%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 Mover’s 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 https://ijr.kntu.ac.ir/article_12523_1dce7c5111eae75bd1ffdbc28b16da73.pdf