Abstract
Progress has been made enabling expensive, high-end inertial measurement units (IMUs) to be used as tracking sensors. However, the cost of these IMUs is prohibitive to their widespread use, and hence the potential of low-cost IMUs is investigated in this study. A wearable low-cost sensing system consisting of IMUs and ultrasound sensors was developed. Core to this system is an extended Kalman filter (EKF), which provides both zero-velocity updates (ZUPTs) and Heuristic Drift Reduction (HDR). The IMU data was combined with ultrasound range measurements to improve accuracy. When a map of the environment was available, a particle filter was used to impose constraints on the possible user motions. The system was therefore composed of three subsystems: IMUs, ultrasound sensors, and a particle filter. A Vicon motion capture system was used to provide ground truth information, enabling validation of the sensing system. Using only the IMU, the system showed loop misclosure errors of 1% with a maximum error of 4–5% during walking. The addition of the ultrasound sensors resulted in a 15% reduction in the total accumulated error. Lastly, the particle filter was capable of providing noticeable corrections, which could keep the tracking error below 2% after the first few steps.
Highlights
With the recent advances in portable sensing techniques, the use of wearable sensors for pedestrian navigation has been attracting increasing attention
A Vicon motion capture system was unavailable, and the performance of the wearable sensing system was assessed in terms of the final loop misclosure
Step Number (b) and inertial/ultrasound/particle filter (IUP) systems for trial 1 of Type 1 walking compared to the ground truth position measured simultaneously by a Vicon motion capture system. (b) The absolute errors of the Inertial measurement units (IMUs), IMU/US and IUP systems at each single step for trial 1 of Type 1 walking. (c) The percentage errors of the IMU, IMU/US and IUP systems at each single step for trial 1 of Type 1 walking
Summary
With the recent advances in portable sensing techniques, the use of wearable sensors for pedestrian navigation has been attracting increasing attention. Such systems have numerous applications, such as healthcare monitoring, intelligent environments [1], lower-limb prosthetic tracking, assisted navigation [2], and emergency responder localisation. Inertial measurement units (IMUs) are ubiquitously used in these systems and can be combined with other sensors such as ultrasound, barometers, and magnetometers. IMUs based on microelectromechanical system (MEMS) technology are normally used. Even high-end commercial MEMS IMUs, such as those produced by Xsens [3], suffer from significant drift over time, and various algorithms have been developed to reduce the accumulated errors
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