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LLama-SLAM: Learning High-Quality Visual Landmarks for Long-Term Mapping and Localization

Luthardt, Stefan ; Willert, Volker ; Adamy, Jürgen (2019)
LLama-SLAM: Learning High-Quality Visual Landmarks for Long-Term Mapping and Localization.
2018 21st International Conference on Intelligent Transportation Systems (ITSC). Maui, Hawaii, USA (04.11. - 07.11.2018)
Conference or Workshop Item, Secondary publication

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: LLama-SLAM: Learning High-Quality Visual Landmarks for Long-Term Mapping and Localization
Language: English
Date: 16 January 2019
Place of Publication: Darmstadt
Year of primary publication: 2018
Publisher: IEEE
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:
Abstract:

The precise localization of vehicles is an important requirement for autonomous driving or advanced driver assistance systems. Using common GNSS the ego position can be measured but not with the reliability and precision necessary. An alternative approach to achieve precise localization is the usage of visual landmarks observed by a camera mounted in the vehicle. However, this raises the necessity of reliable visual landmarks that are easily recognizable and persistent. We propose a novel SLAM algorithm that focuses on learning and mapping such visual long-term landmarks (LLamas). The algorithm therefore processes stereo image streams from several recording sessions in the same spatial area. The key part within LLama-SLAM is the assessment of the landmarks with quality values that are inferred as viewpoint dependent probabilities from observation statistics. By adding solely landmarks of high quality to the final LLama Map, it can be kept compact while still allowing reliable localization. Due to the long-term evaluation of the GNSS measurement during the sessions, the landmarks can be positioned precisely in a global referenced coordinate system. For a first assessment of the algorithm's capabilities, we present some experimental results from the mapping process combining three sessions recorded over two months on the same route.

URN: urn:nbn:de:tuda-tuprints-83575
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 12:15
Last Modified: 08 Dec 2023 07:58
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/8357
PPN: 442892616
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