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March 2025
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Christopher Jan Plachetka
3D Map Evaluation in LiDAR Point Clouds Using Deep Neural Networks:
Dataset, Framework, DNN Architecture, and Methods
This dissertation examines the evaluation of high-definition maps using LiDAR point clouds as sensor data and deep neural networks (DNNs). The developed methods are suitable for both automated driving and geodesy. By utilizing the map as an additional input, the DNN can verify the map even under adverse conditions that degrade the sensor input. This work introduces a novel dataset, a conceptual framework, a specialized DNN architecture, and innovative evaluation methods, contributing significantly to the field.
Keywords: machine learning; artificial intelligence; deep neural networks; computer vision; perception; automated driving; HD maps; map change detection; map deviation detection; map verification; map validation
Mitteilungen aus dem Institut für Nachrichtentechnik der Technischen Universität Braunschweig
Edited by Prof. Dr.-Ing. U. Reimers, Prof. Dr.-Ing. T. Kürner and Prof. Dr.-Ing. T. Fingscheidt, Braunschweig
Volume 85
Other formats
Print version: 978-3-8440-9932-4
DOI 10.2370/9783819100147
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