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Georeferencing Railway Assets Detected in Camera Images And Updating High-Definition Railway Maps

Shah, Elayat Ali (2023) Georeferencing Railway Assets Detected in Camera Images And Updating High-Definition Railway Maps. Masterarbeit, Westfälische Wilhelms-Universität Münster.

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Kurzfassung

Geolocalisation of railway assets is a vital process in railway maintenance, necessitating high precision and often conducted manually via aerial maps and stereo vision cameras. This conventional approach, while reliable, is often time-consuming and expensive, driving an increasing demand for automation and improved accuracy through the use of mono cameras. This research seeks to transform this task's efficiency by automating the geolocalisation of railway assets such as level crossings, railway switches, and embedded rails using mono cameras. The study involves estimating the scale factor for mono cameras to recover depth information and then comparing the established OpenCV-based approach rotation matrix and the Algorithm Vanishing Point rotation matrix method with the novel method: Manual Vanishing Point rotation matrix methods. A custom algorithm was developed to georeference detected railway assets and assess the relative accuracy of the three methodologies. Upon evaluating the results against reference data (switch frogs), the Manual Vanishing Point rotation matrix method, a new proposition in this research, demonstrated superior performance, achieving a mean distance of 5.41m, a median distance of 5.33m, and a standard deviation of 1.72. Following this, the OpenCV-based approach rotation matrix registered a mean distance of 10.92m, a median distance of 9.00m, and a standard deviation of 7.68. Lastly, the Algorithm Vanishing Point rotation matrix method yielded a mean distance of 10.89m, a median distance of 11.00m, and a standard deviation of 2.01.

elib-URL des Eintrags:https://elib.dlr.de/195366/
Dokumentart:Hochschulschrift (Masterarbeit)
Titel:Georeferencing Railway Assets Detected in Camera Images And Updating High-Definition Railway Maps
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Shah, Elayat AliElayat.Shah (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:5 Juni 2023
Referierte Publikation:Nein
Open Access:Nein
Seitenanzahl:104
Status:veröffentlicht
Stichwörter:mono-camera, georeferencing, railway maps
Institution:Westfälische Wilhelms-Universität Münster
Abteilung:Institut für Geoinformatik
HGF - Forschungsbereich:keine Zuordnung
HGF - Programm:keine Zuordnung
HGF - Programmthema:keine Zuordnung
DLR - Schwerpunkt:Digitalisierung
DLR - Forschungsgebiet:D DAT - Daten
DLR - Teilgebiet (Projekt, Vorhaben):D - Digitaler Atlas 2.0
Standort: Braunschweig
Institute & Einrichtungen:Institut für Verkehrssystemtechnik
Hinterlegt von: Shankar, Sangeetha
Hinterlegt am:22 Jun 2023 11:40
Letzte Änderung:22 Jun 2023 11:40

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