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   3D Scene and Object Classification Based on Information Complexity of Depth Data  
نویسنده Norouzzadeh Alireza ,Taghirad H. D.
منبع international journal of robotics - 2015 - دوره : 4 - شماره : 2 - صفحه:28 -35
فایل تمام متن
چکیده    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.
کلیدواژه SLAM ,Loop Closure Detection ,Information Theory ,Kolmogorov Complexity
آدرس k.n.toosi university of technology, k.n.toosi university of technology
پست الکترونیکی taghirad@kntu.ac.ir

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