%0 Journal Article
%T 3D Scene and Object Classification Based on Information Complexity of Depth Data
%J International Journal of Robotics, Theory and Applications
%I K.N. Toosi University of Technology
%Z 2008-7144
%A D. Taghirad, Hamid
%A Norouzzadeh, Alireza
%D 2015
%\ 09/01/2015
%V 4
%N 2
%P 28-35
%! 3D Scene and Object Classification Based on Information Complexity of Depth Data
%K SLAM
%K Loop Closure Detection
%K Information Theory
%K Kolmogorov Complexity
%R
%X In this paper the problem of 3D scene and object classification from depth data is addressed. In contrast to high-dimensional feature-based representation, the depth data is described in a low dimensional space. In order to remedy the curse of dimensionality problem, the depth data is described by a sparse model over a learned dictionary. Exploiting the algorithmic information theory, a new definition for the Kolmogorov complexity is presented based on the Earth Moverâs Distance (EMD). Finally the classification of 3D scenes and objects is accomplished by means of a normalized complexity distance, where its applicability in practice is proved by some experiments on publicly available datasets. Also, the experimental results are compared to some state-of-the-art 3D object classification methods. Furthermore, it has been shown that the proposed method outperforms FAB-Map 2.0 in detecting loop closures, in the sense of the precision and recall.
%U http://ijr.kntu.ac.ir/article_12523_1dce7c5111eae75bd1ffdbc28b16da73.pdf