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

Direct link
BETA
Karlsson, Rickard
Alternative names
Publications (10 of 85) Show all publications
Karlsson, R. & Gustafsson, F. (2017). The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime. IEEE signal processing magazine (Print), 34(2), 60-69
Open this publication in new window or tab >>The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime
2017 (English)In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 34, no 2, p. 60-69Article in journal (Refereed) Published
Abstract [en]

Most navigation systems today rely on global navigation satellite systems (gnss), including in cars. With support from odometry and inertial sensors, this is a sufficiently accurate and robust solution, but there are future demands. Autonomous cars require higher accuracy and integrity. Using the car as a sensor probe for road conditions in cloud-based services also sets other kind of requirements. The concept of the Internet of Things requires stand-alone solutions without access to vehicle data. Our vision is a future with both invehicle localization algorithms and after-market products, where the position is computed with high accuracy in gnss-denied environments. We present a localization approach based on a prior that vehicles spend the most time on the road, with the odometer as the primary input. When wheel speeds are not available, we present an approach solely based on inertial sensors, which also can be used as a speedometer. The map information is included in a Bayesian setting using the particle filter (PF) rather than standard map matching. In extensive experiments, the performance without gnss is shown to have basically the same quality as utilizing a gnss sensor. Several topics are treated: virtual measurements, dead reckoning, inertial sensor information, indoor positioning, off-road driving, and multilevel positioning.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-135786 (URN)10.1109/MSP.2016.2637418 (DOI)000397574300008 ()2-s2.0-85015356723 (Scopus ID)
Projects
Wallenberg Autonomous Systems Program
Note

Funding agencies: Wallenberg Autonomous Systems Program

Available from: 2017-03-22 Created: 2017-03-22 Last updated: 2017-04-21Bibliographically approved
Lindfors, M., Hendeby, G., Gustafsson, F. & Karlsson, R. (2016). Vehicle Speed Tracking Using Chassis Vibrations. In: Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV): . Paper presented at The 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, 19-22 June 2016. (pp. 214-219). IEEE conference proceedings
Open this publication in new window or tab >>Vehicle Speed Tracking Using Chassis Vibrations
2016 (English)In: Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), IEEE conference proceedings, 2016, p. 214-219Conference paper, Published paper (Refereed)
Abstract [en]

The speed of a wheeled vehicle is usually estimatedusing wheel speed sensors (WSS) or GPS. If these signals are unavailable, other methods must be used. We propose a novelapproach exploiting the fact that vibrations from rotating axles,with fundamental frequency proportional to vehicle speed, aretransmitted via the vehicle chassis. Using an accelerometer, these vibrations can be tracked to estimate vehicle speed whileother sources of vibrations act as disturbances. A state-space model for the dynamics of the harmonics is presented andformulated such that there is a conditional linear-Gaussiansubstructure, enabling efficient Rao-Blackwellized methods. Avariant of the Rao-Blackwellized point-mass filter is derived, significantly reducing computational complexity, and reducingthe memory requirements from quadratic to linear in thenumber of grid points. It is applied to experimental data from the sensor cluster of a car and validated using therotational frequency from WSS data. The proposed methodshows improved performance and robustness in comparisonto a Rao-Blackwellized particle filter implementation and afrequency spectrum maximization method.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Keywords
Mapping and Localization; Intelligent Ground, Air and Space Vehicles; Advanced Driver Assistance Systems
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-129689 (URN)10.1109/IVS.2016.7535388 (DOI)000390845600036 ()978-1-5090-1821-5 (ISBN)
Conference
The 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, 19-22 June 2016.
Projects
Wallenberg Autonomous Systems Program (WASP)
Funder
Knut and Alice Wallenberg Foundation
Available from: 2016-06-23 Created: 2016-06-23 Last updated: 2017-01-22Bibliographically approved
Axelsson, P., Karlsson, R. & Norrlöf, M. (2014). Estimation-based Norm-optimal Iterative Learning Control. Systems & control letters (Print), 73, 76-80
Open this publication in new window or tab >>Estimation-based Norm-optimal Iterative Learning Control
2014 (English)In: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 73, p. 76-80Article in journal (Refereed) Published
Abstract [en]

