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Indoor Pedestrian Navigation using an INS/EKF framework for Yaw Drift Reduction and a Foot-mounted IMU

AuthorsJiménez Ruiz, Antonio R. CSIC ORCID; Seco Granja, Fernando CSIC ORCID; Prieto, José Carlos CSIC; Guevara, Jorge CSIC
Issue Date2010
PublisherInstitute of Electrical and Electronics Engineers
CitationWPNC 2010: 7th Workshop on Positioning, Navigation and Communication
AbstractThe estimation of the position of a person in a building is a must for creating Intelligent Spaces. State-of-theart Local Positioning Systems (LPS) require a complex sensornetwork infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we describe and implement a Kalman-based framework, called INSEKF- ZUPT (IEZ), to estimate the position and attitude of a person while walking. IEZ makes use of an Extended Kalman filter (EKF), an INS mechanization algorithm, a Zero Velocity Update (ZUPT) methodology, as well as, a stance detection algorithm. As the IEZ methodology is not able to estimate the heading and its drift (non-observable variables), then several methods are used for heading drift reduction: ZARU, HDR and Compass. The main contribution of the paper is the integration of the heading drift reduction algorithms into a Kalman-based IEZ platform, which represents an extended PDR methodology (IEZ+) valid for operation in indoor spaces with local magnetic disturbances. The IEZ+ PDR methodology was tested in several simulated and real indoor scenarios with a low-performance IMU mounted on the foot. The positioning errors were about 1% of the total travelled distance, which are good figures if compared with other works using IMUs of higher performance.
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Appears in Collections:(CAR) Comunicaciones congresos

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