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  • 1.
    Alhashimi, Anas
    et al.
    Örebro University, School of Science and Technology. Luleå University of Technology, Luleå, Sweden.
    Del Favero, Simone
    Varagnolo, Damiano
    Luleå University of Technology, Luleå, Sweden.
    Gustafsson, Thomas
    Luleå University of Technology, Luleå, Sweden.
    Pillonetto, Gianluigi
    Bayesian strategies for calibrating heteroskedastic static sensors with unknown model structures2018In: 2018 European Control Conference (ECC), IEEE, 2018, p. 2447-2453Conference 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.

  • 2.
    Alhashimi, Anas
    et al.
    Control Engineering Group, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden; Department of Computer Engineering, University of Baghdad, Baghdad, Iraq.
    Pierobon, Giovanni
    Department of Information Engineering, University of Padova, Padova, Italy.
    Varagnolo, Damiano
    Control Engineering Group, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden.
    Gustafsson, Thomas
    Control Engineering Group, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden.
    Modeling and Calibrating Triangulation Lidars for Indoor Applications2018In: 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.

  • 3.
    Alhashimi, Anas
    et al.
    Luleå University of Technology, Luleå, Sweden.
    Varagnolo, Damiano
    Luleå University of Technology, Luleå, Sweden.
    Gustafsson, Thomas
    Luleå University of Technology, Luleå, Sweden.
    Joint Temperature-Lasing Mode Compensation for Time-of-Flight LiDAR Sensors2015In: Sensors, E-ISSN 1424-8220, Vol. 15, no 12, p. 31205-31223Article in journal (Refereed)
    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.

  • 4.
    Alhashimi, Anas
    et al.
    Luleå University of Technology, Luleå, Sweden.
    Varagnolo, Damiano
    Luleå University of Technology, Luleå, Sweden.
    Gustafsson, Thomas
    Luleå University of Technology, Luleå, Sweden.
    Statistical modeling and calibration of triangulation Lidars2016In: 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 (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

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