SLAM Loop Closure Detection Information Theory Kolmogorov Complexity 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.