liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Estimation and Detection with Applications to Navigation
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The ability to navigate in an unknown environment is an enabler for truly utonomous systems. Such a system must be aware of its relative position to the surroundings using sensor measurements. It is instrumental that these measurements are monitored for disturbances and faults. Having correct measurements, the challenging problem for a robot is to estimate its own position and simultaneously build a map of the environment. This problem is referred to as the Simultaneous Localization and Mapping (SLAM) problem. This thesis studies several topics related to SLAM, on-board sensor processing, exploration and disturbance detection.

The particle filter (PF) solution to the SLAM problem is commonly referred to as FastSLAM and has been used extensively for ground robot applications. Having more complex vehicle models using for example flying robots extends the state dimension of the vehicle model and makes the existing solution computationally infeasible. The factorization of the problem made in this thesis allows for a computationally tractable solution.

Disturbance detection for magnetometers and detection of spurious features in image sensors must be done before these sensor measurements can be used for estimation. Disturbance detection based on comparing a batch of data with a model of the system using the generalized likelihood ratio test is considered. There are two approaches to this problem. One is based on the traditional parity space method, where the influence of the initial state is removed by projection, and the other on combining prior information with data in the batch. An efficient parameterization of incipient faults is given which is shown to improve the results considerably.

Another common situation in robotics is to have different sampling rates of the sensors. More complex sensors such as cameras often have slower update rate than accelerometers and gyroscopes. An algorithm for this situation is derived for a class of models with linear Gaussian dynamic model and sensors with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the Kalman filter is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

Vision based target tracking is another important estimation problem in robotics. Distributed exploration with multi-aircraft flight experiments has demonstrated localization of a stationary target with estimate covariance on the order of meters. Grid-based estimation as well as the PF have been examined.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2008. , 174 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1216
Keyword [en]
Navigation, SLAM, Particle Filter, Estimation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-14956ISBN: 978-91-7393-785-6 (print)OAI: oai:DiVA.org:liu-14956DiVA: diva2:117357
Public defence
2008-11-05, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Note
The third article in this thesis is included with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Linköping University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this material, you agree to all provisions of the copyright laws protecting it. Please be advised that wherever a copyright notice from another organization is displayed beneath a figure, a photo, a videotape or a Powerpoint presentation, you must get permission from that organization, as IEEE would not be the copyright holder.Available from: 2008-11-12 Created: 2008-10-03 Last updated: 2009-05-18Bibliographically approved
List of papers
1. Particle Filter SLAM with High Dimensional Vehicle Model
Open this publication in new window or tab >>Particle Filter SLAM with High Dimensional Vehicle Model
2009 (English)In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 55, no 4, 249-266 p.Article in journal (Refereed) Published
Abstract [en]

This work presents a particle filter (PF) method closely related to FastSLAM for solving the simultaneous localization and mapping (SLAM) problem. Using the standard FastSLAM algorithm, only low-dimensional vehicle models can be handled due to computational constraints. In this work an extra factorization of the problem is introduced that makes high-dimensional vehicle models computationally feasible. Results using experimental data from a UAV (helicopter) are presented. The algorithm fuses measurements from on-board inertial sensors (accelerometer and gyro), barometer, and vision in order to solve the SLAM problem.

Place, publisher, year, edition, pages
Springer Netherlands, 2009
Keyword
Rao-Blackwellized/marginalized particle filter, Sensor fusion, Simultaneous localization and mapping, Inertial sensors, UAV, Vision
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-15500 (URN)10.1007/s10846-008-9301-y (DOI)
Projects
CADICS
Available from: 2008-11-12 Created: 2008-11-12 Last updated: 2017-12-14Bibliographically approved
2. Unifying the Parity-Space and GLR Approach to Fault Detection with an IMU Application
Open this publication in new window or tab >>Unifying the Parity-Space and GLR Approach to Fault Detection with an IMU Application
2008 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836Article in journal (Refereed) Submitted
Abstract [en]

Using the parity-space approach, a residual is formed by applying a projection to a batch of observed data and this is a well established approach to fault detection. Based on a stochastic state space model, the parity-space residual can be put into a stochastic framework where conventional hypothesis tests apply. In an on-line application, the batch of data corresponds to a sliding window and in this contribution we develop an improved on-line algorithm that extends the parity-space approach by taking prior information from previous observations into account. For detection of faults, the Generalized Likelihood Ratio (GLR) test is used. This framework allows for including prior information about the initial state, yielding a test statistic with a significantly higher sensitivity to faults. Another key advantage with this approach is that it can be extended to nonlinear systems using an arbitrary nonlinear filter for state estimation, and a linearized model around a nominal state trajectory in the sliding window. We demonstrate the algorithm on data from an Inertial Measurement Unit (IMU), where small and incipient magnetic disturbances are detected using a nonlinear system model.

