |
| Vinod Namboodiri | 2 | 347 | 27.79 |
| Kaushik Sinha | 3 | 244 | 17.81 |