Image-based Monte-Carlo Localisation without a Map

In this paper, we propose a way to fuse the image-based localisation approach with the Monte-Carlo localisation approach. The method we propose does not suffer of the major limitation of the two separated methods: the need of a metric map of the environment for the Monte-Carlo localisation and the failure of the image-based approach in environments with spatial periodicity (perceptual aliasing). The approach we developed exploits the properties of the Fourier Transform of the omnidirectional images and uses the similarity between the images to weights the beliefs about the robot position. Successful experiments in large indoor environment are presented in which we do not used a priory information on the metrical map of the environment.