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Publications (10 of 20) Show all publications
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, Cyprus, Limasson, 12-15 June, 2018 (pp. 2447-2453). Piscataway, NJ: Institute of Electrical and Electronics Engineers (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), Piscataway, NJ: Institute of Electrical and Electronics Engineers (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
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-72550 (URN)10.23919/ECC.2018.8550201 (DOI)2-s2.0-85059819837 (Scopus ID)978-3-9524-2698-2 (ISBN)978-1-5386-5303-6 (ISBN)
Conference
European Control Conference, Cyprus, Limasson, 12-15 June, 2018
Available from: 2019-01-14 Created: 2019-01-14 Last updated: 2019-03-15
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). Cham: Springer Publishing Company
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, Cham: Springer Publishing Company, 2018, p. 342-366Conference paper, Published paper (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
Cham: Springer Publishing Company, 2018
Series
Lecture Notes in Electrical Engineering, ISSN 1876-1100 ; 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:ltu:diva-66484 (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: 2017-11-08 Created: 2017-11-08 Last updated: 2017-11-24Bibliographically approved
Alhashimi, A., Varagnolo, D. & Gustafsson, T. (2017). Calibrating distance sensors for terrestrial applications without groundtruth information. IEEE Sensors Journal, 17(12), 3698-3709, Article ID 7911206.
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. 3698-3709, article id 7911206Article 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
Calibration, Laser radar, Robot sensing systems, Mobile robots, Time measurement expectation maximization, distance sensors, intrinsic sensors calibration, heteroscedastic models, simultaneous sensors calibration, triangulation lidars, ultrasonic sensors, odometry
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-63189 (URN)10.1109/JSEN.2017.2697850 (DOI)000402123400012 ()2-s2.0-8502174995 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-05-29 (rokbeg)

Available from: 2017-04-28 Created: 2017-04-28 Last updated: 2018-07-10Bibliographically approved
Alhashimi, A. (2016). Statistical Calibration Algorithms for Lidars (ed.). (Licentiate dissertation). Paper presented at . Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Statistical Calibration Algorithms for Lidars
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Robots are becoming increasingly available and capable, are becoming part of everyday life in applications: robots that guide blind or mentally handicapped people, robots that clean large office buildings and department stores, robots that assist people in shopping, recreational activities, etc.Localization, in the sense of understanding accurately one's position in the environment, is a basic building block for performing important tasks. Therefore, there is an interest in having robots to perform autonomously and accurately localization tasks in highly cluttered and dynamically changing environments.To perform localization, robots are required to opportunely combine their sensors measurements, sensors models and environment model. In this thesis we aim at improving the tools that constitute the basis of all the localization techniques, that are the models of these sensors, and the algorithms for processing the raw information from them. More specifically we focus on:- finding advanced statistical models of the measurements returned by common laser scanners (a.k.a. Lidars), starting from both physical considerations and evidence collected with opportune experiments;- improving the statistical algorithms for treating the signals coming from these sensors, and thus propose new estimation and system identification techniques for these devices.In other words, we strive for increasing the accuracy of Lidars through opportune statistical processing tools.The problems that we have to solve, in order to achieve our aims, are multiple. The first one is related to temperature dependency effects: the laser diode characteristics, especially the wave length of the emitted laser and the mechanical alignment of the optics, change non-linearly with temperature. In one of the papers in this thesis we specifically address this problem and propose a model describing the effects of temperature changes in the laser diode; these include, among others, the presence of multi-modal measurement noises. Our contributions then include an algorithm that statistically accounts not only for the bias induced by temperature changes, but also for these multi-modality issues.An other problem that we seek to relieve is an economical one. Improving the Lidar accuracy can be achieved by using accurate but expensive laser diodes and optical lenses. This unfortunately raises the sensor cost, and -- obviously -- low cost robots should not be equipped with very expensive Lidars. On the other hand, cheap Lidars have larger biases and noise variance. In an other contribution we thus precisely targeted the problem of how to improve the performance indexes of inexpensive Lidars by removing their biases and artifacts through opportune statistical manipulations of the raw information coming from the sensor. To achieve this goal it is possible to choose two different ways (that have been both explored):1- use the ground truth to estimate the Lidar model parameters;2- find algorithms that perform simultaneously calibration and estimation without using ground truth information. Using the ground truth is appealing since it may lead to better estimation performance. On the other hand, though, in normal robotic operations the actual ground truth is not available -- indeed ground truths usually require environmental modifications, that are costly. We thus considered how to estimate the Lidar model parameters for both the cases above.In last chapter of this thesis we conclude our findings and propose also our current future research directions.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2016. p. 118
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
Information technology - Automatic control, Informationsteknik - Reglerteknik
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-18224 (URN)77e5456a-bb9e-4fb1-b72b-ef40b6a0142a (Local ID)978-91-7583-650-8 (ISBN)978-91-7583-651-5 (ISBN)77e5456a-bb9e-4fb1-b72b-ef40b6a0142a (Archive number)77e5456a-bb9e-4fb1-b72b-ef40b6a0142a (OAI)
Note
Godkänd; 2016; 20160809 (alhana); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Anas Alhashimi Ämne: Reglerteknik/Control Engineering Uppsats: Statistical Calibration Algorithms for Lidars Examinator: Professor Thomas Gustafsson, Institutionen för system- och rymdteknik, Avdelning: Signaler och System, Luleå tekniska universitet. Diskutant: Bitr. Professor Steffi Knorn, Signaler och System, Uppsala Universitet. Tid: Tisdag 6 september, 2016 kl 10.00 Plats: A109, Luleå tekniska universitetAvailable from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Alhashimi, A. W., Varagnolo, D. & Gustafsson, T. (2016). Statistical modeling and calibration of triangulation Lidars. In: Peaucelle D.,Gusikhin O.,Madani K (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, 29-31 July 2016 (pp. 308-317). SciTePress
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] Peaucelle D.,Gusikhin O.,Madani K, SciTePress, 2016, 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
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-62315 (URN)9789897581984 (ISBN)
Conference
13th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2016, Lisbon, Portugal, 29-31 July 2016
Available from: 2017-03-06 Created: 2017-03-06 Last updated: 2017-11-24Bibliographically approved
Alhashimi, A. (2015). Characterization of Neato Lidar (ed.). Paper presented at .
Open this publication in new window or tab >>Characterization of Neato Lidar
2015 (English)Report (Other academic)
Abstract [en]