The norm-optimal iterative learning control (ilc) algorithm for linear systems is extended to an estimation-based norm-optimal ilc  algorithm where the controlled variables are not directly available as measurements. A separation lemma is presented, stating that if a stationary Kalman filter is used for linear time-invariant systems then the ilc  design is independent of the dynamics in the Kalman filter. Furthermore, the objective function in the optimisation problem is modified to incorporate the full probability density function of the error. Utilising the Kullback–Leibler divergence leads to an automatic and intuitive way of tuning the ilc  algorithm. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ilc  algorithm. Stability and convergence properties for the proposed scheme are also derived.

Place, publisher, year, edition, pages
Elsevier, 2014
Keywords
Iterative learning control; Estimation; Filtering; Non-linear systems
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-104791 (URN)10.1016/j.sysconle.2014.08.007 (DOI)000345108000010 ()
Projects
Vinnova Excellence Center LINK-SICExcellence Center at Linköping-Lund in Information Technology, ELLIITSSF project Collaborative Localization
Funder
VINNOVAeLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2014-02-26 Created: 2014-02-26 Last updated: 2017-12-05
Hendeby, G. & Karlsson, R. (2014). Gaussian Mixture PHD Filtering with Variable Probability of Detection. In: 17th International Conference on Information Fusion (FUSION), 2014: . Paper presented at 17th International Conference on Information Fusion, Salamanca, Spain, July 7-10, 2014 (pp. 1-7). IEEE
Open this publication in new window or tab >>Gaussian Mixture PHD Filtering with Variable Probability of Detection
2014 (English)In: 17th International Conference on Information Fusion (FUSION), 2014, IEEE , 2014, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

The probabilistic hypothesis density (PHD) filter has grown in popularity during the last decade as a way to address the multi-target tracking problem. Several algorithms exist; for instance under linear-Gaussian assumptions, the Gaussian mixture PHD (GM-PHD) filter. This paper extends the GM-PHD filter to the common case with variable probability of detection throughout the tracking volume. This allows for more efficient utilization, e.g., in situations with distance dependent probability of detection or occluded regions. The proposed method avoids previous algorithmic pitfalls that can result in a not well-defined PHD. The method is illustrated and compared to the standard GM-PHD in a simplified multi-target tracking example as well asin a realistic nonlinear underwater sonar simulation application, both demonstrating the effectiveness of the proposed method.

Place, publisher, year, edition, pages
IEEE, 2014
Keywords
Gaussian-Mixture PHD; Multi-Target Tracking; Sonar
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-108956 (URN)000363896100043 ()978-849012355-3 (ISBN)
Conference
17th International Conference on Information Fusion, Salamanca, Spain, July 7-10, 2014
Funder
Security LinkSwedish Foundation for Strategic Research
Available from: 2014-07-14 Created: 2014-07-14 Last updated: 2016-06-10
Lundquist, C., Karlsson, R., Özkan, E. & Gustafsson, F. (2014). Tire Radii Estimation Using a Marginalized Particle Filter. IEEE transactions on intelligent transportation systems (Print), 15(2), 663-672
Open this publication in new window or tab >>Tire Radii Estimation Using a Marginalized Particle Filter
2014 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 15, no 2, p. 663-672Article in journal (Refereed) Published
Abstract [en]

In this paper, the measurements of individual wheel speeds and the absolute position from a global positioning system are used for high-precision estimation of vehicle tire radii. The radii deviation from its nominal value is modeled as a Gaussian random variable and included as noise components in a simple vehicle motion model. The novelty lies in a Bayesian approach to estimate online both the state vector and the parameters representing the process noise statistics using a marginalized particle filter (MPF). Field tests show that the absolute radius can be estimated with submillimeter accuracy. The approach is tested in accordance with regulation 64 of the United Nations Economic Commission for Europe on a large data set (22 tests, using two vehicles and 12 different tire sets), where tire deflations are successfully detected, with high robustness, i.e., no false alarms. The proposed MPF approach outperforms common Kalman-filter-based methods used for joint state and parameter estimation when compared with respect to accuracy and robustness.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2014
Keywords
Conjugate prior; marginalized particle filter (MPF); noise parameter estimation; tire radius
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-106514 (URN)10.1109/TITS.2013.2284930 (DOI)000334584800019 ()
Available from: 2014-05-12 Created: 2014-05-09 Last updated: 2017-12-05
Axelsson, P., Karlsson, R. & Norrlöf, M. (2013). Estimation-based ILC using Particle Filter with Application to Industrial Manipulators. In: Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, November 3-7, 2013 (pp. 1740-1745).
Open this publication in new window or tab >>Estimation-based ILC using Particle Filter with Application to Industrial Manipulators
2013 (English)In: Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, p. 1740-1745Conference paper, Published paper (Refereed)
Abstract [en]

An estimation-based iterative learning control (ILC) algorithm is applied to a realistic industrial manipulator model. By measuring the acceleration of the end-effector, the arm angular position accuracy is improved when the measurements are fused with motor angle observations. The estimation problem is formulated in a Bayesian estimation framework where three solutions are proposed: one using the extended Kalman filter (EKF), one using the unscented  Kalman filter (UKF), and one using the particle filter (PF).  The estimates are used in an ILC method to improve the accuracy for following a given reference trajectory.  Since the ILC algorithm is repetitive no computational restrictions on the methods apply explicitly. In an extensive Monte Carlo simulation study it is shown that the PF method outperforms the other methods and that the ILC control law is substantially improved using the PF estimate.

National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-100880 (URN)10.1109/IROS.2013.6696584 (DOI)000331367401125 ()
Conference
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, November 3-7, 2013
Projects
Vinnova Excellence Center LINK-SIC
Funder
Vinnova
Available from: 2014-01-13 Created: 2013-11-14 Last updated: 2014-04-14
Axelsson, P., Karlsson, R. & Norrlöf, M. (2013). Estimation-based Norm-optimal Iterative Learning Control. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Estimation-based Norm-optimal Iterative Learning Control
2013 (English)Report (Other academic)
Abstract [en]

The iterative learning control (ILC) method improvesperformance of systems that repeat the same task several times. In this paper the standard norm-optimal ILC control law for linear systems is extended to an estimation-based ILC algorithm where the controlled variables are not directly available as measurements. The proposed ILC algorithm is proven to be stable and gives monotonic convergence of the error. The estimation-based part of the algorithm uses Bayesian estimation techniques such as the Kalman filter. The objective function in the optimisation problem is modified to incorporate not only the mean value of the estimated variable, but also information about the uncertainty of the estimate. It is further shown that for linear time-invariant systems the ILC design is independent of the estimation method. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ILC algorithm. It is also discussed how the Kullback-Leibler divergence can be used if linearisation cannot be performed. Finally, the proposed solution for non-linear systems is applied and verified in a simulation study with a simplified model of an industrial manipulator system.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. p. 12
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3066
Keywords
Iterative, Learning Control, Estimation, Filtering, Nonlinear systems, Optimal
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-100899 (URN)LiTH-ISY-R-3066 (ISRN)
Projects
Vinnova Excellence Center LINK-SIC
Funder
Vinnova
Available from: 2013-11-14 Created: 2013-11-14 Last updated: 2014-06-16Bibliographically approved
Gustafsson, F. & Karlsson, R. (2013). Generating Dithering Noise for Maximum Likelihood Estimation from Quantized Data. Automatica, 49(2), 554-560
Open this publication in new window or tab >>Generating Dithering Noise for Maximum Likelihood Estimation from Quantized Data
2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 2, p. 554-560Article in journal (Refereed) Published
Abstract [en]

