Map-Aided Fusion of IMU PDR and RSSI Fingerprinting for Improved Indoor Positioning

At present, indoor localization becomes an attractive research area enabling many opportunities. Although there are several solutions for indoor localization, the standalone localization methods suffer from various limitations that affect the localization accuracy. This study presents a map matching-based lightweight sensor fusion technique that can combine the IMU-based PDR with the RSSI fingerprinting method to achieve high precision position estimates. Spatial knowledge from the indoor floor plan is used to implement a landmark-assisted PDR to bound the accumulation error. Moreover, the KD-tree searching method along with a set of map matching techniques are exploited to the proposed sensor fusion technique that reduces the fingerprint search space while eliminates the spatial ambiguity problem of the RSSI. The proposed method was evaluated and compared with several standalone techniques. Results demonstrated that the proposed fusion method yields a median positioning accuracy of 0.73 m and outperformed the considered standalone methods.

Automated summary

This study presents a map matching-based lightweight sensor fusion technique that combines IMU-based PDR with the RSSI fingerprinting method to achieve high precision indoor positioning estimates. By utilizing spatial knowledge from indoor floor plans, the proposed method implements landmark-assisted sensor fusion to reduce fingerprint search space and eliminate spatial ambiguity issues with RSSI.