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MEMS-based inertial navigation based on a magnetic field map
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2013 (English)In: Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013, 6466-6470 p.Conference 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.

Place, publisher, year, edition, pages
2013. 6466-6470 p.
Keyword [en]
Magnetic field, inertial navigation, state estimation, Rao-Blackwellized particle filter, magnetometer
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-102632DOI: 10.1109/ICASSP.2013.6638911ISI: 000329611506126OAI: oai:DiVA.org:liu-102632DiVA: diva2:680123
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
In thesis
1. Probabilistic modeling for positioning applications using inertial sensors
Open this publication in new window or tab >>Probabilistic modeling for positioning applications using inertial sensors
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis, we consider the problem of estimating position and orientation (6D pose) using inertial sensors (accelerometers and gyroscopes). Inertial sensors provide information about the change in position and orientation at high sampling rates. However, they suffer from integration drift and hence need to be supplemented with additional sensors. To combine information from the inertial sensors with information from other sensors we use probabilistic models, both for sensor fusion and for sensor calibration.

Inertial sensors can be supplemented with magnetometers, which are typically used to provide heading information. This relies on the assumption that the measured magnetic field is equal to a constant local magnetic field and that the magnetometer is properly calibrated. However, the presence of metallic objects in the vicinity of the sensor will make the first assumption invalid. If the metallic object is rigidly attached to the sensor, the magnetometer can be calibrated for the presence of this magnetic disturbance. Afterwards, the measurements can be used for heading estimation as if the disturbance was not present. We present a practical magnetometer calibration algorithm that is experimentally shown to lead to improved heading estimates. An alternative approach is to exploit the presence of magnetic disturbances in indoor environments by using them as a source of position information. We show that in the vicinity of a magnetic coil it is possible to obtain accurate position estimates using inertial sensors, magnetometers and knowledge of the magnetic field induced by the coil.

We also consider the problem of estimating a human body’s 6D pose. For this, multiple inertial sensors are placed on the body. Information from the inertial sensors is combined using a biomechanical model which represents the human body as consisting of connected body segments. We solve this problem using an optimization-based approach and show that accurate 6D pose estimates are obtained. These estimates accurately represent the relative position and orientation of the human body, i.e. the shape of the body is accurately represented but the absolute position can not be determined.

To estimate absolute position of the body, we consider the problem of indoor positioning using time of arrival measurements from an ultra-wideband (uwb) system in combination with inertial measurements. Our algorithm uses a tightlycoupled sensor fusion approach and is shown to lead to accurate position and orientation estimates. To be able to obtain position information from the uwb measurements, it is imperative that accurate estimates of the receivers’ positions and clock offsets are known. Hence, we also present an easy-to-use algorithm to calibrate the uwb system. It is based on a maximum likelihood formulation and represents the uwb measurements assuming a heavy-tailed asymmetric noise distribution to account for measurement outliers.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 50 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1656
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-106882 (URN)10.3384/lic.diva-106882 (DOI)LIU-TEK-LIC-2014:89 (Local ID)978-91-7519-341-0 (ISBN)LIU-TEK-LIC-2014:89 (Archive number)LIU-TEK-LIC-2014:89 (OAI)
Presentation
2014-06-05, Visionen, B-building, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2014-05-23 Created: 2014-05-23 Last updated: 2016-12-15Bibliographically approved
2. Probabilistic modeling for sensor fusion with inertial measurements
Open this publication in new window or tab >>Probabilistic modeling for sensor fusion with inertial measurements
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. 46 p.
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:nbn:se:liu:diva-133083 (URN)10.3384/diss.diva-133083 (DOI)9789176856215 (ISBN)
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

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Kok, ManonWahlström, NiklasSchön, ThomasGustafsson, Fredrik

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