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Probabilistic modeling for sensor fusion with inertial measurements
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In recent years, inertial sensors have undergone major developments. The quality of their measurements has improved while their cost has decreased, leading to an increase in availability. They can be found in stand-alone sensor units, so-called inertial measurement units, but are nowadays also present in for instance any modern smartphone, in Wii controllers and in virtual reality headsets.

The term inertial sensor refers to the combination of accelerometers and gyroscopes. These measure the external specific force and the angular velocity, respectively. Integration of their measurements provides information about the sensor's position and orientation. However, the position and orientation estimates obtained by simple integration suffer from drift and are therefore only accurate on a short time scale. In order to improve these estimates, we combine the inertial sensors with additional sensors and models. To combine these different sources of information, also called sensor fusion, we make use of probabilistic models to take the uncertainty of the different sources of information into account. The first contribution of this thesis is a tutorial paper that describes the signal processing foundations underlying position and orientation estimation using inertial sensors.

In a second contribution, we use data from multiple inertial sensors placed on the human body to estimate the body's pose. A biomechanical model encodes the knowledge about how the different body segments are connected to each other. We also show how the structure inherent to this problem can be exploited. This opens up for processing long data sets and for solving the problem in a distributed manner.

Inertial sensors can also be combined with time of arrival measurements from an ultrawideband (UWB) system. We focus both on calibration of the UWB setup and on sensor fusion of the inertial and UWB measurements. The UWB measurements are modeled by a tailored heavy-tailed asymmetric distribution. This distribution naturally handles the possibility of measurement delays due to multipath and non-line-of-sight conditions while not allowing for the possibility of measurements arriving early, i.e. traveling faster than the speed of light.

Finally, inertial sensors can be combined with magnetometers. We derive an algorithm that can calibrate a magnetometer for the presence of metallic objects attached to the sensor. Furthermore, the presence of metallic objects in the environment can be exploited by using them as a source of position information. We present a method to build maps of the indoor magnetic field and experimentally show that if a map of the magnetic field is available, accurate position estimates can be obtained by combining inertial and magnetometer measurements.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. , p. 46
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1814
National Category
Control Engineering Medical Laboratory and Measurements Technologies Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-133083DOI: 10.3384/diss.diva-133083ISBN: 9789176856215 (print)OAI: oai:DiVA.org:liu-133083DiVA, id: diva2:1054718
Public defence
2017-01-13, Visionen, House B, Campus Valla, Linköping, 10:15 (English)
Supervisors
Funder
EU, FP7, Seventh Framework ProgrammeSwedish Research Council
Available from: 2016-12-15 Created: 2016-12-09 Last updated: 2016-12-15Bibliographically approved
List of papers
1. Using inertial sensors for position and orientation estimation
Open this publication in new window or tab >>Using inertial sensors for position and orientation estimation
2016 (English)Report (Other academic)
Abstract [en]

In recent years, micro-machined electromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors, discussing different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter implementations.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2016. p. 79
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3093
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-133169 (URN)LiTH-ISY-R-3093 (ISRN)
Projects
CADICSThe project Probabilistic modeling of dynamical systems (Contract number: 621- 2013-5524)ASSEMBLE
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research
Available from: 2016-12-12 Created: 2016-12-12 Last updated: 2017-04-27Bibliographically approved
2. An optimization-based approach to human body motion capture using inertial sensors
Open this publication in new window or tab >>An optimization-based approach to human body motion capture using inertial sensors
2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014 / [ed] Boje, Edward; Xia, Xiaohua, International Federation of Automatic Control , 2014, p. 79-85Conference paper, Published paper (Refereed)
Abstract [en]

In inertial human motion capture, a multitude of body segments are equipped with inertial measurement units, consisting of 3D accelerometers, 3D gyroscopes and 3D magnetometers. Relative position and orientation estimates can be obtained using the inertial data together with a biomechanical model. In this work we present an optimization-based solution to magnetometer-free inertial motion capture. It allows for natural inclusion of biomechanical constraints, for handling of nonlinearities and for using all data in obtaining an estimate. As a proof-of-concept we apply our algorithm to a lower body configuration, illustrating that the estimates are drift-free and match the joint angles from an optical reference system.

Place, publisher, year, edition, pages
International Federation of Automatic Control, 2014
Series
World Congress, ISSN 1474-6670 ; World Congress, Volume# 19 | Part# 1
Keyword
Human body motion capture, optimization, maximum a posteriori estimation, inertial sensors, 6D pose estimation.
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-111543 (URN)10.3182/20140824-6-ZA-1003.02252 (DOI)978-3-902823-62-5 (ISBN)
Conference
19th World Congress of the International Federation of Automatic Control (IFAC), Cape Town, South Africa, August 24-29, 2014
Projects
MC ImpulseCADICSBALANCE
Funder
EU, FP7, Seventh Framework Programme, 1933031801Swedish Research Council, 1933011102
Available from: 2014-10-22 Created: 2014-10-22 Last updated: 2016-12-15Bibliographically approved
3. A Scalable and Distributed Solution to the Inertial Motion Capture Problem
Open this publication in new window or tab >>A Scalable and Distributed Solution to the Inertial Motion Capture Problem
Show others...
2016 (English)In: Proceedings of the 19th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1348-1355Conference paper, Published paper (Refereed)
Abstract [en]

