2024-03-28T19:30:21Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/322152016-04-13T11:58:16Zcom_10261_31565com_10261_4col_10261_31569
Jiménez Ruiz, Antonio R.
Seco Granja, Fernando
Prieto, José Carlos
Guevara, Jorge
2010
WPNC 2010: 7th Workshop on Positioning, Navigation and Communication
10261/32215
10.1109/WPNC.2010.5649300
The 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.
eng
openAccess
Indoor Pedestrian Navigation using an INS/EKF framework for Yaw Drift Reduction and a Foot-mounted IMU
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