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Modeling and Calibrating Triangulation Lidars for Indoor Applications
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system. Department of Computer EngineeringUniversity of Baghdad.ORCID-id: 0000-0001-6868-2210
Department of Information EngineeringUniversity of Padova.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0002-4310-7938
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0002-0079-9049
2018 (engelsk)Inngår i: 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, s. 342-366Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Cham: Springer Publishing Company, 2018. s. 342-366
Serie
Lecture Notes in Electrical Engineering, ISSN 1876-1100 ; 430
Emneord [en]
Maximum likelihood, Least squares, Statistical inference, Distance mapping sensors, Lidar, Nonlinear system, AIC
HSV kategori
Forskningsprogram
Reglerteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-66484DOI: 10.1007/978-3-319-55011-4_17ISBN: 978-3-319-55010-7 (tryckt)ISBN: 978-3-319-55011-4 (digital)OAI: oai:DiVA.org:ltu-66484DiVA, id: diva2:1155560
Konferanse
13th International Conference, ICINCO 2016 Lisbon, Portugal, 29-31 July, 2016
Tilgjengelig fra: 2017-11-08 Laget: 2017-11-08 Sist oppdatert: 2019-10-08bibliografisk kontrollert

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