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How to Match Tracks of Visual Features for Automotive Long-Term SLAM

Luthardt, Stefan ; Ziegler, Christoph ; Willert, Volker ; Adamy, Jürgen (2019)
How to Match Tracks of Visual Features for Automotive Long-Term SLAM.
2019 IEEE Intelligent Transportation Systems Conference (ITSC). Auckland, New Zealand (October 27-30, 2019)
Conference or Workshop Item, Secondary publication, Postprint

Luthardt_ITSC_2019_FeatureTracks.pdf - Accepted Version
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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: How to Match Tracks of Visual Features for Automotive Long-Term SLAM
Language: English
Date: 27 September 2019
Place of Publication: Darmstadt
Publisher: IEEE
Collation: 8 Seiten
Event Title: 2019 IEEE Intelligent Transportation Systems Conference (ITSC)
Event Location: Auckland, New Zealand
Event Dates: October 27-30, 2019
Corresponding Links:

Accurate localization is a vital prerequisite for future assistance or autonomous driving functions in intelligent vehicles. To achieve the required localization accuracy and availability, long-term visual SLAM algorithms like LLama-SLAM are a promising option. In such algorithms visual feature tracks, i.e. landmark observations over several consecutive image frames, have to be matched to feature tracks recorded days, weeks or months earlier. This leads to a more challenging matching problem than in short-term visual localization and known descriptor matching methods cannot be applied directly. In this paper, we devise several approaches to compare and match feature tracks and evaluate their performance on a long-term data set. With the proposed descriptor combination and masking ("CoMa") method the best track matching performance is achieved with minor computational cost. This method creates a single combined descriptor for each feature track and furthermore increases the robustness by capturing the appearance variations of this track in a descriptor mask.

Uncontrolled Keywords: PRORETA4
Status: Postprint
URN: urn:nbn:de:tuda-tuprints-91082
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: 27 Sep 2019 07:55
Last Modified: 13 Feb 2024 13:45
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/9108
PPN: 455217858
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