Features for Ground Texture Based Localization -- A Survey

Autor:	Schmid, Jan Fabian, Simon, Stephan F., Mester, Rudolf
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: Published at the 30th British Machine Vision Conference (BMVC 2019)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.11948 Zobrazit plný text záznamu View this record from Arxiv