Stereo Vision Local Map Alignment for Robot Environment Mapping

Finding correspondences between sensor measurements obtained at different places is a fundamental task for an autonomous mobile robot. Most matching methods search correspondences between salient features extracted from such measurements. However, finding explicit matches between features is a challenging and expensive task. In this paper we build a local map using a stereo head aided by sonars and propose a method for aligning local maps without searching explicit correspondences between primitives. From objects found by the stereo head, an object probability density distribution is built. Then, the Gauss-Newton algorithm is used to match correspondences, so that, no explicit correspondences are needed.