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CrowdMagMap 2.0: Crowdsourced Magnetic Mapping for Multi-Floor Underground Parking Lot Navigation

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Publicado en:IEEE Transactions on Intelligent Transportation Systems vol. 26, no. 10 (2025), p. 18708-18721
Autor principal: Kuang, Jian
Otros Autores: Wang, Yan, Ding, Longyang, Zhou, Baoding, Xu, Liping, Cao, Li, He, Lanqin, Wen, Yunhui, Niu, Xiaoji
Publicado:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Materias:
Mapping
Dead reckoning
Location based services
Matching
Trajectory optimization
Maps
Indoor environments
Crowdsourcing
Radio signals
Parking facilities
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Resumen:Location-based services (LBS) have become an integral part of daily life and work for the general public. However, achieving widespread and accurate positioning in typical indoor environments remains a significant challenge, particularly in multi-floor indoor parking lots where radio frequency signals like WiFi are often unavailable. Indoor magnetic matching presents a viable solution, but it requires reducing mapping costs through the use of crowdsourced data. To tackle this issue, we propose an innovative method for constructing magnetic maps using crowdsourced vehicle data. Our approach introduces a multi-user joint vehicle dead reckoning technique based on graph optimization, which provides consistent directional estimates of crowdsourced vehicle trajectories. Subsequently, we establish associations between different vehicle trajectories using multi-attribute features of the magnetic field. Building on this foundation, we propose a global trajectory optimization with inequality and equality constraints to achieve precise estimation of crowdsourced vehicle trajectories. Testing with simulated data from two three-floor underground parking lots demonstrates that the proposed method, utilizing only on-board smartphone sensor data, achieves plane and elevation errors of less than 2.75 meters (95%) and 0.59 meters (95%), respectively. Additionally, the magnetic matching positioning error based on crowdsourced magnetic sequence maps is less than 2.29 meters (95%).
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2025.3597273
Fuente:Advanced Technologies & Aerospace Database