Modern Breakthrough Technologies Enable New Applications Based on IMU Systems

Abstract

This paper describes IMU (Inertial Measurement Unit) platforms and their main target applications with a special focus on the 10-degree-of-freedom (10-DOF) inertial platform iNEMO and its technical features and performances. The iNEMO module is equipped with a 3-axis MEMS accelerometer, a 3-axis MEMS gyroscope, a 3-axis MEMS magnetometer, a pressure sensor, and a temperature sensor. Furthermore, the Microcontroller Unit (MCU) collects measurements by the sensors and computes the orientation through a customized Extended Kalman Filter (EKF) for sensor fusion.

Full text

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Hindawi Publishing Corporation
Journal of Sensors
Volume 2011, Article ID 707498, 7 pages
doi:10.1155/2011/707498
Research Article
ModernBreakthroughTechnologiesEnableNewApplications
Basedon IMUSystems
Nunzio Abbate,AdrianoBasile, CarmenBrigante,Alessandro Faulisi,
andFabrizioLaRosa
Automation, Robotics and Trasportation Group, Systems Lab & Technical Marketing, IMS Group (Industrial & Multisegment Sector),
STMicroelectronics, Stradale Primosole 50, Catania 95121, Italy
Correspondence should be addressed to Fabrizio La Rosa, fabrizio.larosa@st.com
Received 4 August 2010; Revised 22 November 2010; Accepted 4 January 2011
Academic Editor: Jiri Homola
Copyright © 2011 Nunzio Abbate et al.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper describes IMU (Inertial Measurement Unit) platforms and their main target applications with a special focus on the
10-degree-of-freedom (10-DOF) inertial platform iNEMO and its technical features and performances. The iNEMO module is
equipped with a 3-axis MEMS accelerometer, a 3-axis MEMS gyroscope, a 3-axis MEMS magnetometer, a pressure sensor, and
a temperature sensor. Furthermore, the Microcontroller Unit (MCU) collects measurements by the sensors and computes the
orientation through a customized Extended Kalman Filter (EKF) for sensor fusion.
1.Introduction
The continuous innovation in new technological processes
permits the Inertial Measurement Units (IMUs) to become
a fundamental part in a broad range of applications, from
the most typical ones such as dead reckoning and game
controllers to the last technological breakthrough sectors as
patients’ rehabilitation in medical segment or the Electronic
Stability Control (ESC) in the automotive segment.
The reason of this great success is mainly attributed to
two factors: first because of the MEMS-based technology
development that has significantly improved inertial sensors’
performances and strongly reduced package sizes, making
another step forward in the field of the system miniaturiza-
tion; then, because of the use of more reliable embedded
algorithms and calibration procedures, designed to enable
the convergence of several sensors in the same platform and
to make the system more robust.
Based on these assumptions, IMUs’ capability of char-
acterizing processes or environments has become a funda-
mental feature for the understanding and the development
of system solutions.
2.MainTarget Applications
IMUs represent a complete hardware solution for a variety of
applications including human machine interfaces, robotics,
platform stabilization, and virtual and augmented reality.
Today’s motion sensing technology, mixed with untradi-
tional algorithms, is enabling new levels of innovation in all
electronics markets.
For example, since its first appearance on the market,
multisensorial platforms have changed the way of playing
with the game consoles in a new dynamic mode. This has
been possible thanks to the data fusion among the different
sensors of the IMU used to implement the game controllers.
Data fusion among several sensors is also important for
navigation system solutions either in automotive applica-
tions or in pedestrian navigation systems used as handheld
devices [1]. In both cases, the IMU provides measurements
for controlling the three-dimensional position and orienta-
tion, as well as acceleration and angular rate measurement
thatcanbeusefultorecognizelinearmovementofthevehicle
in case of loss of GPS signal, if the platform is GPS assisted
[2].

