Name
Abstract | ||
|---|---|---|
This article presents the implementation of an indoor localization approach that combines map matching and a circular particle filter defined in a Bayesian framework. The technique relies only on velocity and heading observations coupled with a map of the road network. No prior knowledge of the initial position is given. A circular distribution is used to match the vehicle's heading with the roads direction. This allows to detect turns and provide a more accurate position estimate. The algorithm is assessed with a synthetic dataset in a real context. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CIST.2016.7804999 | 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt) |
Keywords | Field | DocType |
Indoor localization,Particle filter,Map matching,Circular Filter | Computer vision,Computer science,Circular distribution,Particle filter,Artificial intelligence,Atmospheric measurements,Particle,Map matching,Bayesian probability | Conference |
ISSN | ISBN | Citations |
2327-185X | 978-1-5090-0752-3 | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karim El Mokhtari | 1 | 1 | 0.70 |
| Serge Reboul | 2 | 25 | 7.02 |
| Jean-Bernard Choquel | 3 | 44 | 5.67 |
| Benaissa Amami | 4 | 4 | 2.85 |
| Mohammed Benjelloun | 5 | 163 | 24.87 |