%0 Journal Article
%T Large Scale Environment Partitioning in Mobile Robotics Recognition Tasks
%A Miguel Cazorla
%A Boyan Bonev
%J Journal of Physical Agents
%D 2010
%I 
%X In this paper we present a scalable machine learning approach to mobile robots visual localization. The applicability of machine learning approaches is constrained by the complexity and size of the problem¡¯s domain. Thus, dividing the problem becomes necessary and two essential questions arise: which partition set is optimal for the problem and how to integrate the separate results into a single solution. The novelty of this work is the use of Information Theory for partitioning high-dimensional data. In the presented experiments the domain of the problem is a large sequence of omnidirectional images, each one of them providing a high number of features. A robot which follows the same trajectory has to answer which is the most similar image from the sequence. The sequence is divided so that each partition is suitable for building a simple classifier. The partitions are established on the basis of the information divergence peaks among the images. Measuring the divergence has usually been considered unfeasible in high-dimensional data spaces. We overcome this problem by estimating the Jensen-Renyi divergence with an entropy approximation based on entropic spanning graphs. Finally, the responses of the different classifiers provide a multimodal hypothesis for each incoming image. As the robot is moving, a particle filter is used for attaining the convergence to a unimodal hypothesis.
%K Visual localization
%K entropy
%K Jensen-Renyi diver- gence
%K classifier
%K particle filter
%U http://www.jopha.net/index.php/jopha/article/view/71