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Modeling and Calibrating Triangulation Lidars for Indoor Applications
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Department of Computer EngineeringUniversity of Baghdad.ORCID iD: 0000-0001-6868-2210
Department of Information EngineeringUniversity of Padova.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-4310-7938
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.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, Cham: Springer Publishing Company, 2018, 342-366 p.Conference 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. 342-366 p.
Series
Lecture Notes in Electrical Engineering, ISSN 1876-1100 ; 430
Keyword [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:ltu:diva-66484DOI: 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:ltu-66484DiVA: diva2:1155560
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

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Publisher's full texthttps://link.springer.com/chapter/10.1007/978-3-319-55011-4_17

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