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-Rényi 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.
Visual localization; Entropy; Jensen-Rényi divergence; Classifier; Particle filter