Keyword
Fault detection, Parity space sensor fusion, Inertial sensors, Magnetometer
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-15501 (URN)
Available from: 2008-11-12 Created: 2008-11-12 Last updated: 2017-12-14Bibliographically approved
3. Detecting Spurious Features using Parity Space
Open this publication in new window or tab >>Detecting Spurious Features using Parity Space
2008 (English)In: Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision, 2008, 353-358 p.Conference paper, Published paper (Refereed)
Abstract [en]

Detection of spurious features is instrumental in many computer vision applications. The standard approach is feature based, where extracted features are matched between the image frames. This approach requires only vision, but is computer intensive and not yet suitable for real-time applications. We propose an alternative based on algorithms from the statistical fault detection literature. It is based on image data and an inertial measurement unit (IMU). The principle of analytical redundancy is applied to batches of measurements from a sliding time window. The resulting algorithm is fast and scalable, and requires only feature positions as inputs from the computer vision system. It is also pointed out that the algorithm can be extended to also detect nonstationary features (moving targets for instance). The algorithm is applied to real data from an unmanned aerial vehicle in a navigation application.

Keyword
Detection, Vision, Parity space, Inertial sensors
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-15502 (URN)10.1109/ICARCV.2008.4795545 (DOI)978-1-4244-2287-6 (ISBN)978-1-4244-2286-9 (ISBN)
Conference
International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam, December, 2008
Available from: 2008-11-12 Created: 2008-11-12 Last updated: 2013-02-23Bibliographically approved
4. Fast Particle Filters for Multi-Rate Sensors
Open this publication in new window or tab >>Fast Particle Filters for Multi-Rate Sensors
2007 (English)In: Proceedings of the 15th European Signal Processing Conference, 2007Conference paper, Published paper (Refereed)
Abstract [en]

Computational complexity is a major concern for practical use of the versatile particle filter (PF) for nonlinear filtering applications. Previous work to mitigate the inherent complexity includes the marginalized particle filter (MPF), with the fastSLAM algorithm as one important case. MPF utilizes a linear Gaussian sub-structure in the problem, where the Kalman filter (KF) can be applied. While this reduces the state dimension in the PF, the present work aims at reducing the sampling rate of the PF. The algorithm is derived for a class of models with linear Gaussian dynamic model and two multirate sensors, with different sampling rates, one slow with a nonlinear and/or non-Gaussian measurement relation and one fast with a linear Gaussian measurement relation. For this case, the KF is used to process the information from the fast sensor and the information from the slow sensor is processed using the PF. The problem formulation covers the important special case of fast dynamics and one slow sensor, which appears in many navigation and tracking problems.

Keyword
Nonlinear filters, Particle filter, Kalman filter, Multi-rate sensors
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-15503 (URN)
Conference
15th European Signal Processing Conference, Poznan, Poland, September, 2007
Available from: 2008-11-12 Created: 2008-11-12 Last updated: 2013-02-26Bibliographically approved
5. A Multiple UAV System for Vision-Based Search and Localization
Open this publication in new window or tab >>A Multiple UAV System for Vision-Based Search and Localization
Show others...
2008 (English)In: Proceedings of the '08 American Control Conference, IEEE , 2008, 1985-1990 p.Conference paper, Published paper (Refereed)
Abstract [en]

The contribution of this paper is an experimentally verified real-time algorithm for combined probabilistic search and track using multiple unmanned aerial vehicles (UAVs). Distributed data fusion provides a framework for multiple sensors to search for a target and accurately estimate its position. Vision based sensing is employed, using fixed downward-looking cameras. These sensors are modeled to include vehicle state uncertainty and produce an estimate update regardless of whether the target is detected in the frame or not. This allows for a single framework for searching or tracking, and requires non-linear representations of the target position probability density function (PDF) and the sensor model. While a grid-based system for Bayesian estimation was used for the flight demonstrations, the use of a particle filter solution has also been examined.

Multi-aircraft flight experiments demonstrate vision-based localization of a stationary target with estimated error covariance on the order of meters. This capability for real-time distributed estimation will be a necessary component for future research in information-theoretic control.

Place, publisher, year, edition, pages
IEEE, 2008
Keyword
Bayes methods, Aerospace control, Aircraft, Cameras, Particle filtering
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-15504 (URN)10.1109/ACC.2008.4586784 (DOI)978-1-4244-2079-7 (ISBN)978-1-4244-2078-0 (ISBN)
Conference
'08 American Control Conference, Seattle, WA, USA, June, 2008
Available from: 2008-11-12 Created: 2008-11-12 Last updated: 2013-12-03Bibliographically approved

Open Access in DiVA

Estimation and Detection with Applications to Navigation(2886 kB)1375 downloads
File information
File name FULLTEXT05.pdfFile size 2886 kBChecksum SHA-512
33a0a55fcca0e717f2212b40bd85b2368280efae858f765da18af111898c6b72fc0b67ad8fe83e2d35d44a2e101d112ca84bcdecb33a39a400c14cddc17430ef
Type fulltextMimetype application/pdf
Cover(240 kB)74 downloads
File information
File name COVER04.pdfFile size 240 kBChecksum SHA-512
f14b78842b164846aae7d55cc2b711215e18f6e876d7f0a48c87639cb17af9c71cfcc74e71d7a27cd699f5a3b007f2efdb26e007ee44b63a28fc96b26f1958df
Type coverMimetype application/pdf

Authority records BETA

Törnqvist, David

Search in DiVA

By author/editor
Törnqvist, David
By organisation
Automatic ControlThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 1376 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 4985 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf