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Alhashimi, A., Magnusson, M., Knorn, S. & Varagnolo, D. (2021). Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions. Sensors, 21(1), Article ID E155.
Open this publication in new window or tab >>Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions
2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 1, article id E155Article in journal (Refereed) Published
Abstract [en]

We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that "fixed features shall have fixed relative distances and angles". The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
Heteroskedastic, landmark position estimation, lidar, sensor calibration
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-88424 (URN)10.3390/s21010155 (DOI)000606055200001 ()33383734 (PubMedID)2-s2.0-85098541009 (Scopus ID)
Note

Funding Agencies:

EIT Raw Materials project FIREMII 18011

European Union's Horizon 2020 research and innovation programme 732737

FIREMII project 

Available from: 2021-01-12 Created: 2021-01-12 Last updated: 2022-02-10Bibliographically approved
Adolfsson, D., Magnusson, M., Alhashimi, A., Lilienthal, A. & Andreasson, H. (2021). CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Prague, Czech Republic, (Online Conference), September 27 - October 1, 2021 (pp. 5462-5469). IEEE
Open this publication in new window or tab >>CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
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2021 (English)In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), IEEE, 2021, p. 5462-5469Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.

Place, publisher, year, edition, pages
IEEE, 2021
Series
IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858, E-ISSN 2153-0866
Keywords
Localization SLAM Mapping Radar
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-94463 (URN)10.1109/IROS51168.2021.9636253 (DOI)000755125504051 ()9781665417143 (ISBN)9781665417150 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Prague, Czech Republic, (Online Conference), September 27 - October 1, 2021
Funder
Knowledge FoundationEU, Horizon 2020, 732737
Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2024-01-02Bibliographically approved
Alhashimi, A., Del Favero, S., Varagnolo, D., Gustafsson, T. & Pillonetto, G. (2018). Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structures. In: 2018 European Control Conference (ECC): . Paper presented at European Control Conference (ECC), Limassol, Cyprus, June 12-15, 2018. (pp. 2447-2453). IEEE
Open this publication in new window or tab >>Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structures
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2018 (English)In: 2018 European Control Conference (ECC), IEEE, 2018, p. 2447-2453Conference paper, Published paper (Refereed)
Abstract [en]

This paper investigates the problem of calibrating sensors affected by (i) heteroskedastic measurement noise and (ii) a polynomial bias, describing a systematic distortion of the measured quantity. First, a set of increasingly complex statistical models for the measurement process was proposed. Then, for each model the authors design a Bayesian parameters estimation method handling heteroskedasticity and capable to exploit prior information about the model parameters. The Bayesian problem is solved using MCMC methods and reconstructing the unknown parameters posterior in sampled form. The authors then test the proposed techniques on a practically relevant case study, the calibration of Light Detection and Ranging (Lidar) sensor, and evaluate the different proposed procedures using both artificial and field data.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Control Engineering Probability Theory and Statistics
Research subject
Control Engineering
Identifiers
urn:nbn:se:oru:diva-82176 (URN)10.23919/ECC.2018.8550201 (DOI)000467725302079 ()2-s2.0-85059819837 (Scopus ID)978-3-9524-2698-2 (ISBN)978-1-5386-5303-6 (ISBN)
Conference
European Control Conference (ECC), Limassol, Cyprus, June 12-15, 2018.
Note

Funding Agency:

Norrbottens Forskningsrad

University of Baghdad

Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2020-08-19Bibliographically approved
Alhashimi, A., Pierobon, G., Varagnolo, D. & Gustafsson, T. (2018). Modeling and Calibrating Triangulation Lidars for Indoor Applications. In: Kurosh Madani, Dimitri Peaucelle, Oleg Gusikhin (Ed.), Kurosh Madani, Dimitri Peaucelle, Oleg Gusikhin (Ed.), Informatics in Control, Automation and Robotics: 13th International Conference, ICINCO 2016 Lisbon, Portugal, 29-31 July, 2016. Paper presented at 13th International Conference, ICINCO 2016 Lisbon, Portugal, 29-31 July, 2016 (pp. 342-366). Paper presented at 13th International Conference, ICINCO 2016 Lisbon, Portugal, 29-31 July, 2016. Springer
Open this publication in new window or tab >>Modeling and Calibrating Triangulation Lidars for Indoor Applications
2018 (English)In: Informatics in Control, Automation and Robotics: 13th International Conference, ICINCO 2016 Lisbon, Portugal, 29-31 July, 2016 / [ed] Kurosh Madani, Dimitri Peaucelle, Oleg Gusikhin, Springer, 2018, p. 342-366Chapter in book (Refereed)
Abstract [en]

We present an improved statistical model of the measurement process of triangulation Light Detection and Rangings (Lidars) that takes into account bias and variance effects coming from two different sources of uncertainty: (i) mechanical imperfections on the geometry and properties of their pinhole lens - CCD camera systems, and (ii) inaccuracies in the measurement of the angular displacement of the sensor due to non ideal measurements from the internal encoder of the sensor. This model extends thus the one presented in [2] by adding this second source of errors. Besides proposing the statistical model, this chapter considers: (i) specialized and dedicated model calibration algorithms that exploit Maximum Likelihood (ML)/Akaike Information Criterion (AIC) concepts and that use training datasets collected in a controlled setup, and (ii) tailored statistical strategies that use the calibration results to statistically process the raw sensor measurements in non controlled but structured environments where there is a high chance for the sensor to be detecting objects with flat surfaces (e.g., walls). These newly proposed algorithms are thus specially designed and optimized for inferring precisely the angular orientation of the Lidar sensor with respect to the detected object, a feature that is beneficial especially for indoor navigation purposes.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Electrical Engineering, ISSN 1876-1100, E-ISSN 1876-1119 ; 430
Keywords
Maximum likelihood, Least squares, Statistical inference, Distance mapping sensors, Lidar, Nonlinear system, AIC
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:oru:diva-82177 (URN)10.1007/978-3-319-55011-4_17 (DOI)978-3-319-55010-7 (ISBN)978-3-319-55011-4 (ISBN)
Conference
13th International Conference, ICINCO 2016 Lisbon, Portugal, 29-31 July, 2016
Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2022-10-11Bibliographically approved
Alhashimi, A., Varagnolo, D. & Gustafsson, T. (2017). Calibrating distance sensors for terrestrial applications without groundtruth information. IEEE Sensors Journal, 17(12), 3698-3709
Open this publication in new window or tab >>Calibrating distance sensors for terrestrial applications without groundtruth information
2017 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 17, no 12, p. 3698-3709Article in journal (Refereed) Published
Abstract [en]

This paper describes a new calibration procedure for distance sensors that does not require independent sources of groundtruth information, i.e., that is not based on comparing the measurements from the uncalibrated sensor against measurements from a precise device assumed as the groundtruth. Alternatively, the procedure assumes that the uncalibrated distance sensor moves in space on a straight line in an environment with fixed targets, so that the intrinsic parameters of the statistical model of the sensor readings are calibrated without requiring tests in controlled environments, but rather in environments where the sensor follows linear movement and objects do not move. The proposed calibration procedure exploits an approximated expectation maximization scheme on top of two ingredients: an heteroscedastic statistical model describing the measurement process, and a simplified dynamical model describing the linear sensor movement. The procedure is designed to be capable of not just estimating the parameters of one generic distance sensor, but rather integrating the most common sensors in robotic applications, such as Lidars, odometers, and sonar rangers and learn the intrinsic parameters of all these sensors simultaneously. Tests in a controlled environment led to a reduction of the mean squared error of the measurements returned by a commercial triangulation Lidar by a factor between 3 and 6, comparable to the efficiency of other state-of-the art groundtruth-based calibration procedures. Adding odometric and ultrasonic information further improved the performance index of the overall distance estimation strategy by a factor of up to 1.2. Tests also show high robustness against violating the linear movements assumption.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Expectation maximization, distance sensors, intrinsic sensors calibration, heteroscedastic models, simultaneous sensors calibration, triangulation lidars, ultrasonic sensors, odometry
National Category
Control Engineering Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:oru:diva-90265 (URN)10.1109/JSEN.2017.2697850 (DOI)000402123400012 ()2-s2.0-85021749952 (Scopus ID)
Note

Funding Agencies:

Norrbottens Forskningsråd  

University of Baghdad 

Available from: 2021-03-08 Created: 2021-03-08 Last updated: 2021-03-10Bibliographically approved
Alhashimi, A., Varagnolo, D. & Gustafsson, T. (2016). Statistical modeling and calibration of triangulation Lidars. In: Oleg Gusikhin; Dimitri Peaucelle; Kurosh Madani (Ed.), ICINCO 2016: Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics. Paper presented at 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016), Lisbon, Portugal, July 29-31, 2016. (pp. 308-317). SciTePress, 1
Open this publication in new window or tab >>Statistical modeling and calibration of triangulation Lidars
2016 (English)In: ICINCO 2016: Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics / [ed] Oleg Gusikhin; Dimitri Peaucelle; Kurosh Madani, SciTePress, 2016, Vol. 1, p. 308-317Conference paper, Published paper (Refereed)
Abstract [en]

We aim at developing statistical tools that improve the accuracy and precision of the measurements returned by triangulation Light Detection and Rangings (Lidars). To this aim we: i) propose and validate a novel model that describes the statistics of the measurements of these Lidars, and that is built starting from mechanical considerations on the geometry and properties of their pinhole lens - CCD camera systems; ii) build, starting from this novel statistical model, a Maximum Likelihood (ML) / Akaike Information Criterion (AIC) -based sensor calibration algorithm that exploits training information collected in a controlled environment; iii) develop ML and Least Squares (LS) strategies that use the calibration results to statistically process the raw sensor measurements in non controlled environments. The overall technique allowed us to obtain empirical improvements of the normalized Mean Squared Error (MSE) from 0.0789 to 0.0046

Place, publisher, year, edition, pages
SciTePress, 2016
Keywords
Maximum Likelihood, Least Squares, Statistical Inference, Distance Mapping Sensors, Lidar, Nonlinear System, AIC
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:oru:diva-82193 (URN)9789897581984 (ISBN)
Conference
13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016), Lisbon, Portugal, July 29-31, 2016.
Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2020-08-19Bibliographically approved
Alhashimi, A., Varagnolo, D. & Gustafsson, T. (2015). Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors. Sensors, 15(12), 31205-31223
Open this publication in new window or tab >>Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors
2015 (English)In: Sensors, E-ISSN 1424-8220, Vol. 15, no 12, p. 31205-31223Article in journal (Refereed) Published
Abstract [en]

We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.

Place, publisher, year, edition, pages
MDPI, 2015
Keywords
LiDAR, EM, Gaussian mixture model (GMM), mode hopping, ToF, maximum likelihood (ML), multi-modality, SICK LMS200
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:oru:diva-82181 (URN)10.3390/s151229854 (DOI)000367539100089 ()26690445 (PubMedID)2-s2.0-84949895688 (Scopus ID)
Funder
Swedish Research Council
Note

Funding Agency:

DISIRE

Norrbottens Forskningsrad

Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2022-02-10Bibliographically approved
Alhashimi, A., Hostettler, R. & Gustafsson, T. (2014). An Improvement in the Observation Model for Monte Carlo Localization. In: Joaquim Filipe, Oleg Gusikhin, Kurosh Madani and Jurek Sasiadek (Ed.), Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO (pp. 498-505). SciTePress
Open this publication in new window or tab >>An Improvement in the Observation Model for Monte Carlo Localization
2014 (English)In: Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO / [ed] Joaquim Filipe, Oleg Gusikhin, Kurosh Madani and Jurek Sasiadek, SciTePress , 2014, p. 498-505Chapter in book (Refereed)
Abstract [en]

Accurate and robust mobile robot localization is very important in many robot applications. Monte Carlo localization (MCL) is one of the robust probabilistic solutions to robot localization problems. The sensor model used in MCL directly influence the accuracy and robustness of the pose estimation process. The classical beam models assumes independent noise in each individual measurement beam at the same scan. In practice, the noise in adjacent beams maybe largely correlated. This will result in peaks in the likelihood measurement function. These peaks leads to incorrect particles distribution in the MCL. In this research, an adaptive sub-sampling of the measurements is proposed to reduce the peaks in the likelihood function. The sampling is based on the complete scan analysis. The specified measurement is accepted or not based on the relative distance to other points in the 2D point cloud. The proposed technique has been implemented in ROS and stage simulator. The result shows that selecting suitable value of distance between accepted scans can improve the localization error and reduce the required computations effectively.

Place, publisher, year, edition, pages
SciTePress, 2014
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:oru:diva-82183 (URN)10.5220/0005065604980505 (DOI)978-989-758-040-6 (ISBN)
Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2020-08-26Bibliographically approved
Alhashimi, A. (2012). The application of auto regressive spectrum modeling for identification of the intercepted radar signal frequency modulation. Inventi Impact - Telecom, 2012(3)
Open this publication in new window or tab >>The application of auto regressive spectrum modeling for identification of the intercepted radar signal frequency modulation
2012 (English)In: Inventi Impact - Telecom, ISSN 2249-1414, Vol. 2012, no 3Article in journal (Refereed) Published
Abstract [en]

In the Electronic Warfare receivers, it is important to know the type of modulation of the intercepted Radar signals (MOP modulation on pulse). This information can be very helpful in identifying the type of Radar present and to take the appropriate actions against it. In this paper, a new signal processing method is presented to identify the FM (Frequency Modulation) pattern from the received Radar pulses. The proposed processing method based on Auto Regressive Spectrum Modelling used for digital modulation classification [1]. This model uses the instantaneous frequency and instantaneous bandwidth as obtained from the roots of the autoregressive polynomial. The instantaneous frequency and instantaneous bandwidth together were used to identify the type of modulation in the Radar pulse. Another feature derived from the instantaneous frequency is the frequency rate of change. The frequency rate of change was used to extract the pattern of the frequency change. Results show that this method works properly even for low signal to noise ratios.

Place, publisher, year, edition, pages
Bhopal, India: Inventi Journals Pvt.Ltd., 2012
Keywords
Information technology - Telecommunication, Informationsteknik - Telekommunikation
National Category
Signal Processing Telecommunications Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:oru:diva-82185 (URN)
Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2022-10-26Bibliographically approved
Alhashimi, A. (2011). Design and implementation of fast three stages SLA battery charger for PLC systems. Journal of Engineering, 17(3), 448-465
Open this publication in new window or tab >>Design and implementation of fast three stages SLA battery charger for PLC systems
2011 (English)In: Journal of Engineering, ISSN 1726-4073, Vol. 17, no 3, p. 448-465Article in journal (Refereed) Published
Abstract [en]

New fast sealed lead acid (SLA) battery chargers must be able to charge the fully discharged batteries in a short time. In the same time, the charger must monitor the battery state of health in order to prevent over charge and to extend the battery life time.

In this paper a Fast charger was presented to charge SLA batteries in short time and monitor the battery voltage to prevent over charge. The design was implemented practically. And 150 charger of similar type was produced for commercial use. They are now in service in different Mobile base station sites around Baghdad. It can charge a fully discharged 12V, 4.5Ah battery in less than 5 hours. To supply PLC control system on DC power to about 24 hour of continuous operation during main electricity faults.

During one and half year of continuous operation three faults have been recorded in the 150 chargers. All of the three cases were because of bad components manufacturing.

Place, publisher, year, edition, pages
Baghdad, Iraq: University of Baghdad, College of Engineering, 2011
Keywords
SLA, VRLA, SoC, SoH, Battery Chargers, three mode charger, Fast charger, DC PLC backup system
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:oru:diva-82184 (URN)
Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2020-11-10Bibliographically approved
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