Position, Location, and Navigation (PLAN) Group, Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive, N.W., Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2019 Jul 17;19(14):3140. doi: 10.3390/s19143140.
This paper demonstrates the use of multiple low-cost inertial/magnetic sensors as a pedestrian navigation system for indoor positioning. This research looks at the problem of pedestrian navigation in a practical manner by investigating dead-reckoning methods using low-cost sensors. This work uses the estimated sensor orientation angles to compute the step size from the kinematics of a skeletal model. The orientations of limbs are represented by the tilt angles estimated from the inertial measurements, especially the pitch angle. In addition, different step size estimation methods are compared. A sensor data logging system is developed in order to record all motion data from every limb segment using a single platform and similar types of sensors. A skeletal model of five segments is chosen to model the forward kinematics of the lower limbs. A treadmill walk experiment with an optical motion capture system is conducted for algorithm evaluation. The mean error of the estimated orientation angles of the limbs is less than 6 degrees. The results show that the step length mean error is 3.2 cm, the left stride length mean error is 12.5 cm, and the right stride length mean error is 9 cm. The expected positioning error is less than 5% of the total distance travelled.
本文展示了如何使用多个低成本惯性/磁力传感器作为室内定位的行人导航系统。本研究通过研究使用低成本传感器的推算方法，以实际的方式研究行人导航问题。本工作使用估计的传感器方位角从骨骼模型的运动学计算步长。肢体的方位由从惯性测量中估计的倾斜角表示，特别是俯仰角。此外，还比较了不同的步长估计方法。为了记录使用单个平台和类似类型的传感器的每个肢体段的所有运动数据，开发了一个传感器数据记录系统。选择一个五段骨骼模型来对下肢的运动学进行正向建模。进行了带有光学运动捕捉系统的跑步机行走实验，以对算法进行评估。肢体估计方位角的平均误差小于 6 度。结果表明，步长的平均误差为 3.2 厘米，左腿步长的平均误差为 12.5 厘米，右腿步长的平均误差为 9 厘米。预期的定位误差小于总行程的 5%。
