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Document MT1600P.2019.A
© Xsens Technologies B.V.
MTi Family Reference Manual
4.4 Xsens Sensor Fusion Algorithms
The orientation and position output of the VRU, AHRS and GNSS/INS are computed by Xsens
’
proprietary sensor fusion algorithm. It uses signals of the rate gyroscopes, accelerometers,
magnetometers and optionally a GNSS receiver and barometer to compute a statistical optimal 3D
orientation and position estimates of high accuracy without drift for both static and dynamic movements.
The design of a typical algorithm can be summarized as a sensor fusion algorithm where the
measurement of gravity (by the 3D accelerometers) and Earth magnetic north (by the 3D
magnetometers) compensate for otherwise slowly, but unlimited, increasing (drift) errors from the
integration of rate of turn data (angular velocity from the rate gyroscope). This type of drift compensation
is often called attitude and heading referencing and such a system is referred to as an Attitude and
Heading Reference System (AHRS).
In products where a GNSS receiver is available, GNSS data is continuously used to aid the estimation
of the
device’s
roll, pitch and heading next to position and velocity. An additional benefit is that short
term GNSS outages can be coped with, through dead-reckoning, ensuring continuous data output. Such
a system is referred to as GNSS/INS.
4.4.1 Internal Sensor Bias Estimation
The Xsens algorithm continuously estimates the gyroscope bias. For the rate of turn around the x-axis
and the y-axis (roll and pitch axes), the gyroscope bias is estimated using gravity (accelerometers). In
a homogenous magnetic field and with filter profiles using the magnetometer, also the gyroscope bias
around the z-axis will successfully be estimated.
In some situations, the heading cannot be referenced to the (magnetic) north. This is the case when the
magnetic field is not used (for example for VRU devices) or when the magnetic field is distorted. There
are several ways to mitigate the drift in yaw (rotation around the z-axis):
1. When the MTi has sufficient movement in roll and pitch (>30 degrees for more than 10 seconds),
the gyroscope bias will be estimated for the z-gyroscope. When rotating the MTi back to roll and
pitch around 0 degrees, the yaw will be more stable than before the roll/pitch movements.
2. The yaw drift can also be stabilized by using Active Heading Stabilization (AHS). Refer to section
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of this document for more details.
3. It is also possible to estimate the gyro bias using the manual gyro bias estimation when the MTi
does not rotate (also called no-rotation update). A tutorial of the Manual Gyro Bias Estimation
is available online through the following link:
BASE by Xsens - Manual Gyro Bias Estimation
4.4.2 Roll and Pitch estimation
The Xsens sensor fusion algorithm stabilizes the inclination (i.e. roll and pitch combined) using the
accelerometer signals. An accelerometer measures the specific force that is composed of the
gravitational acceleration plus the linear acceleration due to the movement of the object with respect to
its surroundings. The algorithm uses the assumption that on average the acceleration due to the
movement is zero. Using this assumption, the direction of the gravity can be observed and used to
stabilize the attitude. The orientation of the MTi in the gravity field is accounted for such that centripetal
accelerations or asymmetrical movements cannot cause a degraded orientation estimate performance.
The key here is the amount of time over which the acceleration must be averaged for the assumption to
hold. During this time, the gyroscopes must be able to track the orientation to a high degree of accuracy.
In practice, this limits the amount of time over which the assumption holds true.
However, for some applications this assumption does not hold. For example, an accelerating automobile
may generate significant permanent accelerations for time periods lasting longer than the maximum
duration the MT’s rate gyroscopes can reliably keep track of the orientation. This
may degrade the
