| 内容提要: |
Indoor localization has attracted a lot of attention because of its importance for location-based services. A fusion algorithm based on extreme learning machine (ELM) and Dempster-Shafer (D-S) evidence theory is proposed. ELM learns the data model with high speed. During online phase, the final localization result of a frame is decided by the trust degree obtained from D-S. Angle judgments are also introduced to decrease the big localization errors of turning. Compared with the existing vision-only methods,
the proposed method can both run in real time and achieve good
localization accuracy even in challenging scenarios. |