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-… 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
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.
