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Your search keyword '"Indoor Pedestrian Navigation"' showing total 3 results
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3 results on '"Indoor Pedestrian Navigation"'

2. Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter.

Author

Chunyang Yu, El-Sheimy, Naser, Haiyu Lan, and Zhenbo Liu

Subjects
MICROELECTROMECHANICAL systems, KALMAN filtering
Abstract

In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system's accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios. [ABSTRACT FROM AUTHOR]

Published
2017
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3. Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter

Author

Naser El-Sheimy, Zhenbo Liu, Haiyu Lan, and Chunyang Yu

Subjects
Engineering, Inertial frame of reference, map aiding, lcsh:Mechanical engineering and machinery, indoor pedestrian navigation, 02 engineering and technology, Map matching, Simultaneous localization and mapping, 01 natural sciences, Article, Computer Science::Robotics, lcsh:TJ1-1570, Electrical and Electronic Engineering, Simulation, Auxiliary particle filter, auxiliary particle filter, business.industry, Mechanical Engineering, 010401 analytical chemistry, map information, MEMS sensors, map matching, cascade structure algorithm, Kalman filter, 021001 nanoscience & nanotechnology, 0104 chemical sciences, Control and Systems Engineering, Cascade, Filter (video), 0210 nano-technology, Particle filter, business
Abstract

In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system's accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios.

Published
2017
Full Text
View/download PDF

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