Abstract
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.
Highlights
Development of micro-electromechanical systems (MEMS) has enabled miniaturization of many sensors
Forward kinematics allow the estimation of positions of joints given a skeletal model
This figure clearly shows that the trailing shank pitch angle does have a significant effect on the step size computation
Summary
Development of micro-electromechanical systems (MEMS) has enabled miniaturization of many sensors. The common sensors used in navigation applications are accelerometers, gyroscopes, magnetometers, and barometers. MEMS technology has enabled these sensors to be miniaturized to the level of an integrated circuit chip with very low weight, power consumption, and cost and these devices are commonly found as embedded sensors inside smartphones and fitness trackers. Pedestrian navigation systems have received much attention due to the emergence of MEMS sensors, which have allowed the pedestrian to carry inertial measurement units. An inertial sensor usually consists of an accelerometer triad and a gyroscope triad. In the early development of pedestrian navigation systems, the Defence Advanced Research Project Agency (DARPA) demonstrated a Small Unit Operations/Situation Awareness System (SUO/SAS) for tracking military personnel [1]
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