  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 achieve greatest success rates for incremental
localization, while SIFT stands out when considering severe synthetic
transformations as they might occur during absolute localization.
