3D Map Evaluation in LiDAR Point Clouds Using Deep Neural Networks:

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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.

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