General Meeting on Oct 3, 2013. Keynote Speaker: Tom Jakel from the Univ of MN

The next general meeting of the ION North Star Section will be
Tuesday, Oct 3th in Mechanical Engineering Building Room 1130 at the
University of Minnesota (http://campusmaps.umn.edu/tc/map.php?building=265),
starting at 6:00 PM.

Tom Jakel of the University
of Minnesota will be the keynote speakers. Tom will give a presentation entitled  “Use of Trunk Roll Constraint to Improve Heading Estimation in Pedestrian Dead  Reckoning Navigation Systems.” This presentation is co-authored with Demoz Gebre-Egziabher of the University of Minnesota

Sandwiches and beverages will be served during the Meet and Greet
portion of the meeting.

Please email us at ion.nortstar@gmail.com, if you plan to attend.

Agenda:

6:00 Meet and Greet

6:30 Keynote Speaker

7:45 Section Business

8:00 Final remarks

Keynote presentation:

Title: Use of Trunk Roll Constraint to Improve Heading Estimation in Pedestrian Dead
Reckoning Navigation Systems:

Authors: T. Jakel ,Honeywell; D. Gebre-Egziabher, University of Minnesota, Twin Cities

 

Abstract:  A  method for increasing the accuracy of the
heading estimate in IMU-based personal navigation system will be discussed.
Heading estimation without use or emittance of any external electromagnetic
signals for reference is a critical but challenging component of pedestrian
dead reckoning navigation systems. In the environments where these systems are
normally used, electromagnetic signals may be unavailable. Even when available,
these signals may be intentionally or inadvertently corrupted for an extended
period of time. In such environments, the navigation system must determine
initial heading through gyro-compassing or using information from other sensors
such as magnetometers or radio frequency multilateration systems. Subsequently,
these external measurements may be used periodically to arrest the heading
drift when signal conditions and trajectory allow it.

Due to the size, weight, power, and cost constraints
imposed on a pedestrian navigation systems as well as current IMU performance
limitations, the gyroscopes used to determine heading have significant drift.
To deal with this drift problem without having to rely on information external
to the navigator, the use of human motion models as constraints has been
proposed. For example, one such motion model used as a constraint predicts the
onset of turning motion of the pedestrian by thresholding the outputs of the
yaw gyroscope. For example, unless a significant yaw rate is detected, the user
is assumed to be moving in a straight line. This gyroscope is also integrated
to determine the change in angle over the period of turning, such constraints delay
the open loop gyro integration time onset which increases the accumulated
drift, especially for sudden turns, which can occur during indoor navigation.

The use of the trunk roll angle and angular rate are
presented as additional signals used to predict the onset of pedestrian turning
motion. The signature of the trunk roll motion relative to foot and body
reorientation is described. Integration of the trunk roll motion based turning
prediction method with existing yaw threshold methods is described and
analyzed. Experimental data from ten subjects was captured in a gait
laboratory, where a Vicon motion tracking unit is used for validation. In these
experiments the subjects were instrumented with five low cost IMU units; one on
the right foot, one on the right ankle, one on the right thigh, one on the
lower back and one on the chest. The outputs of the foot-mounted IMUs are used
with Zero Velocity Updates (ZUPTs) to determine the subject’s gross body
motion. The torso mounted IMUs are used to sense upper body motion and this
information is used in activating the trunk-roll constraint. The analysis of
experimental data demonstrates that trunk rolling motion precedes heading
change and can be used together with yaw rate threshold methods to predict the
onset of a turning motion. This technique provides a smaller latency in the
turn detection, which produces a more accurate open-loop gyro integration time
leading to lower accumulated gyroscope drift.