Abstract
In this paper, a method of place recognition is presented. The method is generally classified under the bag-of-visual-words approach. Information from several global image descriptors is incorporated. The data fusion is performed at the feature level.
The efficacy of the combined descriptor is investigated on the dataset recorded from a real robot. To measure the composition effect, all component descriptors are compared along with their combinations. Information on computational complexity of the method is also detailed, although the algorithms used did not undergo a big amount of optimization. The combined descriptor exhibits greater discriminative power, at the cost of increased computational time.
L. Přeučil—The presented research was supported by the Czech Technical University in Prague under grant SGS16/160/OHK3/2T/13, by the Technology Agency of the Czech Republic under the project No. TE01020197 Centre for Applied Cybernetics, and by Horizon 2020 program under the project No. 688117 “Safe human-robot interaction in logistic applications for highly flexible warehouses”.
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Dörfler, M., Přeučil, L. (2016). Robust Place Recognition with Combined Image Descriptors. In: Hodicky, J. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2016. Lecture Notes in Computer Science(), vol 9991. Springer, Cham. https://doi.org/10.1007/978-3-319-47605-6_16
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DOI: https://doi.org/10.1007/978-3-319-47605-6_16
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