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BASIL: Binary Anchor-Based Smart Indoor Localization
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AI
DOI：10.26599/TST.2024.9010008.png)
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Abstract
En 中文
Indoor localization has been challenging research due to the invalidity of the global navigation satellite system in indoor scenarios. Recent advances in ambient assistive living have shown great power in detecting and locating persons living in their homes, especially using the ON/OFF binary sensors. In this paper, we exploit the Bluetooth low-energy beacons as device-based binary anchors under the lowest transmission power to turn any indoor activity and facility interaction into a binary location indicator. The binary anchors are fused with an extended Kalman filter based pedestrian dead-reckoning using a factor graph optimization, with extra constraints including the normalized magnetic loop closure which is optimized using an attenuation factor, and a rapidly-exploring random tree-based map collision validation. The proposed system provides a cost-effective, scalable, and robust localization for common indoor scenarios. The experimental results show an effective sub-meter precision for the long-term trajectories, and a small amount of anchors is enough for significant calibration in large scenarios.
Keywords:
Location awareness
Meters
Accuracy
Pedestrians
Magnetic separation
Magnetic sensors
Attenuation
indoor localization
sensor fusion
graph optimization
binary anchor
loop closure
Journal
T
IF:
3.5
Papers:
987
Citations:
2.5K
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