In inertial motion capture, a multitude of body segments are equipped with inertial sensors, consisting of 3D accelerometers and 3D gyroscopes. Using an optimization-based approach to solve the motion capture problem allows for natural inclusion of biomechanical constraints and for modeling the connection of the body segments at the joint locations. The computational complexity of solving this problem grows both with the length of the data set and with the number of sensors and body segments considered. In this work, we present a scalable and distributed solution to this problem using tailored message passing, capable of exploiting the structure that is inherent in the problem. As a proof-of-concept we apply our algorithm to data from a lower body configuration. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130555 (URN)000391273400178 ()978-0-9964-5274-8 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016
Projects
CADICSELLIITThe project Probabilistic modeling of dynamical systems (Contract number: 621- 2013-5524)
Funder
Swedish Research CouncilELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2016-08-16 Created: 2016-08-16 Last updated: 2017-02-03
4. Indoor Positioning Using Ultrawideband and Inertial Measurements
Open this publication in new window or tab >>Indoor Positioning Using Ultrawideband and Inertial Measurements
2015 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 64, no 4, p. 1293-1303Article in journal (Refereed) Published
Abstract [en]

In this paper, we present an approach to combine measurements from inertial sensors (accelerometers and gyroscopes) with time-of-arrival measurements from an ultrawideband (UWB) system for indoor positioning. Our algorithm uses a tightly coupled sensor fusion approach, where we formulate the problem as a maximum a posteriori (MAP) problem that is solved using an optimization approach. It is shown to lead to accurate 6-D position and orientation estimates when compared to reference data from an independent optical tracking system. To be able to obtain position information from the UWB measurements, it is imperative that accurate estimates of the UWB receivers positions and their clock offsets are available. Hence, we also present an easy-to-use algorithm to calibrate the UWB system using a maximum-likelihood (ML) formulation. Throughout this work, the UWB measurements are modeled by a tailored heavy-tailed asymmetric distribution to account for measurement outliers. The heavy-tailed asymmetric distribution works well on experimental data, as shown by analyzing the position estimates obtained using the UWB measurements via a novel multilateration approach.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015
Keyword
Calibration; heavy-tailed noise distribution; inertial sensors; sensor fusion; ultrawideband (UWB)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-118060 (URN)10.1109/TVT.2015.2396640 (DOI)000353111900004 ()
Note

Funding Agencies|Control, Autonomy, and Decision-making In Complex Systems (CADICS): a Linnaeus Center - Swedish Research Council (VR); BALANCE: a European Commission FP7 Research Project; Swedish Research Council (VR) [621-2013-5524]

Available from: 2015-05-20 Created: 2015-05-20 Last updated: 2017-12-04
5. Magnetometer calibration using inertial sensors
Open this publication in new window or tab >>Magnetometer calibration using inertial sensors
2016 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 16, no 14, p. 5679-5689Article in journal (Refereed) Published
Abstract [en]

In this work we present a practical calibration algorithm that calibrates a magnetometer using inertial sensors. The calibration corrects for magnetometer sensor errors, for the presence of magnetic disturbances and for misalignment between the magnetometer and the inertial sensor axes. It is based on a maximum likelihood formulation and is formulated as an offline method. It is shown to give good results using data from two different commercially available sensor units. Using the calibrated magnetometer measurements in combination with the inertial sensors to determine orientation, is shown to lead to significantly improved heading estimates.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-106879 (URN)10.1109/JSEN.2016.2569160 (DOI)000379601600024 ()
Note

Funding agencies: Funding Agencies|CADICS; Project Probabilistic Modeling of Dynamical Systems through the Swedish Research Council (Vetenskapsradet) [621-2013-5524]; MC Impulse through the European Commission Seventh Framework Programme Research Project; Linnaeus Center through the Swedish Research Council (Vetenskapsradet)

Vid tiden för disputation förelåg publikationen som manuskript

Available from: 2014-05-23 Created: 2014-05-23 Last updated: 2017-12-05
6. MEMS-based inertial navigation based on a magnetic field map
Open this publication in new window or tab >>MEMS-based inertial navigation based on a magnetic field map
2013 (English)In: Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013, p. 6466-6470Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an approach for 6D pose estimation where MEMS inertial measurements are complemented with magnetometer measurements assuming that a model (map) of the magnetic field is known. The resulting estimation problem is solved using a Rao-Blackwellized particle filter. In our experimental study the magnetic field is generated by a magnetic coil giving rise to a magnetic field that we can model using analytical expressions. The experimental results show that accurate position estimates can be obtained in the vicinity of the coil, where the magnetic field is strong.

Keyword
Magnetic field, inertial navigation, state estimation, Rao-Blackwellized particle filter, magnetometer
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-102632 (URN)10.1109/ICASSP.2013.6638911 (DOI)000329611506126 ()
Conference
The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 26-31, 2013
Funder
EU, FP7, Seventh Framework Programme, 1933031801Swedish Research Council, 1933011102
Available from: 2013-12-17 Created: 2013-12-17 Last updated: 2016-12-15

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Citation style
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  • ieee
  • modern-language-association-8th-edition
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  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
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