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Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions
Örebro University, School of Science and Technology. Computer Engineering Department, University of Baghdad, Baghdad, Iraq. (AASS MRO Lab)ORCID iD: 0000-0001-6868-2210
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0001-8658-2985
Department of Autonomous Systems, Otto-von-Guericke University, Magdeburg, Germany..
Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway.
2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 1, article id E155Article in journal (Refereed) Published
Abstract [en]

We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that "fixed features shall have fixed relative distances and angles". The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 21, no 1, article id E155
Keywords [en]
Heteroskedastic, landmark position estimation, lidar, sensor calibration
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-88424DOI: 10.3390/s21010155ISI: 000606055200001PubMedID: 33383734Scopus ID: 2-s2.0-85098541009OAI: oai:DiVA.org:oru-88424DiVA, id: diva2:1516428
Note

Funding Agencies:

EIT Raw Materials project FIREMII 18011

European Union's Horizon 2020 research and innovation programme 732737

FIREMII project 

Available from: 2021-01-12 Created: 2021-01-12 Last updated: 2022-02-10Bibliographically approved

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Alhashimi, AnasMagnusson, Martin

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