Visual Feature Track Dataset
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Date
2020-10-26
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Abstract
Description
This dataset contains 282 visual feature tracks. A visual feature track is a
sequence of feature observations of the same real 3D-landmark in consecutive
image frames. These tracks are the output of a classical feature matching
system, e.g. a Visual Odometry system or a system with Bundle Adjustment.
The feature tracks were recorded at three different days in spring 2017 in a
suburban area. The dataset provides each track as a sequence of square image
patches which contain the surrounding of the observed feature. Since a stereo
camera setup was used, there are two patches per feature observation. In total
the dataset contains 3162 of these image patches.
The dataset was created to investigate the task of long-term feature track
matching, i.e. finding all tracks that belong to the same landmark. Therefore,
the dataset also contains “ground truth” labels which of the tracks from the
different days belong together. Furthermore, the distance to the feature is
given for each observation.
Like every real-world data, this dataset is not perfect. If you identify a
major bug, please write an e-mail to
[christoph.ziegler@rmr.tu-…](mailto:christoph.ziegler@rmr.tu-darmstadt.de
"mail to: christoph.ziegler@rmr.tu-darmstadt.de") with the track-ID and a
description of the problem.
If you use this dataset in your research please cite the associated
publication:
_Stefan Luthardt, Christoph Ziegler, Volker Willert, and Jürgen Adamy: “How to
Match Tracks of Visual Features for Automotive Long-Term SLAM”, IEEE 22nd
International Conference on Intelligent Transportation Systems (ITSC), 2019._
_[Download the Paper](https://tuprints.ulb.tu-
darmstadt.de/9108/1/Luthardt_ITSC_2019_FeatureTracks.pdf)_
This paper also provides future explanations of the track matching task and
describes possible approaches to solve this task.
**BibTex:**
@inproceedings{Luthardt.2019,
author = {Luthardt, Stefan and Ziegler, Christoph and Willert, Volker and
Adamy, Jürgen},
title = {How to Match Tracks of Visual Features for Automotive Long-
Term-{SLAM}},
booktitle = {IEEE 22nd International Conference on Intelligent Transportation
Systems (ITSC)},
year = {2019}
}
Paper describing the associated SLAM algorithm:
_[LLama-SLAM: Learning High-Quality Visual Landmarks for Long-Term Mapping and
Localization.](http://tuprints.ulb.tu-
darmstadt.de/8357/1/Luthardt_ITSC_2018_LLama.pdf)_
Keywords
Visualization, Feature extraction, optimization, Simultaneous localization and mapping, Cameras, Robustness, image matching, mobile robots, pose estimation, robot vision, SLAM, automotive long-term SLAM, visual features, autonomous driving functions, LLama-SLAM, consecutive image frames, feature track, visual feature tracks
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Is Supplement To
https://doi.org/10.1109/ITSC.2019.8916895Project(s)
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Except where otherwise noted, this license is described as CC BY 4.0 - Attribution 4.0 International