Aerospace and Electronic Systems - August 2018 - 30

Feature Article:

DOI. No. 10.1109/MAES.2018.170153

Use of a New Online Calibration Platform With
Applications to Inertial Sensors
Philipp Clausen, Jan Skaloud, École Polytechnique Fédérale de Lausanne, Switzerland
Roberto Molinari, Justin Lee, Stéphane Guerrier, Pennsylvania State University, PA, USA

INTRODUCTION
In many fields, going from economics to physics, it is common
to deal with measurements that are taken in time. These measurements are often explained by known external factors that describe
a large part of their behavior. For example, the evolution of the
unemployment rate in time can be explained by the behavior of
the gross domestic product (the external factor in this case). However, in many cases the external factors are not enough to explain
the entire behavior of the measurements, and it is necessary to use
so-called stochastic models (or probabilistic models) that describe
how the measurements are dependent on each other through time
(i.e., the measurements are explained by the behavior of the previous measurements themselves). The treatment and analysis of the
latter kind of behavior is known by various names, such as timeseries analysis or signal processing. In the majority of cases, the
goal of this analysis is to estimate the parameters of the underlying
models which, in some sense, explain how and to what extent the
observations depend on each other through time.
In the field of engineering there exist many settings where this
type of analysis is essential. One of these is the calibration of socalled inertial sensors that are small devices used in many commercial and noncommercial applications for navigation purposes.
Indeed, the attitude (or orientation) and position of a device can be
computed by integrating different kinds of sensor data in a filter,
which therefore can be described as a tool that combines the information from the sensors. One example of a sensor is the Inertial
Measurement Unit (IMU) and one example of a fusion technique
(i.e., the method to combine the information from the sensors) is
the Kalman Filter (KF). The IMU is composed of a three-axisaccelerometer in combination with a three-axis-gyroscope, which
provides measurements of specific force and rotational speed,
Authors' current addresses: P. Clausen, J. Skaloud, Geodetic
Engineering Laboratory, École Polytechnique Fédérale de
Lausanne, 1015 Lausanne, Switzerland, E-mail: (philipp.
clausen@epfl.ch). R. Molinari, J. Lee, S. Guerrier, Department
of Statistics, Pennsylvania State University, University Park, PA
16802, USA; S. Guerrier, Institute for CyberScience, Pennsylvania State University, University Park, PA 16802, USA
Manuscript received July 30, 2017, revised November 27, 2017,
and ready for publication November 29, 2017.
Review handled by P. Daponte.
0885/8985/18/$26.00 © 2018 IEEE
30

respectively. The integration of this information with other sensors (e.g., odometer, barometer, Global Positioning System (GPS))
allows the increase of the performances, which could never be
achieved individually.
There are different types of IMUs, but one characteristic that
distinguishes them is their level of measurement precision. Indeed,
the so-called high-end-IMUs (i.e., navigational grade) have excellent properties regarding their errors and measurement-noise, meaning that they measure attitude and position with almost perfect precision. These are, for instance, used in laser-scanning-devices from
airplanes or helicopters. However, these devices are very heavy,
costly, and require a large amount of power. The emergence of new
technologies has allowed the creation of smaller devices based on
other physical properties, of which the main example of low-cost
mass-market sensors is given by the Micro-Electro-MechanicalSystem (MEMS). This type of device is based on silicon, leaves
a very small footprint, and is used almost everywhere (e.g., step
counter in a smartwatch, gamepad in a smartphone, fall-detection
in a hard disk). The fact that they are employed in a wide variety of
settings and that they are easily accessible opens up the window for
new applications that have never been considered before. One of
these fields of application is navigation where, for example, small
autopilots employed within micro-aerial-vehicles use MEMSIMUs to compute the attitude and position. These sensors are also
widely used in fields like robotics and car-navigation (see Figure 1).
Although good results are achieved through the integration of these
different sensors in a properly tuned KF, these MEMS-IMUs are
not as precise as the high-end IMUs and suffer from various degrees
of errors when measuring attitude and position.

Figure 1.

Left: Two Navchip IMUs mounted on a sensor board. Right: XSENS
MTi-G used in different robotic applications.

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AUGUST 2018

Aerospace and Electronic Systems - August 2018

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