A theoretical comparison of probabilistic and biomimetic models of mobile robot navigation

This work deals with the domain of space modeling for mobile robotics. It offers a comparison of probabilistic and biomimetic models of navigation. Both approaches are shown to be quite complementary: while the probabilistic methods exploit sound theoretical grounds, they lack the modularity and, as a consequence, flexibility, of their biomimetic counterparts. We propose a new formalism, called the Bayesian Map formalism, that attempts to bridge the gap between the two domains: it is based on Bayesian modeling and inference for defining the building blocks, and uses operators for building hierarchies of models.