Catalogue : Details
| Christopher Jan Plachetka3D Map Evaluation in LiDAR Point Clouds Using Deep Neural Networks:Dataset, Framework, DNN Architecture, and Methods | ||||||
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| ISBN: | 978-3-8440-9932-4 | ||||||
| Series: | Mitteilungen aus dem Institut für Nachrichtentechnik der Technischen Universität Braunschweig Herausgeber: Prof. Dr.-Ing. U. Reimers, Prof. Dr.-Ing. T. Kürner and Prof. Dr.-Ing. T. Fingscheidt Braunschweig | ||||||
| Volume: | 85 | ||||||
| Keywords: | machine learning; deep neural networks; artificial intelligence; computer vision; perception; automated driving; HD maps; map change detection; map deviation detection; map verification; map validation | ||||||
| Type of publication: | Thesis | ||||||
| Language: | English | ||||||
| Pages: | 212 pages | ||||||
| Figures: | 53 figures | ||||||
| Weight: | 283 g | ||||||
| Format: | 21 x 14,8 cm | ||||||
| Binding: | Paperback | ||||||
| Price: | 59,80 € / 74,80 SFr | ||||||
| Published: | March 2025 | ||||||
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| Open Access (PDF): | 978-3-8191-0014-7 | ||||||
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| Review copy: | Here you can order a review copy. | ||||||
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| Abstract: | 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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