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Modeling and Calibrating Triangulation Lidars for Indoor Applications
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.ORCID iD: 0000-0001-6868-2210
Department of Information Engineering, University of Padova, Padova, Italy.
Control Engineering Group, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden.ORCID iD: 0000-0002-4310-7938
Control Engineering Group, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden.ORCID iD: 0000-0002-0079-9049
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. p. 342-366
Series
Lecture Notes in Electrical Engineering, ISSN 1876-1100, E-ISSN 1876-1119 ; 430
Keywords [en]
Maximum likelihood, Least squares, Statistical inference, Distance mapping sensors, Lidar, Nonlinear system, AIC
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:oru:diva-82177DOI: 10.1007/978-3-319-55011-4_17ISBN: 978-3-319-55010-7 (print)ISBN: 978-3-319-55011-4 (electronic)OAI: oai:DiVA.org:oru-82177DiVA, id: diva2:1433178
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

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Alhashimi, Anas

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Alhashimi, AnasVaragnolo, DamianoGustafsson, Thomas
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