TU Darmstadt / ULB / TUbiblio

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

Luthardt, Stefan and Willert, Volker and Adamy, Jürgen (2018):
LLama-SLAM: Learning High-Quality Visual Landmarks for Long-Term Mapping and Localization.
In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, November 4-7, 2018, DOI: 10.1109/ITSC.2018.8569323, [Online-Edition: http://tuprints.ulb.tu-darmstadt.de/8357/1/Luthardt_ITSC_201...],
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Luthardt, Stefan and Willert, Volker and Adamy, Jürgen
Title: LLama-SLAM: Learning High-Quality Visual Landmarks for Long-Term Mapping and Localization
Language: English
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.

Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics
Event Title: 2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Event Location: Maui, Hawaii, USA
Event Dates: November 4-7, 2018
Date Deposited: 20 Jan 2019 20:55
DOI: 10.1109/ITSC.2018.8569323
Official URL: http://tuprints.ulb.tu-darmstadt.de/8357/1/Luthardt_ITSC_201...
URN: urn:nbn:de:tuda-tuprints-83575
Related URLs:
Export:

Optionen (nur für Redakteure)

View Item View Item