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Loop closure detection by algorithmic information theory: implemented on range and camera image data.

Authors
Type
Published Article
Journal
IEEE Transactions on Cybernetics
2168-2275
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Volume
44
Issue
10
Pages
1938–1949
Identifiers
DOI: 10.1109/TCYB.2014.2300180
PMID: 24968363
Source
Medline

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.

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