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Features for Ground Texture Based Localization – A Survey
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<span title="2020-03-03">2020</span>
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arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2020-02-27">2020</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.11948v1">arXiv:2002.11948v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oltluhkkdjb3behvnfp7oxc6wm">fatcat:oltluhkkdjb3behvnfp7oxc6wm</a> </span>
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Ground 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
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.11948v2">arXiv:2002.11948v2</a>
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... ve greatest success rates for incremental localization, while SIFT stands out when considering severe synthetic transformations as they might occur during absolute localization.
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