Clustering in image space for place recognition and visual annotations for human-robot interaction

The most classical way of attempting to solve the vision-guided navigation problem for autonomous robots corresponds to the use of three-dimensional (3-D) geometrical descriptions of the scene; what is known as model-based approaches. However, these approaches do not facilitate the user's task because they require that geometrically precise models of the 3-D environment be given by the user. In this paper, we propose the use of "annotations" posted on some type of blackboard or "descriptive" map to facilitate this user-robot interaction. We show that, by using this technique, user commands can be as simple as "go to label 5." To build such a mechanism, new approaches for vision-guided mobile robot navigation have to be found. We show that this can be achieved by using mixture models within an appearance-based paradigm. Mixture models are more useful in practice than other pattern recognition methods such as principal component analysis (PCA) or Fisher discriminant analysis (FDA)-also known as linear discriminant analysis (LDA), because they can represent nonlinear subspaces. However, given the fact that mixture models are usually learned using the expectation-maximization (EM) algorithm which is a gradient ascent technique, the system cannot always converge to a desired final solution, due to the local maxima problem. To resolve this, a genetic version of the EM algorithm is used. We then show the capabilities of this latest approach on a navigation task that uses the above described "annotations."