In this article, a dynamic localization method based on multi-target tracking is presented. The originality of this method is its capability to manage and propagate uncertainties during the localization process. This multi-level uncertainty propagation stage is based on the use of the Dempster–Shafer theory. The perception system we use is composed of an omnidirectional vision system and a panoramic range finder. It enables us to treat complementary and redundant data and thus to construct a robust sensorial model which integrates an important number of significant primitives. Based on this model, we treat the problem of maintaining a matching and propagating uncertainties on each matched primitive in order to obtain a global uncertainty about the robot configuration.
['Arnaud Clerentin', 'Laurent Delahoche', 'Eric Brassart', 'Cyril Drocourt']
Self localization: a new uncertainty propagation architecture