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Modeling and Calibrating Triangulation Lidars for Indoor Applications

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Informatics in Control, Automation and Robotics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 430))

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Abstract

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.

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Notes

  1. 1.

    Neato Robotics www.neatorobotics.com.

  2. 2.

    Incidentally, the sensor pictured in Fig. 1 was costing around \(\$100.00\) as of December 2016 in Ebay. Nonetheless, the original industrial goal was to reach an end user price of \(\$30.00\).

  3. 3.

    Consider that if we express a line in \(\mathbb {R}^{2}\) as \(x_{2} = a x_{1} + b\) with a the angular coefficient of the line and b its offset, then from a we can estimate the incident angle as \(\widehat{\phi }_{k,1}\) as \(\mathrm {atan}(a)\frac{180}{\pi }\).

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Author information

Authors and Affiliations

  1. Control Engineering Group, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden

    Anas Alhashimi, Damiano Varagnolo & Thomas Gustafsson

  2. Department of Computer Engineering, University of Baghdad, Baghdad, Iraq

    Anas Alhashimi

  3. Department of Information Engineering, University of Padova, Padova, Italy

    Giovanni Pierobon

Authors
  1. Anas Alhashimi
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  2. Giovanni Pierobon
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  3. Damiano Varagnolo
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  4. Thomas Gustafsson
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Corresponding author

Correspondence to Anas Alhashimi .

Editor information

Editors and Affiliations

  1. Images, Signals, and Intelligent Systems Laboratory, University Paris-EST Créteil (UPEC), Créteil, France

    Kurosh Madani

  2. MAC Team, LAAS-CNRS, Toulouse Cedex 4, France

    Dimitri Peaucelle

  3. Ford Research and Advanced Engineering, Dearborn, Michigan, USA

    Oleg Gusikhin

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Alhashimi, A., Pierobon, G., Varagnolo, D., Gustafsson, T. (2018). Modeling and Calibrating Triangulation Lidars for Indoor Applications. In: Madani, K., Peaucelle, D., Gusikhin, O. (eds) Informatics in Control, Automation and Robotics . Lecture Notes in Electrical Engineering, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-319-55011-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-55011-4_17

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