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2Journal of Sensors
STLM75DS2F
Digital temperature sensor
and thermal watchdog
LSM303DLH
6-axis module:
accelerometer and
magnetometer
LY330ALH
MEMS Yaw
gyroscope
LD3985M18R and LDS3985M33R
Voltage regulators
LPS001DL
MEMS pressure sensor
LPR430AL
MEMS pitch and
roll gyroscope
STM32F103RET7
32-bit MCU
Figure 1: The iNEMO board.
The personal navigation systems performed with Pedes-
trian Dead Reckoning (PDR) systems are well-suited solu-
tions for indoor use or in urban environments where GPS
signals are degraded or not available [3]. Moreover the
integration of a pressure sensor in these units provides
further information in terms of altitude. Barometer data
are used to improve satellite-based vertical position and to
fix heights in order to strengthen the navigation system
functionality, because the accuracy of the barometer exceeds
that of GPS module.
In the automotive segment, last solution adopted by car
manufacturers is to integrate the Electronic Stability Control
system (ESC) with the Inertial Measurement Unit (IMU) to
perform data acquisition, previously made by stand-alone
sensors, directly within the ESC electronics module. This
design strategy is recognized as a viable way to reduce the
number of sensor modules in the vehicle while retaining the
performance of the ESC [4].
The Inertial Measurement Units can be also useful
as Human Machine Interface in industrial processes, to
increase workers’ safety avoiding any physical risk in objects
manipulation and environment interaction. IMUs are used
to assign cognition capability to industrial manipulators,
small smart arms, and exoskeleton parts, in order to help
people to better manage assembly processes.
Distributed sensors architecture for motion capture
would be hosted on different structure of robots, like
manipulators and rovers.
In the medical segment a growing attention has been
paid to IMUs as Patient Monitoring tool in order to build
monitoring networks for the patients and the elder people in
the hospitals and in their own houses.
Moreover, during the rehabilitation program [5, 6], it
should be useful to monitor the daily therapeutic activity
by remote. So, patients shall have all possible means to
improverehabilitationcareattheirdisposalattheirdomestic
environment [7–9] and to be properly monitored to check
the effectiveness of the therapy.
3.IMUHardwareStructure
In an Inertial Measurement Unit the inertial sensors and the
Microcontroller Unit (MCU) represent the core of the plat-
form. After data capturing, the MCU executes the Extended
Kalman Filter (EKF), a set of mathematical equations that
provides an efficient computational means able to minimize
the mean of the squared error.
The design of an inertial platform must follow several
requirements and constrains in order to have the best trade
off between performances, cost, and system’s flexibility to
cover a wide range of applications. iNEMO platform has
been designed following these guidelines in order to have a
modular solution based on the principles of miniaturization,
low power consumption, and cost-effectiveness.
The starting point for designing an inertial platform, as
described in [10], is the definition of the main components.
The iNEMO platform is provided with a 10-Degree-of-
Freedom (10-DOF) sensors system, so the products selection
is fundamental to mark out the system performances. For
this reason, the best MEMS-based sensors are selected to
develop the IMU presented in Figure 1 a 3-axis accelerome-
ter,a3-axismagnetometer,and3-axisgyroscopes.Apressure
sensor and a temperature sensor have been included in the
platform.
All these sensors are made by STMicroelectronics (ST),
and their characteristics are summarized in Table 1.
3.1. Geomagnetic Module. The 3-axis accelerometer and 3-
axis magnetometer are included in the LSM303DLH [11]

