Abstract
Within the research project LOBSTER, a system for analyzing the behavior of escaping groups of people in crisis situations within public buildings to support first responders is developed. The smartphone-based indoor localization of the escaping persons is performed by using positioning techniques like WLAN fingerprinting and dead reckoning realized with MEMS-IMU. Hereby, WLAN fingerprinting is analyzed especially in areas of few access points and the IMU-based dead
reckoning is accomplished using step detection and heading estimation. The data of all sensors are fused in combination with building layouts using different Bayes filters. The behavior of the
Bayes filters is investigated especially within indoor environments. The restrictions of the Kalman filter are shown as well as the advantages of a Particle filter using building plans.
reckoning is accomplished using step detection and heading estimation. The data of all sensors are fused in combination with building layouts using different Bayes filters. The behavior of the
Bayes filters is investigated especially within indoor environments. The restrictions of the Kalman filter are shown as well as the advantages of a Particle filter using building plans.
| Originalsprache | englisch |
|---|---|
| Titel | 2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2013) |
| Seiten | 1-10 |
| Publikationsstatus | Veröffentlicht - 2013 |
| Veranstaltung | 4th International Conference on Indoor Positioning and Indoor Navigation: IPIN 2013 - Montbéliard, Frankreich Dauer: 28 Okt. 2013 → 31 Okt. 2013 |
Konferenz
| Konferenz | 4th International Conference on Indoor Positioning and Indoor Navigation |
|---|---|
| Kurztitel | IPIN 2013 |
| Land/Gebiet | Frankreich |
| Ort | Montbéliard |
| Zeitraum | 28/10/13 → 31/10/13 |
Fields of Expertise
- Information, Communication & Computing
Treatment code (Nähere Zuordnung)
- Application