The Quantization Theorem I (QT I) implies that the likelihood function can be reconstructed from quantized sensor observations, given that appropriate dithering noise is added before quantization. We present constructive algorithms to generate such dithering noise. The application to maximum likelihood estimation (mle) is studied in particular. In short, dithering has the same role for amplitude quantization as an anti-alias filter has for sampling, in that it enables perfect reconstruction of the dithered but unquantized signal’s likelihood function. Without dithering, the likelihood function suffers from a kind of aliasing expressed as a counterpart to Poisson’s summation formula which makes the exact mle intractable to compute. With dithering, it is demonstrated that standard mle algorithms can be re-used on a smoothed likelihood function of the original signal, and statistically efficiency is obtained. The implication of dithering to the Cramér–Rao Lower Bound (CRLB) is studied, and illustrative examples are provided.

Place, publisher, year, edition, pages
Elsevier, 2013
Keywords
Maximum likelihood, Estimation, Quantization
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-90218 (URN)10.1016/j.automatica.2012.11.028 (DOI)000315003100028 ()
Funder
Swedish Research Council
Note

Funding Agencies|Swedish Research Council through the center of excellence CADICS||project grant "Fundamental issues in sensor fusion"||

Available from: 2013-03-21 Created: 2013-03-21 Last updated: 2017-12-06Bibliographically approved
Axelsson, P., Karlsson, R. & Norrlöf, M. (2012). Bayesian Methods for Estimating Tool Position of an Industrial Manipulator. In: Proceedings of Reglermöte 2012: . Paper presented at Reglermöte 2012, Uppsala, Sweden, 13-14 June, 2012.
Open this publication in new window or tab >>Bayesian Methods for Estimating Tool Position of an Industrial Manipulator
2012 (English)In: Proceedings of Reglermöte 2012, 2012Conference paper, Published paper (Other academic)
Abstract [en]

State estimation of a flexible industrial manipulator is presented using experimental data. The problem is formulated in a Bayesian framework where the extended Kalman filter and particle filter are used. The filters use the joint positions on the motor side of the gearboxes as well as the acceleration at the end-effector as measurements and estimates the corresponding joint angles on the arm side of the gearboxes. The techniques are verified on a state of the art industrial robot, and it is shown that the use of the acceleration at the end-effector improves the estimates significantly.

Keywords
Estimation, Extended Kalman filter, Particle filter, Accelerometer, Industrial robot
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-88979 (URN)
Conference
Reglermöte 2012, Uppsala, Sweden, 13-14 June, 2012
Projects
Vinnova Excellence Center LINK-SIC at Linköping University, SwedenSSF project Collaborative Localization
Funder
VinnovaSwedish Foundation for Strategic Research
Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2013-07-10
Axelsson, P., Karlsson, R. & Norrlöf, M. (2012). Bayesian State Estimation of a Flexible Industrial Robot. Control Engineering Practice, 20(11), 1220-1228
Open this publication in new window or tab >>Bayesian State Estimation of a Flexible Industrial Robot
2012 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 20, no 11, p. 1220-1228Article in journal (Refereed) Published
Abstract [en]

A sensor fusion method for state estimation of a flexible industrial robot is developed. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, as well as the estimated position of the end-effector are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; the extended Kalman filter and the particle filter. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The technique is also verified in experiments on an ABB robot, where the dynamic performance of the position for the end-effector is significantly improved.

Place, publisher, year, edition, pages
Elsevier, 2012
Keywords
Industrial robot, Positioning, Estimation, Particle filter, Extended Kalman filter, Cramér–Rao lower bound
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-81988 (URN)10.1016/j.conengprac.2012.06.004 (DOI)000309847800015 ()
Projects
Vinnova Excellence Center LINK-SICSSF project Collaborative Localization
Funder
VinnovaSwedish Foundation for Strategic Research
Available from: 2012-09-27 Created: 2012-09-27 Last updated: 2017-12-07
Organisations

Search in DiVA

Show all publications