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Journal of Sensors3
Table 1: Sensors characteristics.
Sensor
Full scale
Sensitivity
Interface
Power supply
Accelerometer
±2 (g)
1mg/LSB
I2C
3.3V
Magnetometer
±2 (Gauss)
Gyroscope
±300 (Deg/s)
3.3 (mV/dps)
Analog
3.3V
I2C
3.3V
Power
management
2-axis gyro roll,
pitch LPR403AL
1-axis gyro Yaw
LY3xxALH
Pressure sensor
LPS001DL
Temperature
sensor STLM75
6-axis
accelerometer
magnetometer
LSM303DLH
User LED.
button
SWD/JTAG
MicroSD
card
Extended
connector
Serial connector
I2C
I2C
STM32F103RET
GPIO
Debug
ADC
SDIO
UART
USB
SPI
GPIO
Mini USB type B
connector
Figure 2: Block diagram of the iNEMO board.
geomagnetic module in 5 × 5× 1.5mm package. The
accelerometer part has a dynamically selectable full-scale
range of ±2g/±4g/±8g, the data output data rate is from
0.5Hz to 1kHz, in very small sizes (3 × 3 × 1mm). In
the accelerometer, the sensing element, capable of detecting
the acceleration, is manufactured using a dedicated process
developed by ST to produce inertial sensors and actuators in
silicon.
The magnetometer range is from ±1.3 to ±8.1 (gauss)
and the bandwidth is about 20Hz. The magnetometer
is based on a thin film trigate fluxgate for detecting a
component of a magnetic field in directions of three axes.
3.2. Gyroscopes. The iNEMO platform includes one 1-axis
Yaw gyro LY330ALH [12] and the biaxial Roll Pitch gyro
LPR5430AL [13].
The gyros have a miniaturized 3 × 5 × 1mm and 4 × 5 ×
1mm package, respectively, a full-scale range of ±300Deg/s
with a bandwidth of 140Hz, and sensitivity of 3.3mV/Deg/s.
Particularly output of LY330ALH [12] has a full scale of
±300◦/s and is capable of measuring rates with a −3dB
bandwidth up to 88Hz.
The combination of these sensors allows a compact
design with all the 3-axial gyro system in one planar layer.
The LPR430AL has a similar structure for each axis.
3.3. Pressure and Temperature Sensors. The LPS001DL pres-
sure sensor is the 300–1100mbar absolute full scale with I2C
digital output and barometer.
Figure 3: First iNEMO prototype platform (on the left) and the
iNEMO (on the right).
The STLM75 is the temperature sensor with –55 to
+125◦C range and I2C digital interface [14].
3.4. Microcontroller Unit. The STM32F103 MCU [15] col-
lects the data from the sensor and performs the EKF
algorithm. The MCU is a high-performance ARM Cortex-
M3 with 32-bit RISC core working at 72MHz, high-speed
embedded memories (flash memory up to 128Kbytes and
SRAM up to 20Kbytes), and an extensive range of enhanced
I/Os and peripherals connected to two APB buses.
3.5. Peripherals. The MCU polls the sensors at fixed fre-
quency, through I2C and ADC channel. After sensor fusion
process the data could be transferred to a collector through
a ZigBee wireless communication or through serial wired
communication. A MicroSD memory is also available for
data logging.
The board architecture is shown in Figure 2, while
Figure 3 shows the first platform prototype [10] and the
iNEMO [16].
4.ExtendedKalman Filter
In the IMU platform, a data fusion algorithm calculates the
orientation data, starting from the measurements of several
sensors. A set of mathematical equations, called Kalman fil-
ter, combines measurements coming from different sensors.
The Kalman filter provides an efficient computational
recursivemeanstoestimatethestateofaprocess,minimizing
the mean of the squared error. As very powerful tool, it
supports estimations of past, present, and even future states,
and it can do so even when the precise nature of the modeled
system is unknown.
Wheneitherthesystemstatedynamicsortheobservation
dynamics is nonlinear, the Extended Kalman Filter (EKF) is
adopted.
In this scenario, using several kinds of sensors, the char-
acteristics of each one overcome the limitation of the others.
So, while gyroscopes measure orientation by integrating
angular velocities, and the accelerometer and magnetometer
provide a noisy and disturbed but drift-free measurement of

