用于室内导航的9- DoF MEMS IMU的统计传感器融合
Statistical Sensor Fusion of a 9-DoF MEMS IMU for Indoor Navigation
摘要：
MEMS IMU与磁力计的传感器融合是一种流行的系统设计，因为这种9-DoF（自由度）系统能够实现无漂移的3D定向跟踪。然而，这些系统通常容易受到环境磁失真的影响，并且缺乏有用的位置信息;在没有外部位置辅助（例如卫星/超宽带定位系统）的情况下，来自9自由度MEMS IMU的航位推算位置精度由于未建模误差而迅速恶化。定位信息在许多卫星拒绝的地理信息学应用（例如，室内导航、基于位置的服务等）中是有价值的。提出了一种改进的9自由度IMU室内位姿跟踪方法。通过采用鲁棒的原位用户自校准方法，在紧耦合后处理最小二乘框架中同时对加速度计、陀螺仪和磁力计的系统误差进行建模，可以提高9自由度MEMS IMU的估计轨迹的精度。通过相对磁测量更新和鲁棒的权重函数的组合，该方法能够容忍高水平的磁失真。在各种异质磁场条件下对所提出的自动校准方法进行了使用测试，以模拟一个人在口袋里带着传感器走路，一个人检查他们的手机，以及一个人带着智能手表走路。在这些实验中，所提出的算法提高了79.8 - 89.5%的原位航位推算的定位精度和72.9 - 92.8%的航位推算的定位精度，从而减少了相对定位误差从米级到分米级后，10秒的积分，而无需对用户的动态进行假设。
英文摘要：
Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8 - 89.5% and the dead-reckoned positioning accuracy by 72.9 - 92.8%, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user's dynamics.
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