Image-based navigation paradigms have recently emerged as an interesting alternative to conventional model- based methods in mobile robotics. In this paper, we augment the existing image-based navigation approaches by presenting a novel image-based exploration algorithm. The algorithm facilitates a mobile robot equipped only with a monocular pan-tilt camera to autonomously explore a typical indoor environment. The algorithm infers frontier information directly from the images and displaces the robot towards regions that are informative for navigation. The frontiers are detected using a geometric context-based segmentation scheme that exploits the natural scene structure in indoor environments. In the due process, a topological graph of the workspace is built in terms of images which can be subsequently utilised for the tasks of localisation, path planning and navigation. Experimental results on a mobile robot in an unmodified laboratory and corridor environments demonstrate the validity of the approach.

Most robotic vision algorithms are proposed by envisaging robots operating in structured environments where the world is assumed to be rigid. These algorithms fail to provide optimum behavior when the robot has to be controlled with respect to active non-rigid targets. This paper presents a new framework for visual servoing that accomplishes the robot positioning task even in non-rigid environments. We introduce a space-time representation scheme for modeling the deformations of a non-rigid object and propose a model-free hybrid approach that exploits the two-view geometry induced by the space-time features to perform the servoing task. Our formulation can address a variety of non-rigid motions and can tackle large camera displacements without being affected by the degeneracies in the task space. Experimental results validate our approach and demonstrate the robust and stable behavior