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4Journal of Sensors
orientation,theEKFweightsthethreesourcesofinformation
in an appropriate way.
In this section, after a brief formulation of the discrete-
time EKF algorithm, it will be described the structure of the
quaternion-based EKF.
4.1.KalmanFilterFormulation. Ingeneral,theKalmanFilter
algorithm estimates the state of a discrete-time process
starting from the equations below:
xk+1= Akxk+Bkuk+wk,
zk= Hkxk+vk,
(1)
(2)
where
(i) xkis the state vector at the k time step, while zkis the
output,
(ii) A, B, and H are, respectively, state, input, and output
matrices,
(iii) w, v are state and measured noise. They are random,
Gaussian, and white noise source with covariance
matrix Q and R, respectively.
Equation (1) is the state equation while (4) is the output
equation. The vector x contains all of the information about
the present state of the system, but we cannot measure x
directly.
For every time step, the algorithm provides estimation
both for the state xkand for the error covariance Pk. This one
provides an indication of the uncertainty associated with the
current state estimate.
The updated measured equations (corrector equations)
provide a feedback by incorporating a new measured value
into the a priori estimate to get an improved a posteriori
estimate.
The equations for this recursive algorithm are shown in
Figure 4 [17].
The Kalman Gain K derives from the minimizing of the
a posteriori covariance error and could be considered as a
measure of the confidence level of the predicted state. In fact,
if R approaches zero, the actual measured z is more reliable
than the predicted measured H? x, while if P approaches zero,
4.2. Quaternion-Based EKF. In inertial systems, the ori-
entation obtained by integrating gyros’ data includes any
superimposed sensor drifts and noises. The orientation drift
errors caused by gyros can be reduced including additional
sensors (i.e., accelerometers and magnetometers).
In the present work, a classic state augmentation tech-
nique is applied to the process model, so the state vector
is composed by orientation and gyro bias. In this way the
earth’s gravitational and magnetic fields vectors are resolved
by the aiding system in the body frame, with their known
representation in the NED (North East Down) absolute
referenceframe[18].Quaternionsareusedtorepresentspace
orientationinordertoimprovecomputationalefficiencyand
avoid singularities [19]. After EKF running, the computed
the predicted measure is more dependable.
Initial estimates for ? xkand Pk−1
Time update (“predict”)
(1) Project the state ahead
? x−
(2) Project the error covariance ahead
P−
k= A? x−
k−1+Buk
Measurement update (“correct”)
k= APk−1AT+Q
(1) Compute the Kalman gain
Kk= P−
(2) Project the error covariance ahead
? xk= ? xk+Kk(zk−H? x−
(3) Update the error covariance
Pk= (I −KkH)P−
kHT(HP−
kHT+R)−1
k)
k
Figure 4: Flow diagram of the time-discrete Kalman filter: at each
time step k, the “Time update” projects the current state estimation
ahead in time. The “Measurement update” adjusts the projected
estimation by an actual measurement.
quaternions have to be translated into Roll, Pitch, and Yaw
angles, through transformation equations [20].
The continuous-time, nonlinear system equations are
[21]
˙ x = f(x,ω)+w,
y = h(x)+v,
(3)
(4)
where
(i) x = [qbw]Trepresents the state of the system that is
composed of the quaternion q = [q0q1q2q3] and the
rate gyro bias bω= [bωxbωybωz],
(ii) ω is the angular rates vector [ωxωyωz],
(iii) y = [am] is the measurement vector composed by
acceleration measurements [axayaz] and magnetic
field [mxmymz].
Nonlinear functions, f(x,ω), and h(x) in (1) and (2) can
be explained as
⎡
⎢
⎢
⎢
⎡
0
f (x,ω) =
⎢
⎢
⎢
⎢
⎢
⎣
⎢
⎢
1
2
⎡
⎢
⎢
⎢
⎢
⎢
⎣
⎢
−q1 −q2 −q3
q0
−q3
q3
q0
q2
−q1
q0
−q2
q1
⎤
⎥
⎥
⎥
⎥
⎥
⎦
⎥
⎡
⎢
⎢
⎢
⎣
ωx−bwx
ωy−bwy
ωz−bwz
⎤
⎥
⎥
⎥
⎦
03x1
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦
⎥
⎥
, (5)
h(x) =
⎣Cb
n
?q?
0
?q?
Cb
n
⎤
⎦
⎡
⎣
− →g
− →
m
⎤
⎦.
(6)
Because of its nonlinearity, the system is linearized
calculatingtheJacobianof f andhfunctions:soanExtended

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Journal of Sensors5
Gyros
Accelerometer
Magnetometer
State
estimation
Roll-pitch
correction
Yaw
correction
ωx
ωy
ωz
ax
ay
az
mx
my
mz
? q
?bω
? q?
?b?ω
q
bω
Figure 5: Block diagram of system including the implemented quaternion-based EKF.
−200
−150
−100
−50
0
50
100
50
Angle (degree)
55 60
Developed IMU
Commercial IMU
6570 75
Time (s)
Roll angle
80859095100
150
105
200
(a)
−100
−80
−60
−40
−20
0
50
Angle (degree)
5560
Developed IMU
Commercial IMU
65 7075
Time (s)
Pitch angle
80 8590 95 100
20
105
40
(b)
−200
−150
−100
−50
0
50
100
Angle (deg)
60
Developed IMU
Commercial IMU
Time (s)
Yaw angle
80100
150
200
(c)
Figure 6: Trend of the Roll (a), Pitch (b), and Yaw (c) angles measured from the iNEMO (dotted line) and from commercial IMU (solid
line).