基于振动的车辆定位航位推算

M. Kourogi, Ryosuke Ichikari, Takahiro Miura, Satoki Ogiso, T. Okuma
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摘要

跟踪仓库和工厂中叉车等车辆的位置至关重要,因为它有助于人员安全和制造和物流活动的输出性能。有必要开发一种不经任何修改而通过附加通用惯性测量单元(imu)来定位车辆的方法。在本研究中,我们的目标是提供基于振动分析的imu直接附着在车辆上的车辆位置估计技术。我们还开发了一种新的方法,可以在没有任何人工程序的情况下自动校准所提出的振动特征和道路状况。在扩展的卡尔曼滤波框架中融合了短期(0.5-2秒内)的加速度积分和长期(> 30秒)的振动特征观测,以稳定地估计相关参数,也可用于检测路况变化。在这项研究中,我们证实,在没有任何手动校准程序的情况下,使用智能手机,电动轮椅和轻便马车的定位精度低于平均行驶距离的1-5%。借助通用蓝牙低功耗(BLE)信标,我们也证实了定位误差平均可以限制在2米以下。
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Vibration-Based Dead-Reckoning for Vehicle Localization
Tracking locations of vehicles such as forklifts in warehouses and factories is crucial since it contributes to the safety of personnel and the output performance of manufacturing and logistics activities. It is necessary to develop a method of locating the vehicles by attaching generic inertial measurement units (IMUs) without any modifications. In this research, we have aimed at providing technologies to estimate locations of the vehicles with the IMUs simply attached to the vehicles based on vibration analysis. We have also developed a novel method of automatic calibration between the proposed vibration signatures and the road conditions without any manual procedures. Integration of acceleration in short terms (within 0.5-2 seconds) and observation of vibration signatures in long terms (> 30 seconds) are fused in the extended Kalman filtering framework to stably estimate the correlation parameters and can also be used to detect changes in the road conditions. In this research, we confirmed that accuracy of localization is below 1-5% of distance travelled on average for electric wheelchairs and handy wagons without any manual calibration procedures by using the smartphones. With aids of generic Bluetooth Low Energy (BLE) beacons, we also confirmed that the error of location can be bounded to below 2 meters on average.
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