Matching of affinely invariant regions for visual servoing

This paper develops new image matching techniques for visual servoing based on affine invariants which allow one to deal with large viewpoint changes and that do not rely on specific markers. The only assumption is that there are some locally planar and unoccluded scene regions that have enough structure to be detected in the image. Those regions are classified by a set of illumination and viewpoint invariant features. The features represent the image in a very compact way and allow fast comparison and feature matching between quite different viewpoints. The matching procedure is embedded in a visual servoing system for a mobile robot. Experiments show its potential for navigation with large camera rotations and view point changes in a cluttered environment without the need for artificial landmarks.