Abstract
One of the common ways for solving indoor navigation is known as Pedestrian Dead Reckoning (PDR), which employs inertial and magnetic sensors typically embedded in a smartphone carried by a user. Estimation of the pedestrian’s heading is a crucial step in PDR algorithms, since it is a dominant factor in the positioning accuracy. In this paper, rather than assuming the device to be fixed in a certain orientation on the pedestrian, we focus on estimating the vertical direction in the sensor frame of an unconstrained smartphone. To that end, we establish a framework for gravity direction estimation and highlight the important role it has for solving the heading in the horizontal plane. Furthermore, we provide detailed derivation of several approaches for calculating the heading angle, based on either the gyroscope or the magnetic sensor, all of which employ the estimated vertical direction. These various methods—both for gravity direction and for heading estimation—are demonstrated, analyzed and compared using data recorded from field experiments with commercial smartphones.
Highlights
Indoor navigation for pedestrians is essential for various location-based applications, such as commercial and emergency services
Solution indoors, a common approach to handle this task is to use the inertial and magnetic sensors embedded in smartphones or other wearable devices, in a framework known as pedestrian dead reckoning (PDR) [1]
We suggest averaging the measurements within the stationary interval instead of applying the low-pass filter, obtaining a single gravity direction estimation, γs, that will be used as initialization for the procedure described
Summary
Indoor navigation for pedestrians is essential for various location-based applications, such as commercial and emergency services. Solution indoors, a common approach to handle this task is to use the inertial and magnetic sensors embedded in smartphones or other wearable devices, in a framework known as pedestrian dead reckoning (PDR) [1]. Typical PDR algorithms consist of estimating pedestrian’s step length and heading angle. These are combined, along with initial conditions, in order to calculate the pedestrian’s trajectory in a horizontal coordinate system. It was shown that motion mode classification (pedestrian mode [2] and smartphone mode [3]) improves positioning accuracy in PDR by enabling a proper gain selection for the step length estimation phase
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
