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Multi-Session Visual Roadway Mapping

Boschenriedter, Stefan ; Hossbach, Phillip ; Linnhoff, Clemens ; Luthardt, Stefan ; Wu, Siqian (2019)
Multi-Session Visual Roadway Mapping.
2018 21st International Conference on Intelligent Transportation Systems (ITSC). Maui, Hawaii, USA (04.11. - 07.11.2018)
Conference or Workshop Item, Secondary publication

Luthardt_ITSC_2018_Roadway.pdf - Accepted Version
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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Multi-Session Visual Roadway Mapping
Language: English
Date: 16 January 2019
Place of Publication: Darmstadt
Year of primary publication: 2018
Publisher: IEEE
Book Title: 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Event Title: 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Event Location: Maui, Hawaii, USA
Event Dates: 04.11. - 07.11.2018
Corresponding Links:

This paper proposes an algorithm for camera based roadway mapping in urban areas. With a convolutional neural network the roadway is detected in images taken by a camera mounted in the vehicle. The detected roadway masks from all images of one driving session are combined according to their corresponding GPS position to create a probabilistic grid map of the roadway. Finally, maps from several driving sessions are merged by a feature matching algorithm to compensate for errors in the roadway detection and localization inaccuracies. Hence, this approach utilizes solely low-cost sensors common in usual production vehicles and can generate highly detailed roadway maps from crowd-sourced data.

URN: urn:nbn:de:tuda-tuprints-83567
Classification DDC: 600 Technology, medicine, applied sciences > 600 Technology
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Date Deposited: 16 Jan 2019 12:17
Last Modified: 08 Dec 2023 07:54
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/8356
PPN: 442892608
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