Selection of image features for robot positioning using mutual information

Abstract

The authors and Venaille (1996) developed a prototype for visual robot positioning, based on global image descriptors and neural networks. Now, a procedure to automatically select subsets of image features most relevant to determine pose variations along each of the six degrees of freedom (DOFs) has been incorporated into the prototype. This procedure is based on a statistical measure of variable interdependence, called mutual information. Three families of features are considered in this paper: geometric moments, eigenfeatures and pose-image covariance vectors. The experimental results described show the quantitative and qualitative benefits of carrying out this feature selection prior to training the neural network: fewer network inputs need to be considered, thus considerably shortening training times; the DOFs that would yield larger errors can be determined beforehand, so that more informative features can be looked for; the ordering of the features selected for each DOF often admits a very natural interpretation, which in turn helps to provide insights for devising features tailored to each DOF.