Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking.
Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking.
复制标题
惯性/磁传感器测量与地图信息的融合用于行人跟踪
DOI：
10.3390/s17020340
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发表时间：
2017-02-10
期刊：
影响因子：
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通讯作者：
Zhang ZQ
中科院分区：
文献类型：
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作者：
Bao SD;Meng XL;Xiao W;Zhang ZQ
基于可穿戴惯性/磁性传感器的人体运动分析在物理治疗、步态分析、康复等生物医学领域有着重要的应用。下半身生物运动分析的主要挑战之一是如何可靠地提供人体在行走过程中的位置估计。本文提出了一种基于粒子滤波的脚载式惯性磁传感器人体位置估计方法，该方法不仅使用了传统的零速度更新(ZUPT)算法，而且利用MAP信息进一步修正了加速度双积分漂移，从而提高了估计精度。在所提出的方法中，设计了一个简单的姿态相位检测器来根据陀螺仪测量来识别步态周期的姿态相位。对于步态周期中的非站姿阶段，在过程模型中引入了基于ZUPT信息的加速度控制变量，同时将矢量地图信息作为二进制伪量测，进一步提高了位置估计的精度，降低了行走轨迹的不确定性。然后设计了一种粒子滤波器，将ZUPT信息和二值伪量测融合在一起。通过室内和室外环境下的闭环行走实验对所提出的人体位置估计方法进行了评估。对比研究的结果表明，该方法对具有有用地图信息的应用场景是有效的。
The wearable inertial/magnetic sensor based human motion analysis plays an important role in many biomedical applications, such as physical therapy, gait analysis and rehabilitation. One of the main challenges for the lower body bio-motion analysis is how to reliably provide position estimations of human subject during walking. In this paper, we propose a particle filter based human position estimation method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity update (ZUPT), but also applies map information to further correct the acceleration double integration drift and thus improve estimation accuracy. In the proposed method, a simple stance phase detector is designed to identify the stance phase of a gait cycle based on gyroscope measurements. For the non-stance phase during a gait cycle, an acceleration control variable derived from ZUPT information is introduced in the process model, while vector map information is taken as binary pseudo-measurements to further enhance position estimation accuracy and reduce uncertainty of walking trajectories. A particle filter is then designed to fuse ZUPT information and binary pseudo-measurements together. The proposed human position estimation method has been evaluated with closed-loop walking experiments in indoor and outdoor environments. Results of comparison study have illustrated the effectiveness of the proposed method for application scenarios with useful map information.