Features for Ground Texture Based Localization - A Survey
Jan Fabian Schmid (Robert Bosch GmbH), Stephan F. Simon (Robert Bosch GmbH), Rudolf Mester (NTNU Trondheim) AbstractGround texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization. In this paper, we provide the first extensive evaluation of available feature extraction methods for this task, using separately taken image pairs as well as synthetic transformations. We identify AKAZE, SURF and CenSurE as best performing keypoint detectors, and find pairings of CenSurE with the ORB, BRIEF and LATCH feature descriptors to achieve greatest success rates for incremental localization, while SIFT stands out when considering severe synthetic transformations as they might occur during absolute localization.
DOI
10.5244/C.33.184
https://dx.doi.org/10.5244/C.33.184
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@inproceedings{BMVC2019,
title={Features for Ground Texture Based Localization - A Survey},
author={Jan Fabian Schmid and Stephan F. Simon and Rudolf Mester},
year={2019},
month={September},
pages={184.1--184.13},
articleno={184},
numpages={13},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Kirill Sidorov and Yulia Hicks},
doi={10.5244/C.33.184},
url={https://dx.doi.org/10.5244/C.33.184}
}
title={Features for Ground Texture Based Localization - A Survey},
author={Jan Fabian Schmid and Stephan F. Simon and Rudolf Mester},
year={2019},
month={September},
pages={184.1--184.13},
articleno={184},
numpages={13},
booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
publisher={BMVA Press},
editor={Kirill Sidorov and Yulia Hicks},
doi={10.5244/C.33.184},
url={https://dx.doi.org/10.5244/C.33.184}
}