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• W2130488527 endingPage "1949" @default.
• W2130488527 startingPage "1938" @default.
• W2130488527 abstract "In this paper the problem of loop closing from depth or camera image information in an unknown environment is investigated. A sparse model is constructed from a parametric dictionary for every range or camera image as mobile robot observations. In contrast to high-dimensional feature-based representations, in this model, the dimension of the sensor measurements' representations is reduced. Considering the loop closure detection as a clustering problem in high-dimensional space, little attention has been paid to the curse of dimensionality in the existing state-of-the-art algorithms. In this paper, a representation is developed from a sparse model of images, with a lower dimension than original sensor observations. Exploiting the algorithmic information theory, the representation is developed such that it has the geometrically transformation invariant property in the sense of Kolmogorov complexity. A universal normalized metric is used for comparison of complexity based representations of image models. Finally, a distinctive property of normalized compression distance is exploited for detecting similar places and rejecting incorrect loop closure candidates. Experimental results show efficiency and accuracy of the proposed method in comparison to the state-of-the-art algorithms and some recently proposed methods." @default.
• W2130488527 created "2016-06-24" @default.
• W2130488527 creator A5066024223 @default.
• W2130488527 creator A5070019550 @default.
• W2130488527 date "2014-10-01" @default.
• W2130488527 modified "2023-09-27" @default.
• W2130488527 title "Loop Closure Detection by Algorithmic Information Theory: Implemented on Range and Camera Image Data" @default.
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• W2130488527 doi "https://doi.org/10.1109/tcyb.2014.2300180" @default.
• W2130488527 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24968363" @default.
• W2130488527 hasPublicationYear "2014" @default.
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