The Lidars are very useful sensors in many robotic applications. The problem is that the price of these sensors are quite expensive. A cheap version of these sensors is the Neato {Neato Robotics, Inc. https://www.neatorobotics.com/company/} Lidar. In this report we will present different experiments that had been done to characterize this device. Also discuss the possibilities that can be done to improve its performance in the robotics applications.

Publisher
p. 4
Keywords
Lidar, Robotics, sensor, characterization, Technology - Electrical engineering, electronics and photonics, Teknikvetenskap - Elektroteknik, elektronik och fotonik
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-22637 (URN)399ccb2f-3da0-4113-a1d7-8de50429ba9f (Local ID)399ccb2f-3da0-4113-a1d7-8de50429ba9f (Archive number)399ccb2f-3da0-4113-a1d7-8de50429ba9f (OAI)
Note
Godkänd; 2015; 20151202 (alhana)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Alhashimi, A., Varagnolo, D. & Gustafsson, T. (2015). Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors (ed.). Paper presented at . 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, ISSN 1424-8220, 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.

National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-8300 (URN)10.3390/s151229854 (DOI)000367539100089 ()26690445 (PubMedID)2-s2.0-84949895688 (Scopus ID)6cc26a2c-2f62-437e-8238-6536773e8534 (Local ID)6cc26a2c-2f62-437e-8238-6536773e8534 (Archive number)6cc26a2c-2f62-437e-8238-6536773e8534 (OAI)
Note
Validerad; 2016; Nivå 2; 20151211 (alhana)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Alhashimi, A., Nikolakopoulos, G. & Gustafsson, T. (2014). An improvement in the observarion model for Monte Carlo localization (ed.). Paper presented at Reglermöte 2014 : 03/06/2014 - 04/06/2014. Paper presented at Reglermöte 2014 : 03/06/2014 - 04/06/2014.
Open this publication in new window or tab >>An improvement in the observarion model for Monte Carlo localization
2014 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-29723 (URN)349e6d0f-63b1-497a-9063-b1f7fb0e19a2 (Local ID)349e6d0f-63b1-497a-9063-b1f7fb0e19a2 (Archive number)349e6d0f-63b1-497a-9063-b1f7fb0e19a2 (OAI)
Conference
Reglermöte 2014 : 03/06/2014 - 04/06/2014
Note
Godkänd; 2014; 20141104 (alhana)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Alhashimi, A., Hostettler, R. & Gustafsson, T. (2014). An Improvement in the Observation Model for Monte Carlo Localization (ed.). In: (Ed.), J. Felipe (Ed.), Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics: Vienna, Austria, 1-3, September, 2014 (pp. 498-505). Paper presented at . : 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: Vienna, Austria, 1-3, September, 2014, 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:ltu:diva-20144 (URN)25257edf-8707-4b91-aca2-588ac6b210f5 (Local ID)9789897580406 (ISBN)25257edf-8707-4b91-aca2-588ac6b210f5 (Archive number)25257edf-8707-4b91-aca2-588ac6b210f5 (OAI)
Note
Godkänd; 2014; 20140915 (alhana)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved
Alhashimi, A., Nikolakopoulos, G. & Gustafsson, T. (2014). Observation model for Monte Carlo Localization (ed.). Paper presented at Reglermöte 2014 : 03/06/2014 - 04/06/2014. Paper presented at Reglermöte 2014 : 03/06/2014 - 04/06/2014.
Open this publication in new window or tab >>Observation model for Monte Carlo Localization
2014 (English)Conference paper, Oral presentation only (Other academic)
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-34114 (URN)838911c9-df06-4271-a634-2d37a46881cb (Local ID)838911c9-df06-4271-a634-2d37a46881cb (Archive number)838911c9-df06-4271-a634-2d37a46881cb (OAI)
Conference
Reglermöte 2014 : 03/06/2014 - 04/06/2014
Note
Godkänd; 2014; 20141121 (andbra)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-6868-2210

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