云-端协同的智能手机行人室内外无缝定位技术及其原型系统验证

Pedestrian Indoor and Outdoor Seamless Positioning Technology and Prototype System Based on Cloud-End Collaboration of Smartphone

  • 摘要: 随着位置服务应用的兴起和移动智能终端的普及,高精度导航定位需求正在从孤立的区域延伸到无缝的全域,从专业群体走向大众用户。然而,实时、连续、完备的行人导航仍然面临着诸多挑战,如室外区域全球导航卫星系统(global navigation satellite system,GNSS)信号易受遮挡影响、室内环境Wi-Fi/低功耗蓝牙(bluetooth low energy,BLE)/地磁指纹库更新频繁、室外内过渡区域平滑切换困难等。此外,大众移动智能终端的定位传感器受成本与功耗限制,观测数据一般噪声较大且稳定性差。因此,提出设计了云-端协同的大众行人用户室内外无缝精密定位方案,突破了信标指纹库的众包采集与快速更新、智能手机多传感器信息融合的精密定位、室内外无缝切换等关键难题,研制了大众行人协同精密定位软件,通过综合利用位置服务平台提供的协同精密定位增强信息,以及智能手机终端获取的多源观测数据(GNSS、微型惯性测量单元(miniature inertial measurement unit,MIMU)、Wi-Fi、BLE、磁力计及气压计等),实现了大众用户室外精度优于1.5 m,室内精度1~3 m,并具备室内外平滑切换能力的导航定位服务,支撑了大众用户室内外无缝精密定位的需求。

     

    Abstract:
      Objectives  With the rise of location service applications and the popularization of mobile smart terminals, the demand for high-precision navigation and positioning is extending from isolated areas to seamless whole areas, from professional groups to mass users. However, real-time, continuous, and complete pedestrian navigation still faces many challenges, such as occlusion of global navigation satellite system (GNSS) signals, frequent updates of Wi-Fi/ bluetooth low energy (BLE)/geomagnetic fingerprint databases, and smoothly switching in the transitional area of indoor and outdoor. In addition, the positioning sensors of popular mobile smart terminals are limited by cost and power consumption, and the observation data is generally noisy and poor in stability.
      Methods  Therefore, this paper proposes a pedestrian indoor and outdoor seamless positioning technology based on cloud-end collaboration of smartphone, which breaks through the crowd sourced collection and rapid update of the beacon fingerprint database, the precise positioning of the smart phone multi-sensor information fusion, and the indoor and outdoor seamless switching. We have developed a smartphone positioning application for pedestrians. The collaborative precision positioning enhancement information is provided by the location service platform and the multi-source observations data (GNSS, miniature inertial measurement unit (MIMU), Wi-Fi, BLE, magnetometer, barometer) are obtained by the smartphone.
      Results  The feild test results show that the outdoor positioning accuracy of ordinary users is better than 1.5 m, and indoor accuracy is about 1‒3 m.
      Conclusions  It supports thedemand of mass-market users for seamless and precise positioning indoors and outdoors.

     

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