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Optimizacija postopkov terestričnega laserskega skeniranja za meritve visoke natančnosti : doktorska disertacija
Kregar, Klemen (Author), Kogoj, Dušan (Mentor) More about this mentor... This link opens in a new window, Mikoš, Matjaž (Thesis defence commission member), Ambrožič, Tomaž (Thesis defence commission member), Kosmatin Fras, Mojca (Thesis defence commission member), Veljanovski, Tatjana (Thesis defence commission member)

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Abstract
V disertaciji se ukvarjamo z optimizacijo postopkov terestričnega laserskega skeniranja, ker želimo povečati natančnost in zanesljivost rezultatov ter zmanjšati čas in stroške za izvedbo postopka. Glavni namen je kalibracija laserskega skenerja, ki jo želimo nadgraditi s postopki strojnega učenja. Za izvedbo kalibracije vzpostavimo postopek preciznega določanja centrov tarč iz skenogramov. Predlagamo robusten postopek visoke natančnosti, s katerim ovrednotimo tudi natančnost meritev s skenerjem. Vzpostavili smo dve kalibracijski bazi, v katerih smo položaje točk določili s klasično geodetsko metodologijo z najvišjo dosegljivo natančnostjo. Izdelali smo lasten program za izravnavo samokalibracije z izvirnim načinom zagotovitve geodetskega datuma. Na kalibracijskih bazah smo kalibrirali dva terestrična laserska skenerja. Rezultate samokalibracije smo uporabili za določanje dodatnih sistematičnih pogreškov meritev, pri čemer smo uporabili klasično analizo z izravnavo krivulj ter strojno učenje. Prikazana sta dva praktična primera uporabe terestričnega laserskega skeniranja za naloge, kjer je zahtevana visoka natančnost meritev in rezultatov. V termoelektrarni Brestanica preverjamo odklon visokih dimnikov od navpičnice, na pregradi Melje na Dravi pa preizkušamo zaznavanje spremembe oblike ali položaja prelivne stene z inovativno metodo statističnega testiranja sprememb parametrov ravnine.

Language:Slovenian
Keywords:grajeno okolje, gradbeništvo, disertacije, terestrično lasersko skeniranje, kalibracija, strojno učenje, deformacijska analiza
Work type:Doctoral dissertation (mb31)
Tipology:2.08 - Doctoral Dissertation
Organization:FGG - Faculty of Civil and Geodetic Engineering
Year:2016
Publisher:[K. Kregar]
Number of pages:XVIII, 135 str.
UDC:528.5-187:528.02:(043)
COBISS.SI-ID:7778913 Link is opened in a new window
Views:1562
Downloads:493
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Secondary language

Language:English
Title:Optimization of terrestrial laser scanning for high precision measurements : doctoral thesis
Abstract:
The thesis deals with the optimization of terrestrial laser scanning procedures in terms of increasing the accuracy and reliability of the results and reducing the time and cost of the procedure. The main intent of the dissertation is to calibrate the laser scanner and upgrade it with machine learning procedures. In order to carry out the calibration, we need to establish the procedure for precisely determining the target centers from the scans. We propose a robust high precision process with which we also veri�ed the accuracy of the scanner. Two calibration �elds were established and within them we determined the positions of the points with the classical geodetic methodology with the highest achievable accuracy. We developed self-calibration software with an original method for de�ning geodetic datum. Two terrestrial laser scanners were calibrated on the calibration �elds. The self-calibration results were used to determine the additional systematic errors of the measurements at which we used the classical analysis with curve adjustment as well as machine learning. Two practical examples of the application of the terrestrial laser scanning for tasks that require high accuracy measurements and results are shown. In the thermal power plant Brestanica we determined the inclination of its high chimneys. On the high barrier on the Drava River we tried to detect changes in the shape or position of the over ow wall with the use of an innovative method of statistically testing the changes of the plane parameters.

Keywords:building environment, civil engineering, thesis, terrestrial laser scanning, calibration, machine learning, deformation analysis

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